paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
020f9d3c-907f-4f99-9102-e348f9e2360d | divide-and-contrast-source-free-domain | 2211.06612 | null | https://arxiv.org/abs/2211.06612v1 | https://arxiv.org/pdf/2211.06612v1.pdf | Divide and Contrast: Source-free Domain Adaptation via Adaptive Contrastive Learning | We investigate a practical domain adaptation task, called source-free domain adaptation (SFUDA), where the source-pretrained model is adapted to the target domain without access to the source data. Existing techniques mainly leverage self-supervised pseudo labeling to achieve class-wise global alignment [1] or rely on ... | ['Guanbin Li', 'Liang Lin', 'Siyuan Li', 'Zhen Li', 'Hui Cheng', 'Weikai Chen', 'Ziyi Zhang'] | 2022-11-12 | null | null | null | null | ['source-free-domain-adaptation'] | ['computer-vision'] | [ 8.77503157e-02 -1.59256980e-02 -4.68394697e-01 -6.41183317e-01
-1.01800001e+00 -4.65415359e-01 5.76659679e-01 1.16548622e-02
-2.20987394e-01 7.58914411e-01 5.96695952e-02 8.65425244e-02
-9.28851366e-02 -6.35865867e-01 -5.60282946e-01 -9.09120798e-01
2.88474977e-01 6.90953255e-01 3.05911571e-01 -2.72805482... | [10.276878356933594, 2.999518871307373] |
c8dfed0a-7832-4b22-baf2-101c24b699cf | impactcite-an-xlnet-based-method-for-citation | 2005.06611 | null | https://arxiv.org/abs/2005.06611v1 | https://arxiv.org/pdf/2005.06611v1.pdf | ImpactCite: An XLNet-based method for Citation Impact Analysis | Citations play a vital role in understanding the impact of scientific literature. Generally, citations are analyzed quantitatively whereas qualitative analysis of citations can reveal deeper insights into the impact of a scientific artifact in the community. Therefore, citation impact analysis (which includes sentiment... | ['Sheraz Ahmed', 'Dominique Mercier', 'Vikas Rajashekar', 'Syed Tahseen Raza Rizvi', 'Andreas Dengel'] | 2020-05-05 | null | null | null | null | ['citation-intent-classification'] | ['natural-language-processing'] | [-3.68255675e-01 -4.08196121e-01 -6.38589203e-01 6.41788691e-02
-7.27101505e-01 -7.38649368e-01 1.07013750e+00 5.42075455e-01
-4.31499571e-01 7.93447793e-01 4.63410795e-01 -6.15171254e-01
-2.05991626e-01 -7.62273967e-01 -4.72172320e-01 -2.66710252e-01
6.85593486e-02 1.95749864e-01 3.84001881e-02 -8.69330242... | [9.63268756866455, 8.259973526000977] |
a9324587-c59d-4afa-b4a4-348fb1cfe22d | positional-contrastive-learning-for | 2106.09157 | null | https://arxiv.org/abs/2106.09157v3 | https://arxiv.org/pdf/2106.09157v3.pdf | Positional Contrastive Learning for Volumetric Medical Image Segmentation | The success of deep learning heavily depends on the availability of large labeled training sets. However, it is hard to get large labeled datasets in medical image domain because of the strict privacy concern and costly labeling efforts. Contrastive learning, an unsupervised learning technique, has been proved powerful... | ['Yiyu Shi', 'Jingtong Hu', 'Jian Zhuang', 'Meiping Huang', 'Haiyun Yuan', 'Xiaowei Xu', 'Xinrong Hu', 'Yawen Wu', 'Dewen Zeng'] | 2021-06-16 | null | null | null | null | ['volumetric-medical-image-segmentation'] | ['medical'] | [ 6.37480319e-01 2.04553887e-01 -2.73510247e-01 -6.39777243e-01
-1.03673565e+00 -5.12925327e-01 2.89366782e-01 2.01430738e-01
-5.31278014e-01 9.54356194e-01 -1.30955741e-01 -2.19569281e-01
3.76294777e-02 -5.92724621e-01 -7.00859010e-01 -9.72976804e-01
-1.74719449e-02 4.79332089e-01 2.75273234e-01 1.94782764... | [14.738402366638184, -2.1292803287506104] |
96f5c54d-0674-419f-9e9e-44049d3c8f1f | effects-of-word-embeddings-on-neural-network | 1805.05237 | null | http://arxiv.org/abs/1805.05237v2 | http://arxiv.org/pdf/1805.05237v2.pdf | Effects of Word Embeddings on Neural Network-based Pitch Accent Detection | Pitch accent detection often makes use of both acoustic and lexical features
based on the fact that pitch accents tend to correlate with certain words. In
this paper, we extend a pitch accent detector that involves a convolutional
neural network to include word embeddings, which are state-of-the-art vector
representati... | ['Sabrina Stehwien', 'Antje Schweitzer', 'Ngoc Thang Vu'] | 2018-05-14 | null | null | null | null | ['cross-corpus'] | ['computer-vision'] | [-4.45950657e-01 -2.89807111e-01 -2.19137266e-01 -5.01548111e-01
-5.57054102e-01 -7.71926105e-01 4.25695509e-01 4.16710436e-01
-1.02571917e+00 4.40672636e-01 5.10787904e-01 -2.83040494e-01
2.59113610e-01 -8.07589948e-01 -3.84928375e-01 -2.45263904e-01
-2.58701712e-01 1.65892392e-01 1.72781751e-01 -5.41949928... | [10.738890647888184, 9.500494956970215] |
d1cdccad-5662-42da-aab2-b52643814e49 | limits-of-an-ai-program-for-solving-college | 2208.06906 | null | https://arxiv.org/abs/2208.06906v1 | https://arxiv.org/pdf/2208.06906v1.pdf | Limits of an AI program for solving college math problems | Drori et al. (2022) report that "A neural network solves, explains, and generates university math problems by program synthesis and few-shot learning at human level ... [It] automatically answers 81\% of university-level mathematics problems." The system they describe is indeed impressive; however, the above descriptio... | ['Ernest Davis'] | 2022-08-14 | null | null | null | null | ['program-synthesis'] | ['computer-code'] | [ 1.39116682e-02 4.94282156e-01 -4.85010408e-02 -2.71112144e-01
-8.11389029e-01 -8.13120186e-01 4.61345077e-01 6.90915063e-02
9.00235102e-02 1.15372968e+00 2.37122804e-01 -9.61240172e-01
-3.33178878e-01 -1.05414116e+00 -6.49028122e-01 -2.45758951e-01
5.00434041e-01 5.23061335e-01 9.98492315e-02 -6.15076721... | [9.245269775390625, 7.18405294418335] |
9741e9c3-3dc1-4fa9-a252-78868c4ebdd1 | double-check-your-state-before-trusting-it | 2206.07989 | null | https://arxiv.org/abs/2206.07989v2 | https://arxiv.org/pdf/2206.07989v2.pdf | Double Check Your State Before Trusting It: Confidence-Aware Bidirectional Offline Model-Based Imagination | The learned policy of model-free offline reinforcement learning (RL) methods is often constrained to stay within the support of datasets to avoid possible dangerous out-of-distribution actions or states, making it challenging to handle out-of-support region. Model-based RL methods offer a richer dataset and benefit gen... | ['Zongqing Lu', 'Xiu Li', 'Jiafei Lyu'] | 2022-06-16 | null | null | null | null | ['d4rl'] | ['robots'] | [-3.44521135e-01 4.07978445e-01 -9.39625084e-01 -2.18642280e-01
-8.80532742e-01 -9.18376088e-01 8.06017518e-01 -2.66981542e-01
-4.10347462e-01 1.16497862e+00 2.84249276e-01 -6.92572832e-01
1.21967278e-01 -5.36445558e-01 -1.11506879e+00 -6.83112919e-01
-2.10598156e-01 7.79046834e-01 -1.21280039e-02 -3.14426750... | [4.052041053771973, 2.055196523666382] |
d69f6f00-2c12-443a-b215-5635f290e26e | recognizing-arguing-subjectivity-and-argument | null | null | https://aclanthology.org/W12-3810 | https://aclanthology.org/W12-3810.pdf | Recognizing Arguing Subjectivity and Argument Tags | null | ['Rebecca Hwa', 'Janyce Wiebe', 'er', 'Alex Conrad'] | 2012-07-01 | recognizing-arguing-subjectivity-and-argument-1 | https://aclanthology.org/W12-3810 | https://aclanthology.org/W12-3810.pdf | ws-2012-7 | ['subjectivity-analysis'] | ['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... | [-7.384544372558594, 3.661895275115967] |
26026692-b2ee-48b9-a25e-a892c1d9a2f7 | few-shot-relational-reasoning-via-connection | 2210.06722 | null | https://arxiv.org/abs/2210.06722v1 | https://arxiv.org/pdf/2210.06722v1.pdf | Few-shot Relational Reasoning via Connection Subgraph Pretraining | Few-shot knowledge graph (KG) completion task aims to perform inductive reasoning over the KG: given only a few support triplets of a new relation $\bowtie$ (e.g., (chop,$\bowtie$,kitchen), (read,$\bowtie$,library), the goal is to predict the query triplets of the same unseen relation $\bowtie$, e.g., (sleep,$\bowtie$,... | ['Jure Leskovec', 'Hongyu Ren', 'Qian Huang'] | 2022-10-13 | null | null | null | null | ['relational-reasoning'] | ['natural-language-processing'] | [ 9.28663462e-02 8.08894336e-01 -3.88240010e-01 -3.89347345e-01
-7.51251936e-01 -1.18839391e-01 3.23897183e-01 3.34246188e-01
-1.15056396e-01 7.04266369e-01 -7.29864389e-02 -3.58192593e-01
-2.74254024e-01 -1.23288763e+00 -1.24637926e+00 -3.51986885e-01
-2.07454383e-01 8.78353059e-01 3.88978302e-01 -4.64603096... | [8.95909595489502, 7.953689098358154] |
e8ba4492-8667-4053-a023-4046397371f5 | enhanced-single-shot-detector-for-small | 2205.05927 | null | https://arxiv.org/abs/2205.05927v1 | https://arxiv.org/pdf/2205.05927v1.pdf | Enhanced Single-shot Detector for Small Object Detection in Remote Sensing Images | Small-object detection is a challenging problem. In the last few years, the convolution neural networks methods have been achieved considerable progress. However, the current detectors struggle with effective features extraction for small-scale objects. To address this challenge, we propose image pyramid single-shot de... | ['Jie Yang', 'Jocelyn Chanussot', 'Eric Granger', 'Masoumeh Zareapoor', 'Pourya Shamsolmoali'] | 2022-05-12 | null | null | null | null | ['small-object-detection'] | ['computer-vision'] | [ 9.76434574e-02 -3.18870485e-01 7.83946291e-02 -2.46909931e-01
-6.37653589e-01 8.06065872e-02 6.01615489e-01 -1.49064034e-01
-5.87289155e-01 4.63042438e-01 1.74970210e-01 3.59737128e-01
8.95587653e-02 -1.01070344e+00 -5.87018073e-01 -5.81553459e-01
7.30177313e-02 -1.57490164e-01 1.26624084e+00 -1.35195494... | [8.820202827453613, -0.4182262718677521] |
c3fb8049-9cac-4e44-8d65-794fce6f5540 | adaptive-warm-start-mcts-in-alphazero-like | 2105.06136 | null | https://arxiv.org/abs/2105.06136v1 | https://arxiv.org/pdf/2105.06136v1.pdf | Adaptive Warm-Start MCTS in AlphaZero-like Deep Reinforcement Learning | AlphaZero has achieved impressive performance in deep reinforcement learning by utilizing an architecture that combines search and training of a neural network in self-play. Many researchers are looking for ways to reproduce and improve results for other games/tasks. However, the architecture is designed to learn from ... | ['Aske Plaat', 'Mike Preuss', 'Hui Wang'] | 2021-05-13 | null | null | null | null | ['board-games'] | ['playing-games'] | [-4.45527643e-01 -4.35173884e-02 -8.92599300e-02 -1.20346770e-01
-5.02140880e-01 -4.35909539e-01 3.52898657e-01 -1.59926578e-01
-9.94772315e-01 1.07508957e+00 -1.57787830e-01 -4.96350467e-01
-3.01672727e-01 -1.13692689e+00 -7.26896584e-01 -7.46632576e-01
-4.28404957e-01 5.86700737e-01 4.78076726e-01 -9.31117177... | [3.5645768642425537, 1.4894155263900757] |
533200ac-70c5-43df-8d15-2da2c6397100 | e-2-go-motion-motion-augmented-event-stream | 2112.03596 | null | https://arxiv.org/abs/2112.03596v3 | https://arxiv.org/pdf/2112.03596v3.pdf | E$^2$(GO)MOTION: Motion Augmented Event Stream for Egocentric Action Recognition | Event cameras are novel bio-inspired sensors, which asynchronously capture pixel-level intensity changes in the form of "events". Due to their sensing mechanism, event cameras have little to no motion blur, a very high temporal resolution and require significantly less power and memory than traditional frame-based came... | ['Barbara Caputo', 'Matteo Matteucci', 'Emanuele Gusso', 'Marco Cannici', 'Gabriele Goletto', 'Mirco Planamente', 'Chiara Plizzari'] | 2021-12-07 | null | null | null | null | ['event-based-vision'] | ['computer-vision'] | [ 3.99435103e-01 -5.17009497e-01 -5.94637841e-02 -9.07009467e-02
-1.14200026e-01 -4.20750231e-01 4.21730757e-01 -8.63562375e-02
-7.62172818e-01 6.27728462e-01 2.41496593e-01 3.76594253e-02
6.71852157e-02 -7.22880363e-01 -5.23906291e-01 -7.36607075e-01
-8.33661482e-03 -3.10976446e-01 5.67019939e-01 1.15512282... | [8.631546974182129, -1.2885894775390625] |
8a12e111-9ba2-427a-bd3a-b0f83ed399be | cross-modal-learning-by-hallucinating-missing | null | null | https://doi.org/10.1016/B978-0-12-817358-9.00018-4 | https://www.researchgate.net/publication/334901581_Cross-modal_Learning_by_Hallucinating_Missing_Modalities_in_RGB-D_Vision | Cross-modal Learning by Hallucinating Missing Modalities in RGB-D Vision | Diverse input data modalities can provide complementary cues for several tasks, usually leading to more robust algorithms and better performance. However, while a (training) dataset could be accurately designed to include a variety of sensory inputs, it is often the case that not all modalities are available in real li... | ['Nuno C. Garcia', 'Vittorio Murino', 'Pietro Morerio'] | 2019-01-01 | null | null | null | multimodal-scene-understanding-algorithms | ['multimodal-activity-recognition'] | ['computer-vision'] | [ 6.34682536e-01 -1.39151454e-01 -1.81760728e-01 -4.03695434e-01
-9.74193335e-01 -5.00900686e-01 8.49894166e-01 -1.98540539e-01
-5.81688106e-01 8.20735633e-01 3.85957360e-01 1.10945717e-01
-1.53628170e-01 -4.08589691e-01 -6.91831410e-01 -1.00389755e+00
-1.49524391e-01 2.73238093e-01 4.33064270e-04 -3.36102657... | [8.149580955505371, 0.6654144525527954] |
aec5de6d-06ad-4bc3-9770-f1a9fbe3e2ea | semi-supervised-deep-large-baseline | 2212.02763 | null | https://arxiv.org/abs/2212.02763v1 | https://arxiv.org/pdf/2212.02763v1.pdf | Semi-supervised Deep Large-baseline Homography Estimation with Progressive Equivalence Constraint | Homography estimation is erroneous in the case of large-baseline due to the low image overlay and limited receptive field. To address it, we propose a progressive estimation strategy by converting large-baseline homography into multiple intermediate ones, cumulatively multiplying these intermediate items can reconstruc... | ['Shuaicheng Liu', 'Songchen Han', 'Yuhang Lu', 'Haipeng Li', 'Hai Jiang'] | 2022-12-06 | null | null | null | null | ['homography-estimation'] | ['computer-vision'] | [ 2.03899905e-01 2.84316782e-02 4.12045792e-02 -3.35642457e-01
-9.24173057e-01 -2.68307686e-01 4.71831352e-01 -3.67149204e-01
-3.15267473e-01 5.36749482e-01 2.80020922e-01 3.14953566e-01
1.75930977e-01 -6.53412640e-01 -9.95778739e-01 -7.43668735e-01
2.68579990e-01 2.44387999e-01 4.11467761e-01 -4.20830362... | [8.742971420288086, -2.295504331588745] |
31070792-ba56-4a62-98bc-b289b06c395a | electrophysiological-indicators-of-gesture | 1811.05058 | null | http://arxiv.org/abs/1811.05058v1 | http://arxiv.org/pdf/1811.05058v1.pdf | Electrophysiological indicators of gesture perception | Background: While there has been abundant research concerning neurological
responses to gesture generation, the time course of gesture processing is not
well understood. Specifically, it is not clear if or how particular
characteristics within the kinematic execution of gestures capture attention
and aid in the classif... | [] | 2018-11-13 | null | null | null | null | ['gesture-generation'] | ['robots'] | [ 1.74011871e-01 -5.52710295e-01 -3.54604930e-01 -6.93397671e-02
-4.85687882e-01 -5.94624341e-01 6.60441458e-01 1.38570622e-01
-5.58274269e-01 3.39233965e-01 7.82457471e-01 1.95816889e-01
-4.40883994e-01 4.17942501e-04 -4.53480870e-01 -7.50017881e-01
-8.03515315e-01 -2.47798905e-01 6.60064146e-02 1.06483154... | [13.016499519348145, 3.429910659790039] |
be2d553d-434a-4fc9-b2d6-2043bb04903e | actgan-flexible-and-efficient-one-shot-face | 2003.13840 | null | https://arxiv.org/abs/2003.13840v1 | https://arxiv.org/pdf/2003.13840v1.pdf | ActGAN: Flexible and Efficient One-shot Face Reenactment | This paper introduces ActGAN - a novel end-to-end generative adversarial network (GAN) for one-shot face reenactment. Given two images, the goal is to transfer the facial expression of the source actor onto a target person in a photo-realistic fashion. While existing methods require target identity to be predefined, we... | ['Volodymyr Budzan', 'Marian Petruk', 'Ivan Kosarevych', 'Orest Kupyn', 'Mykola Maksymenko', 'Markian Kostiv'] | 2020-03-30 | null | null | null | null | ['face-reenactment'] | ['computer-vision'] | [ 4.04542834e-01 3.70422959e-01 3.47205251e-01 -5.87671280e-01
-6.04293644e-01 -5.39319158e-01 6.48486853e-01 -8.36204886e-01
-7.24471780e-03 7.69410193e-01 1.01282157e-01 2.96763718e-01
2.92250097e-01 -9.39604163e-01 -8.88616800e-01 -8.29263628e-01
2.69742697e-01 1.68829337e-01 -4.32572901e-01 -5.19764423... | [12.705150604248047, -0.08667301386594772] |
090d43cd-e346-4a22-afa5-7bc4b095f1c1 | benchmark-data-to-study-the-influence-of-pre | 2306.12150 | null | https://arxiv.org/abs/2306.12150v1 | https://arxiv.org/pdf/2306.12150v1.pdf | Benchmark data to study the influence of pre-training on explanation performance in MR image classification | Convolutional Neural Networks (CNNs) are frequently and successfully used in medical prediction tasks. They are often used in combination with transfer learning, leading to improved performance when training data for the task are scarce. The resulting models are highly complex and typically do not provide any insight i... | ['Stefan Haufe', 'Kerstin Ritter', 'Fabian Eitel', 'Céline Budding', 'Benedict Clark', 'Rick Wilming', 'Marta Oliveira'] | 2023-06-21 | null | null | null | null | ['explainable-artificial-intelligence', 'transfer-learning'] | ['computer-vision', 'miscellaneous'] | [ 5.45302689e-01 5.23820281e-01 -2.91382402e-01 -6.05880678e-01
-4.58571374e-01 -1.00540057e-01 7.92247593e-01 5.10865748e-01
-3.86645496e-01 8.19404244e-01 3.06580186e-01 -3.48255932e-01
-5.32294631e-01 -5.60114324e-01 -8.87402713e-01 -6.86632931e-01
-1.69706643e-01 6.19972050e-01 -3.88339683e-02 -1.71279162... | [8.771265029907227, 5.678853511810303] |
66fd7349-8039-4603-b3dc-b2ef1b32c4db | detail-preserved-point-cloud-completion-via | 2007.02374 | null | https://arxiv.org/abs/2007.02374v1 | https://arxiv.org/pdf/2007.02374v1.pdf | Detail Preserved Point Cloud Completion via Separated Feature Aggregation | Point cloud shape completion is a challenging problem in 3D vision and robotics. Existing learning-based frameworks leverage encoder-decoder architectures to recover the complete shape from a highly encoded global feature vector. Though the global feature can approximately represent the overall shape of 3D objects, it ... | ['Chunxia Xiao', 'Wenxiao Zhang', 'Qingan Yan'] | 2020-07-05 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/5105_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123700511.pdf | eccv-2020-8 | ['point-cloud-completion'] | ['computer-vision'] | [-6.16160855e-02 3.04774530e-02 3.12850177e-01 -4.27971572e-01
-6.87768042e-01 -4.62762356e-01 6.26471400e-01 1.52775943e-01
3.90665755e-02 3.26453239e-01 1.62185043e-01 2.97706634e-01
-4.49893251e-02 -7.36340046e-01 -8.69660795e-01 -5.66949964e-01
8.99252072e-02 5.27561307e-01 2.84030080e-01 8.28663558... | [8.375473022460938, -3.5162010192871094] |
b1246912-f00a-4fc8-b0ea-7381cfafa661 | qvmix-and-qvmix-max-extending-the-deep | 2012.12062 | null | https://arxiv.org/abs/2012.12062v1 | https://arxiv.org/pdf/2012.12062v1.pdf | QVMix and QVMix-Max: Extending the Deep Quality-Value Family of Algorithms to Cooperative Multi-Agent Reinforcement Learning | This paper introduces four new algorithms that can be used for tackling multi-agent reinforcement learning (MARL) problems occurring in cooperative settings. All algorithms are based on the Deep Quality-Value (DQV) family of algorithms, a set of techniques that have proven to be successful when dealing with single-agen... | ['Matthia Sabatelli', 'Jonathan Pisane', 'Gilles Louppe', 'Pierre Geurts', 'Damien Ernst', 'Pascal Leroy'] | 2020-12-22 | null | null | null | null | ['smac-1', 'smac'] | ['playing-games', 'playing-games'] | [-4.89214778e-01 1.91846535e-01 -3.01977426e-01 1.08342633e-01
-7.65151203e-01 -5.03544986e-01 1.00790894e+00 4.35491264e-01
-8.85567546e-01 1.27079129e+00 2.72998493e-02 -3.15295994e-01
-6.83720052e-01 -7.02240765e-01 -7.96778381e-01 -6.95139349e-01
-6.95636749e-01 8.93466890e-01 4.16296005e-01 -9.33938205... | [3.849879264831543, 1.8436626195907593] |
a82e3ba2-721d-42e8-accf-e430b2309837 | cpgnet-cascade-point-grid-fusion-network-for | 2204.09914 | null | https://arxiv.org/abs/2204.09914v3 | https://arxiv.org/pdf/2204.09914v3.pdf | CPGNet: Cascade Point-Grid Fusion Network for Real-Time LiDAR Semantic Segmentation | LiDAR semantic segmentation essential for advanced autonomous driving is required to be accurate, fast, and easy-deployed on mobile platforms. Previous point-based or sparse voxel-based methods are far away from real-time applications since time-consuming neighbor searching or sparse 3D convolution are employed. Recent... | ['Zhenhua Wang', 'Hongyu Pan', 'Gang Zhang', 'Xiaoyan Li'] | 2022-04-21 | null | null | null | null | ['robust-3d-semantic-segmentation', 'lidar-semantic-segmentation'] | ['computer-vision', 'computer-vision'] | [-7.42047355e-02 -2.89809048e-01 -1.98867097e-01 -5.84906816e-01
-6.42068386e-01 -1.20979100e-01 5.25485694e-01 -7.94150010e-02
-5.64231813e-01 4.78753954e-01 -3.52855921e-01 -4.04038876e-01
-2.87267387e-01 -1.26883483e+00 -8.24356019e-01 -6.95681334e-01
4.10508722e-01 8.29527020e-01 8.45144272e-01 -1.54383540... | [8.094676971435547, -2.625796318054199] |
cb4c7602-deb8-4dd9-b9c3-68240b6137e1 | collagan-collaborative-gan-for-missing-image-1 | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Lee_CollaGAN_Collaborative_GAN_for_Missing_Image_Data_Imputation_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Lee_CollaGAN_Collaborative_GAN_for_Missing_Image_Data_Imputation_CVPR_2019_paper.pdf | CollaGAN: Collaborative GAN for Missing Image Data Imputation | In many applications requiring multiple inputs to obtain a desired output, if any of the input data is missing, it often introduces large amounts of bias. Although many techniques have been developed for imputing missing data, the image imputation is still difficult due to complicated nature of natural images. To addre... | [' Jong Chul Ye', ' Won-Jin Moon', ' Junyoung Kim', 'Dongwook Lee'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['image-imputation'] | ['computer-vision'] | [ 7.59297013e-01 1.78758036e-02 7.44424239e-02 -5.01604974e-01
-1.01139915e+00 -5.15194833e-01 3.53202105e-01 -5.89779556e-01
-3.62273008e-02 1.23614955e+00 1.69822648e-01 6.13637315e-03
2.37554461e-01 -7.17076004e-01 -1.17686439e+00 -8.63710225e-01
6.86947465e-01 2.67929763e-01 -5.45399547e-01 1.32573381... | [11.70372200012207, -0.41153597831726074] |
40c46f0f-d979-4142-bb18-580cb438c33f | dalaj-a-dataset-for-linguistic-acceptability-1 | null | null | https://aclanthology.org/2021.nlp4call-1.3 | https://aclanthology.org/2021.nlp4call-1.3.pdf | DaLAJ – a dataset for linguistic acceptability judgments for Swedish | null | ['Julia Klezl', 'Yousuf Ali Mohammed', 'Elena Volodina'] | null | null | null | null | nlp4call-nodalida-2021-5 | ['linguistic-acceptability'] | ['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... | [-7.513768196105957, 3.5689454078674316] |
327cb444-e124-42e2-98b3-ac11394ac59b | dont-miss-the-labels-label-semantic-augmented | null | null | https://aclanthology.org/2021.findings-acl.245 | https://aclanthology.org/2021.findings-acl.245.pdf | Don’t Miss the Labels: Label-semantic Augmented Meta-Learner for Few-Shot Text Classification | null | ['Wei zhang', 'YuHao Lin', 'Lingqiao Liu', 'Qiaoyang Luo'] | null | null | null | null | findings-acl-2021-8 | ['few-shot-text-classification'] | ['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... | [-7.348437786102295, 3.6334152221679688] |
4e80d4a5-31e4-447f-8a33-969dc4706dbb | mobilebrick-building-lego-for-3d | 2303.01932 | null | https://arxiv.org/abs/2303.01932v2 | https://arxiv.org/pdf/2303.01932v2.pdf | MobileBrick: Building LEGO for 3D Reconstruction on Mobile Devices | High-quality 3D ground-truth shapes are critical for 3D object reconstruction evaluation. However, it is difficult to create a replica of an object in reality, and even 3D reconstructions generated by 3D scanners have artefacts that cause biases in evaluation. To address this issue, we introduce a novel multi-view RGBD... | ['Victor Adrian Prisacariu', 'Philip H. S. Torr', 'Robert Castle', 'Jia-Wang Bian', 'Kejie Li'] | 2023-03-03 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Li_MobileBrick_Building_LEGO_for_3D_Reconstruction_on_Mobile_Devices_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Li_MobileBrick_Building_LEGO_for_3D_Reconstruction_on_Mobile_Devices_CVPR_2023_paper.pdf | cvpr-2023-1 | ['3d-object-reconstruction', 'object-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 2.05687404e-01 8.42754450e-03 2.32026309e-01 -4.28165436e-01
-1.00449133e+00 -7.69508362e-01 4.31961447e-01 -2.54385859e-01
-4.07750867e-02 2.73822337e-01 5.80412485e-02 -3.11827697e-02
-1.32677341e-02 -8.61283958e-01 -9.03044641e-01 -2.89009273e-01
1.67427897e-01 9.57366407e-01 3.88653070e-01 -1.46967769... | [8.494458198547363, -2.8441519737243652] |
fb036535-c278-469e-8703-5b527e2f867a | a-neural-based-program-decompiler | 1906.12029 | null | https://arxiv.org/abs/1906.12029v1 | https://arxiv.org/pdf/1906.12029v1.pdf | A Neural-based Program Decompiler | Reverse engineering of binary executables is a critical problem in the computer security domain. On the one hand, malicious parties may recover interpretable source codes from the software products to gain commercial advantages. On the other hand, binary decompilation can be leveraged for code vulnerability analysis an... | ['Yuandong Tian', 'Haolan Liu', 'Huili Chen', 'Farinaz Koushanfar', 'Xinyun Chen', 'Jishen Zhao', 'Cheng Fu'] | 2019-06-28 | null | null | null | null | ['computer-security'] | ['miscellaneous'] | [ 4.64709640e-01 -1.40018553e-01 -7.67703950e-01 1.78823676e-02
-7.25173414e-01 -7.41250813e-01 2.40232214e-01 1.09976642e-01
6.59243986e-02 2.10985988e-01 -1.26451284e-01 -1.22303557e+00
5.12428939e-01 -7.07948327e-01 -9.57225978e-01 -1.56891868e-01
1.87808663e-01 7.52522498e-02 2.94937998e-01 -1.56172901... | [7.110142707824707, 7.816238880157471] |
6da2c0f5-1f61-40c5-b352-dda6462c2480 | joint-dimensionality-reduction-for-separable | 2101.05500 | null | https://arxiv.org/abs/2101.05500v1 | https://arxiv.org/pdf/2101.05500v1.pdf | Joint Dimensionality Reduction for Separable Embedding Estimation | Low-dimensional embeddings for data from disparate sources play critical roles in multi-modal machine learning, multimedia information retrieval, and bioinformatics. In this paper, we propose a supervised dimensionality reduction method that learns linear embeddings jointly for two feature vectors representing data of ... | ['Yoram Bresler', 'Hao Cheng', 'Bihan Wen', 'Yanjun Li'] | 2021-01-14 | null | null | null | null | ['supervised-dimensionality-reduction'] | ['computer-vision'] | [-7.37168267e-02 -1.33778289e-01 -3.70477855e-01 -2.63696790e-01
-9.72836196e-01 -4.90569264e-01 4.71508473e-01 4.63216364e-01
-5.25550961e-01 5.42008460e-01 3.80298048e-01 -1.32951856e-01
-5.97196579e-01 -6.23635054e-01 -3.39807004e-01 -7.90580392e-01
-4.48804647e-01 5.03810346e-01 -6.77705407e-02 1.56504288... | [7.3635149002075195, 4.797533988952637] |
ef8b8879-0f73-442d-bfbf-fd7116b0741b | cater-a-diagnostic-dataset-for-compositional | 1910.04744 | null | https://arxiv.org/abs/1910.04744v2 | https://arxiv.org/pdf/1910.04744v2.pdf | CATER: A diagnostic dataset for Compositional Actions and TEmporal Reasoning | Computer vision has undergone a dramatic revolution in performance, driven in large part through deep features trained on large-scale supervised datasets. However, much of these improvements have focused on static image analysis; video understanding has seen rather modest improvements. Even though new datasets and spat... | ['Deva Ramanan', 'Rohit Girdhar'] | 2019-10-10 | null | null | null | null | ['video-object-tracking'] | ['computer-vision'] | [ 1.50445446e-01 -1.14479497e-01 -3.85916919e-01 -4.27903652e-01
-4.15709764e-01 -8.40205133e-01 1.08444166e+00 -4.17855591e-01
-6.46342412e-02 5.30227244e-01 3.28049630e-01 -4.27067913e-02
7.16320947e-02 -4.16575164e-01 -9.93510604e-01 -7.13923752e-01
-3.70998085e-01 4.91297662e-01 3.98966193e-01 -3.00942779... | [8.680935859680176, 0.47692692279815674] |
97aea8c4-f223-41d1-91eb-0ba057f26dfe | learning-free-form-deformations-for-3d-object | 1803.10932 | null | http://arxiv.org/abs/1803.10932v1 | http://arxiv.org/pdf/1803.10932v1.pdf | Learning Free-Form Deformations for 3D Object Reconstruction | Representing 3D shape in deep learning frameworks in an accurate, efficient
and compact manner still remains an open challenge. Most existing work
addresses this issue by employing voxel-based representations. While these
approaches benefit greatly from advances in computer vision by generalizing 2D
convolutions to the... | ['Anders Eriksson', 'Frederic Maire', 'Dominic Jack', 'Clinton Fookes', 'Sridha Sridharan', 'Sareh Shirazi', 'Jhony K. Pontes'] | 2018-03-29 | null | null | null | null | ['3d-object-reconstruction'] | ['computer-vision'] | [ 3.00025702e-01 1.00759029e-01 8.72522146e-02 -5.01475871e-01
-9.29895103e-01 -4.35336739e-01 6.32351637e-01 3.80467415e-01
-2.02230364e-01 3.88857633e-01 -4.46063936e-01 -1.37568846e-01
1.85110688e-01 -1.32646346e+00 -1.15800083e+00 -3.93495291e-01
-7.39298835e-02 9.47192013e-01 4.07614350e-01 -2.29394864... | [8.524945259094238, -3.6043901443481445] |
46c30d80-dabb-470d-ace7-71d1712a25db | deep-relighting-networks-for-image-light | 2008.08298 | null | https://arxiv.org/abs/2008.08298v2 | https://arxiv.org/pdf/2008.08298v2.pdf | Deep Relighting Networks for Image Light Source Manipulation | Manipulating the light source of given images is an interesting task and useful in various applications, including photography and cinematography. Existing methods usually require additional information like the geometric structure of the scene, which may not be available for most images. In this paper, we formulate th... | ['Daniel P. K. Lun', 'Chu-Tak Li', 'Zhi-Song Liu', 'Wan-Chi Siu', 'Li-Wen Wang'] | 2020-08-19 | null | null | null | null | ['image-relighting'] | ['computer-vision'] | [ 4.90610749e-01 -4.76118401e-02 4.53126222e-01 -1.61943018e-01
-2.21435696e-01 -2.57979453e-01 5.56154847e-01 -5.54111540e-01
-6.01846389e-02 8.51463318e-01 3.18246067e-01 -2.51145393e-01
4.25390214e-01 -8.04970920e-01 -1.00753546e+00 -8.19278657e-01
5.18769741e-01 -1.08389676e-01 1.76383644e-01 -2.89240539... | [10.567606925964355, -2.385394334793091] |
dc88410d-30eb-456c-9faf-f885126481f4 | formality-style-transfer-with-hybrid-textual | 1903.06353 | null | http://arxiv.org/abs/1903.06353v1 | http://arxiv.org/pdf/1903.06353v1.pdf | Formality Style Transfer with Hybrid Textual Annotations | Formality style transformation is the task of modifying the formality of a
given sentence without changing its content. Its challenge is the lack of
large-scale sentence-aligned parallel data. In this paper, we propose an
omnivorous model that takes parallel data and formality-classified data jointly
to alleviate the d... | ['Furu Wei', 'Tao Ge', 'Ruochen Xu'] | 2019-03-15 | null | null | null | null | ['formality-style-transfer'] | ['natural-language-processing'] | [ 6.57257497e-01 2.33276337e-01 -1.87894508e-01 -6.71268344e-01
-1.13351929e+00 -6.89171314e-01 6.34438396e-01 7.15193525e-03
-6.85618937e-01 9.34808314e-01 5.46026170e-01 -2.49994159e-01
3.12990457e-01 -4.01098311e-01 -8.42322171e-01 -3.50418657e-01
4.93691176e-01 5.31265497e-01 1.19992964e-01 -6.54595733... | [11.572406768798828, 9.518424034118652] |
9c8bdb26-592c-4422-a1f4-cb2c6bde707b | paragraph-based-transformer-pre-training-for | 2205.01228 | null | https://arxiv.org/abs/2205.01228v2 | https://arxiv.org/pdf/2205.01228v2.pdf | Paragraph-based Transformer Pre-training for Multi-Sentence Inference | Inference tasks such as answer sentence selection (AS2) or fact verification are typically solved by fine-tuning transformer-based models as individual sentence-pair classifiers. Recent studies show that these tasks benefit from modeling dependencies across multiple candidate sentences jointly. In this paper, we first ... | ['Alessandro Moschitti', 'Luca Soldaini', 'Siddhant Garg', 'Luca Di Liello'] | 2022-05-02 | null | https://aclanthology.org/2022.naacl-main.181 | https://aclanthology.org/2022.naacl-main.181.pdf | naacl-2022-7 | ['answer-selection'] | ['natural-language-processing'] | [ 1.67874470e-01 8.35465044e-02 -2.44430497e-01 -8.10804963e-01
-1.76637220e+00 -6.09193027e-01 6.54703915e-01 3.93244892e-01
-3.08478385e-01 9.43125367e-01 3.17940742e-01 -6.08445704e-01
1.42153148e-02 -7.79888093e-01 -1.00404191e+00 -6.49238378e-02
3.07583272e-01 6.99819386e-01 4.32588696e-01 -3.29989731... | [11.091841697692871, 8.431422233581543] |
c8b05bf1-8ff8-4c2c-abb2-48d1cb00b703 | differentiable-frequency-based | 2209.09194 | null | https://arxiv.org/abs/2209.09194v2 | https://arxiv.org/pdf/2209.09194v2.pdf | Differentiable Frequency-based Disentanglement for Aerial Video Action Recognition | We present a learning algorithm for human activity recognition in videos. Our approach is designed for UAV videos, which are mainly acquired from obliquely placed dynamic cameras that contain a human actor along with background motion. Typically, the human actors occupy less than one-tenth of the spatial resolution. Ou... | ['Dinesh Manocha', 'Ming Lin', 'Divya Kothandaraman'] | 2022-09-15 | null | null | null | null | ['activity-recognition-in-videos'] | ['computer-vision'] | [ 6.40719235e-01 -1.58950850e-01 -1.93725362e-01 -1.62069857e-01
-5.29153764e-01 -4.65099514e-01 5.70214987e-01 -4.77687240e-01
-5.24020433e-01 5.02439678e-01 2.55655617e-01 3.80211949e-01
-1.95723951e-01 -3.34432483e-01 -8.60377491e-01 -9.04078066e-01
-6.05508685e-01 -4.26862895e-01 -1.08093150e-01 1.65699378... | [8.482495307922363, 0.509787917137146] |
e3051d75-491e-4764-88c3-6cbc416b1258 | deep-keyphrase-generation | 1704.06879 | null | https://arxiv.org/abs/1704.06879v3 | https://arxiv.org/pdf/1704.06879v3.pdf | Deep Keyphrase Generation | Keyphrase provides highly-condensed information that can be effectively used for understanding, organizing and retrieving text content. Though previous studies have provided many workable solutions for automated keyphrase extraction, they commonly divided the to-be-summarized content into multiple text chunks, then ran... | ['Peter Brusilovsky', 'Sanqiang Zhao', 'Rui Meng', 'Daqing He', 'Yu Chi', 'Shuguang Han'] | 2017-04-23 | deep-keyphrase-generation-1 | https://aclanthology.org/P17-1054 | https://aclanthology.org/P17-1054.pdf | acl-2017-7 | ['keyphrase-generation'] | ['natural-language-processing'] | [-5.85406609e-02 1.11130498e-01 -4.44203556e-01 2.00808957e-01
-9.51025546e-01 -6.89989090e-01 9.31936741e-01 7.32168019e-01
-2.87479669e-01 6.07242763e-01 1.07070553e+00 -2.50631962e-02
9.76721719e-02 -9.30300236e-01 -6.89494789e-01 -4.29276615e-01
2.76667476e-01 2.52729714e-01 2.10495412e-01 -3.23600799... | [12.29585075378418, 8.9010009765625] |
32106577-59dc-4a23-8639-e863b7f30302 | improving-zero-shot-multilingual-neural-1 | 2305.07310 | null | https://arxiv.org/abs/2305.07310v1 | https://arxiv.org/pdf/2305.07310v1.pdf | Improving Zero-shot Multilingual Neural Machine Translation by Leveraging Cross-lingual Consistency Regularization | The multilingual neural machine translation (NMT) model has a promising capability of zero-shot translation, where it could directly translate between language pairs unseen during training. For good transfer performance from supervised directions to zero-shot directions, the multilingual NMT model is expected to learn ... | ['Haifeng Wang', 'Hua Wu', 'Zhongjun He', 'Liwen Zhang', 'Pengzhi Gao'] | 2023-05-12 | null | null | null | null | ['nmt'] | ['computer-code'] | [-8.86773765e-02 -1.24006368e-01 -7.23243773e-01 -3.78612310e-01
-1.37032926e+00 -5.72444022e-01 8.25063765e-01 -3.15041542e-01
-2.58184463e-01 8.87634993e-01 4.01649326e-01 -7.87720501e-01
4.24733520e-01 -5.78495443e-01 -1.06261075e+00 -4.77316350e-01
3.24795246e-01 7.11804271e-01 -4.36773092e-01 -5.21422267... | [11.548465728759766, 10.190595626831055] |
2a08ef4d-9c53-4e9a-9824-b5f0e5aa79de | understanding-and-stabilizing-gans-training | 1909.13188 | null | https://arxiv.org/abs/1909.13188v4 | https://arxiv.org/pdf/1909.13188v4.pdf | Understanding and Stabilizing GANs' Training Dynamics with Control Theory | Generative adversarial networks (GANs) are effective in generating realistic images but the training is often unstable. There are existing efforts that model the training dynamics of GANs in the parameter space but the analysis cannot directly motivate practically effective stabilizing methods. To this end, we present ... | ['Kun Xu', 'Jun Zhu', 'Chongxuan Li', 'Bo Zhang'] | 2019-09-29 | null | https://openreview.net/forum?id=BJe7h34YDS | https://openreview.net/pdf?id=BJe7h34YDS | null | ['l2-regularization'] | ['methodology'] | [ 1.58186153e-01 3.73228282e-01 1.14091121e-01 7.77070001e-02
-7.27395952e-01 -7.28303850e-01 8.06750834e-01 -7.92284667e-01
8.75981674e-02 9.95336592e-01 -7.33121410e-02 -3.35876703e-01
2.45111600e-01 -7.26402223e-01 -9.78625834e-01 -1.11971676e+00
3.05214852e-01 2.45539367e-01 -2.40630150e-01 -4.79643196... | [11.579325675964355, 0.010694744996726513] |
d8bd095d-8350-4c75-889c-611cde3ad87c | distribution-aware-graph-representation | 2205.06576 | null | https://arxiv.org/abs/2205.06576v1 | https://arxiv.org/pdf/2205.06576v1.pdf | Distribution-Aware Graph Representation Learning for Transient Stability Assessment of Power System | The real-time transient stability assessment (TSA) plays a critical role in the secure operation of the power system. Although the classic numerical integration method, \textit{i.e.} time-domain simulation (TDS), has been widely used in industry practice, it is inevitably trapped in a high computational complexity due ... | ['Mingli Song', 'Zunlei Feng', 'Jie Song', 'Quan Zhang', 'Rong Yan', 'Na Yu', 'Shunyu Liu', 'KaiXuan Chen'] | 2022-05-12 | null | null | null | null | ['numerical-integration'] | ['miscellaneous'] | [-2.22976610e-01 -9.76427495e-02 -1.33895800e-01 6.54002354e-02
-2.34353095e-01 -5.96447170e-01 9.46244895e-02 4.08449113e-01
1.79372326e-01 7.96911180e-01 -5.87795496e-01 -8.00594628e-01
-6.69822097e-01 -8.92156422e-01 -9.33697000e-02 -1.00943112e+00
-5.03510594e-01 3.56098533e-01 -3.63182127e-02 -6.27485037... | [5.9242353439331055, 2.6051368713378906] |
a4472a72-b8d8-4f4c-8184-a922ba0bfb5d | improving-electron-micrograph-signal-to-noise | 1807.11234 | null | http://arxiv.org/abs/1807.11234v2 | http://arxiv.org/pdf/1807.11234v2.pdf | Improving Electron Micrograph Signal-to-Noise with an Atrous Convolutional Encoder-Decoder | We present an atrous convolutional encoder-decoder trained to denoise
512$\times$512 crops from electron micrographs. It consists of a modified
Xception backbone, atrous convoltional spatial pyramid pooling module and a
multi-stage decoder. Our neural network was trained end-to-end to remove
Poisson noise applied to lo... | ['Jeffrey M. Ede'] | 2018-07-30 | null | null | null | null | ['2048'] | ['playing-games'] | [ 3.45953405e-01 -2.59396322e-02 6.05716228e-01 -3.72555912e-01
-1.18926620e+00 -2.00645819e-01 3.81860226e-01 7.42328689e-02
-1.03031671e+00 1.02261603e+00 4.60130572e-02 -1.02326311e-02
-1.33640483e-01 -8.34252000e-01 -8.81256282e-01 -1.02446258e+00
-1.55561287e-02 2.78660685e-01 3.59228313e-01 -3.77745293... | [13.124985694885254, -2.6573493480682373] |
8380a505-a11b-453f-9905-92dc3db19ddd | goal-conditioned-predictive-coding-as-an | 2307.03406 | null | https://arxiv.org/abs/2307.03406v1 | https://arxiv.org/pdf/2307.03406v1.pdf | Goal-Conditioned Predictive Coding as an Implicit Planner for Offline Reinforcement Learning | Recent work has demonstrated the effectiveness of formulating decision making as a supervised learning problem on offline-collected trajectories. However, the benefits of performing sequence modeling on trajectory data is not yet clear. In this work we investigate if sequence modeling has the capability to condense tra... | ['Chen Sun', 'Shijie Wang', 'Ce Zhang', 'Zilai Zeng'] | 2023-07-07 | null | null | null | null | ['offline-rl', 'decision-making'] | ['playing-games', 'reasoning'] | [ 9.94202420e-02 9.35770795e-02 -9.13572848e-01 -2.79034883e-01
-6.13773048e-01 -6.78911030e-01 1.02510190e+00 -1.65774468e-02
-1.73045278e-01 8.47898602e-01 7.28302300e-01 -6.78474665e-01
-4.64731865e-02 -6.87547386e-01 -7.81450927e-01 -7.09181964e-01
-3.64154965e-01 4.62594628e-01 -2.59693041e-02 -2.98894763... | [4.148338317871094, 1.729852557182312] |
1aaaae61-a21a-4c91-bacc-b31470594349 | annotation-cost-efficient-active-learning-for | 2306.11605 | null | https://arxiv.org/abs/2306.11605v2 | https://arxiv.org/pdf/2306.11605v2.pdf | Annotation Cost Efficient Active Learning for Content Based Image Retrieval | Deep metric learning (DML) based methods have been found very effective for content-based image retrieval (CBIR) in remote sensing (RS). For accurately learning the model parameters of deep neural networks, most of the DML methods require a high number of annotated training images, which can be costly to gather. To add... | ['Begüm Demir', 'Lars Möllenbrok', 'Gencer Sumbul', 'Genc Hoxha', 'Julia Henkel'] | 2023-06-20 | null | null | null | null | ['metric-learning', 'content-based-image-retrieval', 'active-learning', 'metric-learning', 'active-learning'] | ['computer-vision', 'computer-vision', 'methodology', 'methodology', 'natural-language-processing'] | [ 2.81444132e-01 -6.18985444e-02 8.01521167e-02 -6.63887620e-01
-1.24073946e+00 -5.99486053e-01 4.47201341e-01 5.08583486e-01
-7.06943393e-01 6.27182186e-01 -2.61942387e-01 -1.30701348e-01
-4.65109348e-01 -9.90555048e-01 -4.21359628e-01 -9.95733798e-01
-5.13198897e-02 7.83964157e-01 -9.35145095e-02 3.35904360... | [9.75486946105957, -1.3047770261764526] |
f63283b4-d504-4a1b-a67d-6add306fee14 | wizmap-scalable-interactive-visualization-for | 2306.09328 | null | https://arxiv.org/abs/2306.09328v1 | https://arxiv.org/pdf/2306.09328v1.pdf | WizMap: Scalable Interactive Visualization for Exploring Large Machine Learning Embeddings | Machine learning models often learn latent embedding representations that capture the domain semantics of their training data. These embedding representations are valuable for interpreting trained models, building new models, and analyzing new datasets. However, interpreting and using embeddings can be challenging due ... | ['Duen Horng Chau', 'Fred Hohman', 'Zijie J. Wang'] | 2023-06-15 | null | null | null | null | ['navigate'] | ['reasoning'] | [-6.33646548e-01 1.18516333e-01 -3.60867232e-01 -1.14489421e-01
-5.09229958e-01 -8.19607258e-01 6.37551963e-01 4.49015111e-01
-1.79113448e-01 1.58518940e-01 7.01506257e-01 -7.36401320e-01
-8.55640415e-03 -9.46808636e-01 -2.21905485e-01 -2.36209571e-01
-1.87014788e-01 2.70749837e-01 6.30877316e-02 4.47478779... | [10.39069652557373, 8.387252807617188] |
8c4f24a4-be15-4fcd-9b51-9778c82a8db2 | managing-large-dataset-gaps-in-urban-air | 2212.10273 | null | https://arxiv.org/abs/2212.10273v1 | https://arxiv.org/pdf/2212.10273v1.pdf | Managing Large Dataset Gaps in Urban Air Quality Prediction: DCU-Insight-AQ at MediaEval 2022 | Calculating an Air Quality Index (AQI) typically uses data streams from air quality sensors deployed at fixed locations and the calculation is a real time process. If one or a number of sensors are broken or offline, then the real time AQI value cannot be computed. Estimating AQI values for some point in the future is ... | ['Alan F. Smeaton', 'Mark Roantree', 'Elke Eichlemann', 'Adam Stapleton', 'Phuc H. Le-Khac', 'Dinh Viet Cuong'] | 2022-12-19 | null | null | null | null | ['air-pollution-prediction'] | ['miscellaneous'] | [ 2.42300227e-01 -4.32720751e-01 1.18828088e-01 -4.39305782e-01
-8.63892972e-01 -6.14449918e-01 3.48459154e-01 6.90787971e-01
-2.07575083e-01 8.76472950e-01 2.47636795e-01 -7.55717099e-01
-6.98034286e-01 -1.43720531e+00 -3.46705109e-01 -4.60393190e-01
2.66612381e-01 8.04628074e-01 9.72510651e-02 1.19456671... | [6.2215962409973145, 2.5078747272491455] |
1621e0ad-27be-47f8-8624-d80997f7866c | can-wifi-estimate-person-pose | 1904.00277 | null | http://arxiv.org/abs/1904.00277v2 | http://arxiv.org/pdf/1904.00277v2.pdf | Can WiFi Estimate Person Pose? | WiFi human sensing has achieved great progress in indoor localization,
activity classification, etc. Retracing the development of these work, we have
a natural question: can WiFi devices work like cameras for vision applications?
In this paper We try to answer this question by exploring the ability of WiFi
on estimatin... | ['Stanislav Panev', 'Fei Wang', 'Ziyi Dai', 'Dong Huang', 'Jinsong Han'] | 2019-03-30 | null | null | null | null | ['rf-based-pose-estimation'] | ['computer-vision'] | [ 8.47598836e-02 -1.52912214e-01 5.18835224e-02 -5.13655484e-01
-7.53588200e-01 -7.45557487e-01 5.52424252e-01 -7.45077729e-01
-3.53983819e-01 9.31003928e-01 6.23895943e-01 2.30185036e-03
-4.81711812e-02 -6.51329160e-01 -9.30283308e-01 -5.31645894e-01
-7.09176343e-03 -3.25749163e-03 -6.73320070e-02 3.47269237... | [6.786060810089111, 0.5594683289527893] |
43d3bfcb-5bfd-4903-ba1d-b3abe0488bfe | fedmt-federated-learning-with-mixed-type | 2210.02042 | null | https://arxiv.org/abs/2210.02042v2 | https://arxiv.org/pdf/2210.02042v2.pdf | FedMT: Federated Learning with Mixed-type Labels | In federated learning (FL), classifiers (e.g., deep networks) are trained on datasets from multiple centers without exchanging data across them, and thus improves sample efficiency. In the classical setting of FL, the same labeling criterion is usually employed across all centers being involved in training. This constr... | ['Xiaoxiao Li', 'Gang Niu', 'Aline Talhouk', 'Qiong Zhang'] | 2022-10-05 | null | null | null | null | ['type'] | ['speech'] | [ 1.92338571e-01 -1.04323486e-02 -5.31595707e-01 -4.87315834e-01
-7.14392602e-01 -6.59038723e-01 1.96141869e-01 1.86113119e-01
-5.00018239e-01 7.94643879e-01 -6.90657422e-02 -3.37235838e-01
-3.77451897e-01 -6.89300299e-01 -6.69700682e-01 -1.03459167e+00
1.47275999e-01 5.22487104e-01 -3.35368603e-01 2.37817034... | [6.039620399475098, 6.435494899749756] |
2eb67920-ac3f-42e7-8893-e677e4499024 | boosting-value-decomposition-via-unit-wise | 2305.07182 | null | https://arxiv.org/abs/2305.07182v1 | https://arxiv.org/pdf/2305.07182v1.pdf | Boosting Value Decomposition via Unit-Wise Attentive State Representation for Cooperative Multi-Agent Reinforcement Learning | In cooperative multi-agent reinforcement learning (MARL), the environmental stochasticity and uncertainties will increase exponentially when the number of agents increases, which puts hard pressure on how to come up with a compact latent representation from partial observation for boosting value decomposition. To tackl... | ['Chunlin Chen', 'Zhi Wang', 'Zichuan Liu', 'Yuanyang Zhu', 'Qingpeng Zhao'] | 2023-05-12 | null | null | null | null | ['multi-agent-reinforcement-learning', 'starcraft-ii', 'starcraft'] | ['methodology', 'playing-games', 'playing-games'] | [-4.04127508e-01 3.71562451e-01 -4.33081955e-01 -6.01541437e-02
-8.02701831e-01 -5.60439289e-01 7.74398804e-01 -1.53500691e-01
-4.88624781e-01 1.15266871e+00 5.71502686e-01 -5.42975925e-02
-4.83722240e-01 -7.45613337e-01 -6.63379729e-01 -1.17990243e+00
-3.08952153e-01 6.80686295e-01 -1.50678873e-01 -4.41905499... | [3.7099640369415283, 2.044111490249634] |
a26136fc-500a-4a64-8872-b7ed87737c0c | predicting-optimal-value-functions-by | 1909.05004 | null | https://arxiv.org/abs/1909.05004v4 | https://arxiv.org/pdf/1909.05004v4.pdf | Predicting optimal value functions by interpolating reward functions in scalarized multi-objective reinforcement learning | A common approach for defining a reward function for Multi-objective Reinforcement Learning (MORL) problems is the weighted sum of the multiple objectives. The weights are then treated as design parameters dependent on the expertise (and preference) of the person performing the learning, with the typical result that a ... | ['Arpan Kusari', 'Jonathan P. How'] | 2019-09-11 | null | null | null | null | ['multi-objective-reinforcement-learning'] | ['methodology'] | [-1.52330905e-01 3.24855931e-02 -3.81561816e-01 -2.81179845e-01
-8.19089234e-01 -5.55835545e-01 4.34316009e-01 1.60578355e-01
-1.04815531e+00 1.33861244e+00 -3.88111353e-01 -2.27272987e-01
-5.48846304e-01 -6.15685701e-01 -7.52805948e-01 -9.27640975e-01
-1.42103553e-01 6.15546644e-01 1.74252972e-01 -5.45535982... | [4.281239032745361, 2.186870574951172] |
c5ebe427-292e-4b84-9d62-463948bebcca | learning-uncertainty-with-artificial-neural-1 | 2206.06317 | null | https://arxiv.org/abs/2206.06317v1 | https://arxiv.org/pdf/2206.06317v1.pdf | Learning Uncertainty with Artificial Neural Networks for Improved Predictive Process Monitoring | The inability of artificial neural networks to assess the uncertainty of their predictions is an impediment to their widespread use. We distinguish two types of learnable uncertainty: model uncertainty due to a lack of training data and noise-induced observational uncertainty. Bayesian neural networks use solid mathema... | ['Jochen De Weerdt', 'Hans Weytjens'] | 2022-06-13 | null | null | null | null | ['predictive-process-monitoring'] | ['time-series'] | [ 2.43568808e-01 4.48084354e-01 8.46141428e-02 -8.31385732e-01
-3.34487647e-01 -4.57331061e-01 7.91396797e-01 4.70311522e-01
-2.73117214e-01 9.25591350e-01 -2.96422895e-02 -5.60212016e-01
-5.98292887e-01 -1.00690103e+00 -6.29928470e-01 -2.44597182e-01
-2.27731764e-01 6.36264741e-01 1.45707756e-01 4.24857795... | [7.367177963256836, 3.8318915367126465] |
4aaeb95e-afe0-4200-a5a1-be4e51c6e056 | evaluating-machine-translation-in-cross | null | null | https://aclanthology.org/2022.amta-research.25 | https://aclanthology.org/2022.amta-research.25.pdf | Evaluating Machine Translation in Cross-lingual E-Commerce Search | Multilingual query localization is integral to modern e-commerce. While machine translation is widely used to translate e-commerce queries, evaluation of query translation in the context of the down-stream search task is overlooked. This study proposes a search ranking-based evaluation framework with an edit-distance b... | ['Amita Misra', 'Liling Tan', 'Hang Zhang'] | null | null | null | null | amta-2022-9 | ['cross-lingual-information-retrieval'] | ['natural-language-processing'] | [-1.23391345e-01 -6.32559180e-01 -7.99946785e-01 -1.54394269e-01
-1.82236767e+00 -1.21229994e+00 7.35781550e-01 3.84158581e-01
-8.80029559e-01 4.03862268e-01 1.78275228e-01 -6.62636399e-01
-3.89278978e-01 -6.89463854e-01 -5.27184784e-01 6.31651357e-02
3.87573749e-01 9.92952943e-01 3.19109946e-01 -7.16959357... | [11.542150497436523, 9.997648239135742] |
b58e62fb-2061-4579-b268-0489d4701001 | metatroll-few-shot-detection-of-state | 2303.07354 | null | https://arxiv.org/abs/2303.07354v1 | https://arxiv.org/pdf/2303.07354v1.pdf | MetaTroll: Few-shot Detection of State-Sponsored Trolls with Transformer Adapters | State-sponsored trolls are the main actors of influence campaigns on social media and automatic troll detection is important to combat misinformation at scale. Existing troll detection models are developed based on training data for known campaigns (e.g.\ the influence campaign by Russia's Internet Research Agency on t... | ['Jey Han Lau', 'Xiuzhen Zhang', 'Lin Tian'] | 2023-03-13 | null | null | null | null | ['misinformation', 'few-shot-text-classification'] | ['miscellaneous', 'natural-language-processing'] | [ 0.31964913 -0.19871451 -0.5911734 -0.09994107 -1.184722 -0.54283154
1.212251 0.3043932 -0.7719142 0.69065994 0.5167142 -0.4719228
0.10542185 -0.79632306 -0.72003406 -0.32002202 0.12656482 0.6725086
0.24392481 -0.86082995 0.2090474 -0.03945249 -1.2206322 0.5367905
0.60013515 0.27150384 -0.060... | [8.468006134033203, 10.665050506591797] |
e830d661-4ad2-474d-b20a-fc5b868c4ec3 | generative-models-for-local-network-community | 1804.04469 | null | http://arxiv.org/abs/1804.04469v1 | http://arxiv.org/pdf/1804.04469v1.pdf | Generative models for local network community detection | Local network community detection aims to find a single community in a large
network, while inspecting only a small part of that network around a given seed
node. This is much cheaper than finding all communities in a network. Most
methods for local community detection are formulated as ad-hoc optimization
problems. In... | ['Twan van Laarhoven'] | 2018-04-12 | null | null | null | null | ['local-community-detection'] | ['graphs'] | [ 1.70096919e-01 3.47292215e-01 -7.04868697e-03 2.23191962e-01
-2.62358308e-01 -8.73626530e-01 5.91154397e-01 8.15925062e-01
-1.62107721e-01 5.32984316e-01 -1.91859394e-01 -1.40279442e-01
-1.70750618e-01 -1.25079834e+00 -6.03563130e-01 -8.22795630e-01
-4.51234102e-01 9.31926429e-01 7.00285077e-01 2.38734446... | [6.921823978424072, 5.208592891693115] |
77966e60-cb5c-49ad-ab62-3299420fd506 | a-generic-approach-for-enhancing-gans-by | 2112.03502 | null | https://arxiv.org/abs/2112.03502v1 | https://arxiv.org/pdf/2112.03502v1.pdf | A Generic Approach for Enhancing GANs by Regularized Latent Optimization | With the rapidly growing model complexity and data volume, training deep generative models (DGMs) for better performance has becoming an increasingly more important challenge. Previous research on this problem has mainly focused on improving DGMs by either introducing new objective functions or designing more expressiv... | ['Jinhui Xu', 'Changyou Chen', 'Chunyuan Li', 'Yufan Zhou'] | 2021-12-07 | null | null | null | null | ['text-guided-image-editing'] | ['computer-vision'] | [ 4.72145200e-01 7.29917735e-02 -4.89020832e-02 -3.91981870e-01
-7.24235237e-01 -2.65320808e-01 8.09492767e-01 -3.17613125e-01
-1.56502366e-01 8.09261143e-01 3.52511592e-02 -2.29409665e-01
1.06250562e-01 -7.67494798e-01 -6.62858844e-01 -7.69943953e-01
4.57236797e-01 5.82711399e-01 -2.71693170e-01 1.09279990... | [11.520604133605957, -0.4754030704498291] |
11a30642-512b-4d63-b5b3-5e008f0661b0 | 3d-semantic-scene-completion-from-a-single | 1905.06231 | null | https://arxiv.org/abs/1905.06231v1 | https://arxiv.org/pdf/1905.06231v1.pdf | 3D Semantic Scene Completion from a Single Depth Image using Adversarial Training | We address the task of 3D semantic scene completion, i.e. , given a single depth image, we predict the semantic labels and occupancy of voxels in a 3D grid representing the scene. In light of the recently introduced generative adversarial networks (GAN), our goal is to explore the potential of this model and the effici... | ['Yueh-Tung Chen', 'Martin Garbade', 'Juergen Gall'] | 2019-05-15 | null | null | null | null | ['3d-semantic-scene-completion'] | ['computer-vision'] | [ 4.79900956e-01 6.56071067e-01 3.48867863e-01 -3.34126920e-01
-7.77157605e-01 -5.38171053e-01 9.76433873e-01 -4.76790577e-01
-3.51884216e-01 7.85554111e-01 4.13003653e-01 -1.31288305e-01
3.68391484e-01 -7.23961115e-01 -8.89896154e-01 -5.81036687e-01
1.31015047e-01 6.90115273e-01 2.33076513e-02 1.80328488... | [8.938161849975586, -3.0648856163024902] |
bf620573-4472-4bab-b554-bbea21396f08 | multi-armed-bandit-learning-for-tdma | 2302.05301 | null | https://arxiv.org/abs/2302.05301v1 | https://arxiv.org/pdf/2302.05301v1.pdf | Multi-armed Bandit Learning for TDMA Transmission Slot Scheduling and Defragmentation for Improved Bandwidth Usage | This paper proposes a Time Division Multiple Access (TDMA) MAC slot allocation protocol with efficient bandwidth usage in wireless sensor networks and Internet of Things (IoTs). The developed protocol has two primary components: a Multi-Armed Bandits (MAB)-based slot allocation mechanism for collision free transmission... | ['Subir Biswas', 'Amit Kumar Bhuyan', 'Hrishikesh Dutta'] | 2023-01-14 | null | null | null | null | ['multi-armed-bandits'] | ['miscellaneous'] | [ 1.50521681e-01 7.14610994e-01 -6.20431781e-01 -3.47017199e-01
-2.01832615e-02 -2.29776919e-01 2.37130120e-01 6.59793839e-02
-1.03637779e+00 1.33649850e+00 -1.03110445e+00 -5.11523664e-01
-7.05322385e-01 -1.21827519e+00 -6.81503862e-02 -1.35143745e+00
-5.91330886e-01 8.91101420e-01 7.28935540e-01 -3.95704322... | [5.958409309387207, 1.58686101436615] |
49f2a6d1-bd55-4a86-a8dd-448e5b46c2a0 | fvqa-2-0-introducing-adversarial-samples-into | 2303.10699 | null | https://arxiv.org/abs/2303.10699v1 | https://arxiv.org/pdf/2303.10699v1.pdf | FVQA 2.0: Introducing Adversarial Samples into Fact-based Visual Question Answering | The widely used Fact-based Visual Question Answering (FVQA) dataset contains visually-grounded questions that require information retrieval using common sense knowledge graphs to answer. It has been observed that the original dataset is highly imbalanced and concentrated on a small portion of its associated knowledge g... | ['Bill Byrne', 'Zhilin Wang', 'Weizhe Lin'] | 2023-03-19 | null | null | null | null | ['common-sense-reasoning'] | ['reasoning'] | [-2.09389940e-01 6.74957335e-01 -9.76772383e-02 -1.58037752e-01
-9.56854522e-01 -1.17529583e+00 4.37405735e-01 2.29010180e-01
1.59358367e-01 6.81611240e-01 2.20520362e-01 -6.48887157e-01
1.86022390e-02 -1.04540741e+00 -1.00919187e+00 1.45799667e-02
8.27760920e-02 5.52917302e-01 6.70506239e-01 -3.99059981... | [10.970978736877441, 1.947223424911499] |
fc08d343-f5fd-40f4-86ce-7034369845da | lifted-inference-with-linear-order-axiom | 2211.01164 | null | https://arxiv.org/abs/2211.01164v1 | https://arxiv.org/pdf/2211.01164v1.pdf | Lifted Inference with Linear Order Axiom | We consider the task of weighted first-order model counting (WFOMC) used for probabilistic inference in the area of statistical relational learning. Given a formula $\phi$, domain size $n$ and a pair of weight functions, what is the weighted sum of all models of $\phi$ over a domain of size $n$? It was shown that compu... | ['Ondřej Kuželka', 'Jan Tóth'] | 2022-11-02 | null | null | null | null | ['relational-reasoning'] | ['natural-language-processing'] | [ 1.01908289e-01 5.93893409e-01 6.51001036e-02 -4.47947443e-01
-8.96083891e-01 -5.17625391e-01 3.57161015e-02 3.77621382e-01
-7.27960646e-01 6.67235196e-01 -6.32995129e-01 -9.24212337e-01
-3.31401616e-01 -1.59265530e+00 -8.49312961e-01 -6.28048182e-01
-5.87959230e-01 1.12403274e+00 6.86571956e-01 -2.79841442... | [8.62928581237793, 6.730592727661133] |
30a62a6c-355d-48e6-80ad-ed6da199f3ee | quantitative-analysis-of-automatic-image | 1701.01480 | null | http://arxiv.org/abs/1701.01480v1 | http://arxiv.org/pdf/1701.01480v1.pdf | Quantitative Analysis of Automatic Image Cropping Algorithms: A Dataset and Comparative Study | Automatic photo cropping is an important tool for improving visual quality of
digital photos without resorting to tedious manual selection. Traditionally,
photo cropping is accomplished by determining the best proposal window through
visual quality assessment or saliency detection. In essence, the performance of
an ima... | ['Bing-Yu Chen', 'Hwann-Tzong Chen', 'Yu-Chen Tsai', 'Kai-Han Chang', 'Tzu-Wei Huang', 'Yi-Ling Chen'] | 2017-01-05 | null | null | null | null | ['image-cropping'] | ['computer-vision'] | [ 5.72594106e-01 -2.59883732e-01 -3.06216598e-01 -3.43470246e-01
-1.26558948e+00 -6.55146182e-01 3.88260752e-01 2.75562525e-01
-2.20122010e-01 4.09814179e-01 3.05772781e-01 -1.59166716e-02
1.31371081e-01 -4.72391456e-01 -7.41532683e-01 -5.20234942e-01
7.92816207e-02 -1.75394982e-01 4.62930083e-01 2.29439717... | [11.252705574035645, -1.061044454574585] |
8959aade-cae4-425b-9b15-4538aa6a1438 | vnhsge-vietnamese-high-school-graduation | 2305.12199 | null | https://arxiv.org/abs/2305.12199v1 | https://arxiv.org/pdf/2305.12199v1.pdf | VNHSGE: VietNamese High School Graduation Examination Dataset for Large Language Models | The VNHSGE (VietNamese High School Graduation Examination) dataset, developed exclusively for evaluating large language models (LLMs), is introduced in this article. The dataset, which covers nine subjects, was generated from the Vietnamese National High School Graduation Examination and comparable tests. 300 literary ... | ['Nguyen Hong-Phuoc', 'Nguyen Thi-My-Thanh', 'Nguyen Van-Tien', 'Ngo Bac-Bien', 'Phan Xuan-Dung', 'Vo The-Duy', 'Le Ngoc-Bich', 'Dao Xuan-Quy'] | 2023-05-20 | null | null | null | null | ['reading-comprehension', 'question-rewriting'] | ['natural-language-processing', 'natural-language-processing'] | [-3.63778859e-01 1.24361344e-01 3.47656086e-02 1.58325970e-01
-1.10334456e+00 -8.36572409e-01 7.99732566e-01 5.89137554e-01
-6.14327133e-01 8.65414619e-01 2.53343791e-01 -8.54230165e-01
-3.05699557e-01 -8.11624706e-01 -4.94329184e-01 -2.08845004e-01
4.08599615e-01 4.17869121e-01 3.83093730e-02 -5.21941602... | [11.058526039123535, 8.662670135498047] |
1002634b-7d4b-4e0d-b5e4-33d95353410a | controlling-perceived-emotion-in-symbolic | 2208.05162 | null | https://arxiv.org/abs/2208.05162v4 | https://arxiv.org/pdf/2208.05162v4.pdf | Controlling Perceived Emotion in Symbolic Music Generation with Monte Carlo Tree Search | This paper presents a new approach for controlling emotion in symbolic music generation with Monte Carlo Tree Search. We use Monte Carlo Tree Search as a decoding mechanism to steer the probability distribution learned by a language model towards a given emotion. At every step of the decoding process, we use Predictor ... | ['Levi H. S. Lelis', 'Jim Whitehead', 'Lili Mou', 'Lucas N. Ferreira'] | 2022-08-10 | null | null | null | null | ['music-generation', 'music-generation'] | ['audio', 'music'] | [ 2.29689389e-01 -7.80033097e-02 -8.14918354e-02 -4.21384454e-01
-1.08419323e+00 -6.30035162e-01 3.65578353e-01 4.39080670e-02
-2.84011424e-01 9.29233432e-01 2.76558161e-01 2.52288699e-01
-1.08516552e-01 -7.20496595e-01 -4.59456086e-01 -6.72535956e-01
-7.72692785e-02 7.01172948e-01 -6.15162365e-02 -4.95352037... | [16.009960174560547, 5.549931049346924] |
0cdce88d-cc15-4877-ba1e-d76a22bf6425 | optimal-transport-for-change-detection-on | 2302.07025 | null | https://arxiv.org/abs/2302.07025v3 | https://arxiv.org/pdf/2302.07025v3.pdf | Optimal Transport for Change Detection on LiDAR Point Clouds | Unsupervised change detection between airborne LiDAR data points, taken at separate times over the same location, can be difficult due to unmatching spatial support and noise from the acquisition system. Most current approaches to detect changes in point clouds rely heavily on the computation of Digital Elevation Model... | ['Makoto Yamada', 'Peter Naylor', 'Marco Fiorucci'] | 2023-02-14 | null | null | null | null | ['change-detection'] | ['computer-vision'] | [ 7.99128234e-01 -5.49135506e-01 1.22360535e-01 -5.44406712e-01
-8.90427291e-01 -6.72121227e-01 8.00466537e-01 6.57076657e-01
-8.36370230e-01 7.80702710e-01 -3.45588773e-01 -3.87804091e-01
-4.27455127e-01 -1.40410411e+00 -5.33583760e-01 -5.57592332e-01
-2.30795771e-01 9.23821211e-01 8.55851114e-01 -1.50780007... | [8.424668312072754, -2.5805282592773438] |
1e1118a3-3f29-4d9a-9a31-145ba0437e2d | evaluating-persuasion-strategies-and-deep | null | null | https://aclanthology.org/E17-2077 | https://aclanthology.org/E17-2077.pdf | Evaluating Persuasion Strategies and Deep Reinforcement Learning methods for Negotiation Dialogue agents | In this paper we present a comparative evaluation of various negotiation strategies within an online version of the game {``}Settlers of Catan{''}. The comparison is based on human subjects playing games against artificial game-playing agents ({`}bots{'}) which implement different negotiation dialogue strategies, using... | ['Klaus-Peter Engelbrecht', "Heriberto Cuay{\\'a}huitl", 'Markus Guhe', 'Ioannis Efstathiou', 'Alex Lascarides', 'Mihai Dobre', 'Simon Keizer', 'Oliver Lemon'] | 2017-04-01 | null | null | null | eacl-2017-4 | ['persuasion-strategies'] | ['computer-vision'] | [-2.54962910e-02 8.64756763e-01 1.36325747e-01 -2.90513605e-01
-5.71471453e-01 -8.47177446e-01 9.91949797e-01 -6.49086088e-02
-8.81062925e-01 1.19967270e+00 2.77536124e-01 -5.92367828e-01
-1.32620811e-01 -1.13989425e+00 1.04612343e-01 -4.31817681e-01
8.53997990e-02 1.06843603e+00 3.45800579e-01 -1.47112489... | [3.6688332557678223, 1.5448708534240723] |
7e4020ff-57f2-46b5-b63e-34f7dc91ef94 | meta-learning-initializations-for-interactive | 2210.15371 | null | https://arxiv.org/abs/2210.15371v1 | https://arxiv.org/pdf/2210.15371v1.pdf | Meta-Learning Initializations for Interactive Medical Image Registration | We present a meta-learning framework for interactive medical image registration. Our proposed framework comprises three components: a learning-based medical image registration algorithm, a form of user interaction that refines registration at inference, and a meta-learning protocol that learns a rapidly adaptable netwo... | ['Dean Barratt', 'Yipeng Hu', 'Zachary M. C. Baum'] | 2022-10-27 | null | null | null | null | ['medical-image-registration'] | ['medical'] | [ 5.40571094e-01 7.80318499e-01 -2.26198342e-02 -4.43491846e-01
-1.20579040e+00 -2.93826580e-01 4.25786823e-01 3.23111802e-01
-8.49699080e-01 3.87833208e-01 1.88152686e-01 -5.25301874e-01
-5.47304809e-01 -5.26037037e-01 -6.53228104e-01 -7.68640161e-01
-8.57646406e-01 9.13652599e-01 1.10889159e-01 -3.20512652... | [13.876609802246094, -2.6256189346313477] |
dcfce9d7-7687-4078-afa2-f9b05f46d45b | balancing-lexical-and-semantic-quality-in | 2305.09898 | null | https://arxiv.org/abs/2305.09898v1 | https://arxiv.org/pdf/2305.09898v1.pdf | Balancing Lexical and Semantic Quality in Abstractive Summarization | An important problem of the sequence-to-sequence neural models widely used in abstractive summarization is exposure bias. To alleviate this problem, re-ranking systems have been applied in recent years. Despite some performance improvements, this approach remains underexplored. Previous works have mostly specified the ... | ['Yong Suk Choi', 'Jeewoo Sul'] | 2023-05-17 | null | null | null | null | ['abstractive-text-summarization', 'semantic-textual-similarity', 'semantic-similarity'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 1.69192016e-01 -1.67077661e-01 -4.66039181e-01 -5.72189629e-01
-1.02022862e+00 -4.84404355e-01 5.14935434e-01 2.82255501e-01
-6.76192462e-01 8.92853916e-01 7.95142949e-01 -6.31446987e-02
5.82207926e-02 -7.23208368e-01 -5.55002451e-01 -4.05449808e-01
3.41332138e-01 8.51268321e-02 2.78256088e-01 -1.56973436... | [12.257293701171875, 9.292240142822266] |
270c83b7-0c57-4029-9ec9-f435f38d89e8 | can-characters-reveal-your-native-language-a | null | null | https://aclanthology.org/D14-1142 | https://aclanthology.org/D14-1142.pdf | Can characters reveal your native language? A language-independent approach to native language identification | null | ['Marius Popescu', 'Radu Tudor Ionescu', 'Aoife Cahill'] | 2014-10-01 | null | null | null | emnlp-2014-10 | ['native-language-identification'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.373507022857666, 3.6945815086364746] |
0de3a08b-a065-43df-b1e1-544ab75b620e | lipreading-with-long-short-term-memory | 1601.08188 | null | http://arxiv.org/abs/1601.08188v1 | http://arxiv.org/pdf/1601.08188v1.pdf | Lipreading with Long Short-Term Memory | Lipreading, i.e. speech recognition from visual-only recordings of a
speaker's face, can be achieved with a processing pipeline based solely on
neural networks, yielding significantly better accuracy than conventional
methods. Feed-forward and recurrent neural network layers (namely Long
Short-Term Memory; LSTM) are st... | ['Jürgen Schmidhuber', 'Jan Koutník', 'Michael Wand'] | 2016-01-29 | null | null | null | null | ['lipreading'] | ['computer-vision'] | [ 3.21178198e-01 1.75433397e-01 -1.09215908e-01 -5.98642766e-01
-1.10759163e+00 -1.84833840e-01 4.39736813e-01 -2.42378995e-01
-5.60253203e-01 2.93878049e-01 4.19409841e-01 -3.73168141e-01
2.97865897e-01 -1.33886486e-01 -6.60933554e-01 -5.40356100e-01
5.04759327e-02 -3.55705223e-03 -1.21080957e-01 2.58485317... | [14.350528717041016, 5.08786153793335] |
dcc8e4a8-ef86-498b-81aa-643fa13b7e54 | mixrts-toward-interpretable-multi-agent | 2209.07225 | null | https://arxiv.org/abs/2209.07225v2 | https://arxiv.org/pdf/2209.07225v2.pdf | MIXRTs: Toward Interpretable Multi-Agent Reinforcement Learning via Mixing Recurrent Soft Decision Trees | Multi-agent reinforcement learning (MARL) recently has achieved tremendous success in a wide range of fields. However, with a black-box neural network architecture, existing MARL methods make decisions in an opaque fashion that hinders humans from understanding the learned knowledge and how input observations influence... | ['Chunlin Chen', 'Yang Gao', 'Zhi Wang', 'Yuanyang Zhu', 'Zichuan Liu'] | 2022-09-15 | null | null | null | null | ['starcraft-ii', 'starcraft'] | ['playing-games', 'playing-games'] | [ 3.33030671e-01 6.54755235e-01 -5.09673715e-01 -3.84744167e-01
-4.74927247e-01 -7.55465686e-01 9.21637058e-01 5.76971844e-02
-5.44970185e-02 6.62738442e-01 5.01599073e-01 -5.55010200e-01
-3.30412477e-01 -4.35478956e-01 -6.41220808e-01 -6.70101285e-01
-7.81464428e-02 7.77589977e-01 -1.33033767e-01 -3.46609443... | [4.14121150970459, 1.7261509895324707] |
9c1de94f-d5a2-4fec-ad75-fbdf8922fd85 | exploiting-robust-unsupervised-video-person | 2111.05170 | null | https://arxiv.org/abs/2111.05170v3 | https://arxiv.org/pdf/2111.05170v3.pdf | Exploiting Robust Unsupervised Video Person Re-identification | Unsupervised video person re-identification (reID) methods usually depend on global-level features. And many supervised reID methods employed local-level features and achieved significant performance improvements. However, applying local-level features to unsupervised methods may introduce an unstable performance. To i... | ['Xiujun Shu', 'Wei Gao', 'Ge Li', 'Xianghao Zang'] | 2021-11-09 | exploiting-robust-unsupervised-video-person-1 | https://arxiv.org/abs/2111.05170 | https://arxiv.org/pdf/2111.05170.pdf | null | ['unsupervised-person-re-identification'] | ['computer-vision'] | [-2.80389965e-01 -4.59651321e-01 -2.30235353e-01 -4.66482282e-01
-6.12867832e-01 -1.12133719e-01 6.51042402e-01 1.88038617e-01
-6.84495270e-01 5.91473818e-01 4.66910660e-01 4.13744360e-01
-8.95124972e-02 -6.36190116e-01 -3.96687478e-01 -8.24975491e-01
-1.25047028e-01 -9.84602720e-02 2.45421246e-01 7.30738267... | [14.766327857971191, 1.0309139490127563] |
1843e0fa-81dc-4fa4-920a-dce7b166293b | suffering-toasters | 2306.17258 | null | https://arxiv.org/abs/2306.17258v2 | https://arxiv.org/pdf/2306.17258v2.pdf | Suffering Toasters -- A New Self-Awareness Test for AI | A widely accepted definition of intelligence in the context of Artificial Intelligence (AI) still eludes us. Due to our exceedingly rapid development of AI paradigms, architectures, and tools, the prospect of naturally arising AI consciousness seems more likely than ever. In this paper, we claim that all current intell... | ['Ira Wolfson'] | 2023-06-29 | null | null | null | null | ['philosophy'] | ['miscellaneous'] | [ 3.33490163e-01 4.38917756e-01 2.30639011e-01 -2.42159784e-01
4.16549951e-01 -5.89626491e-01 1.01639175e+00 6.17888756e-02
-5.33087075e-01 6.14163935e-01 2.35385686e-01 -6.15842104e-01
-3.93612772e-01 -6.69906437e-01 -1.52935192e-01 -4.06137466e-01
2.56503731e-01 4.12761837e-01 3.09436605e-03 -4.33940679... | [9.005188941955566, 6.356170654296875] |
615a2f29-1ff0-4163-a836-35b67b002539 | fenrir-physics-enhanced-regression-for | 2202.01287 | null | https://arxiv.org/abs/2202.01287v2 | https://arxiv.org/pdf/2202.01287v2.pdf | Fenrir: Physics-Enhanced Regression for Initial Value Problems | We show how probabilistic numerics can be used to convert an initial value problem into a Gauss--Markov process parametrised by the dynamics of the initial value problem. Consequently, the often difficult problem of parameter estimation in ordinary differential equations is reduced to hyperparameter estimation in Gauss... | ['Philipp Hennig', 'Nathanael Bosch', 'Filip Tronarp'] | 2022-02-02 | null | null | null | null | ['numerical-integration'] | ['miscellaneous'] | [-2.24376634e-01 -5.13592474e-02 -2.55150318e-01 -4.59349416e-02
-1.12296748e+00 -5.36479294e-01 7.75903106e-01 -1.49999633e-02
-7.37770438e-01 1.28873277e+00 -3.39877069e-01 -4.10685927e-01
-3.81243229e-01 -4.21486706e-01 -2.89075077e-01 -1.23995233e+00
-6.68220967e-02 8.56535673e-01 1.41152099e-01 -3.45229208... | [6.626845836639404, 3.932269811630249] |
cc5c8506-fe6a-47ce-ac69-d57c4c36df8e | cnn-based-autoencoder-application-in-breast | null | null | https://ieeexplore.ieee.org/document/9502205 | https://ieeexplore.ieee.org/document/9502205 | CNN Based Autoencoder Application in Breast Cancer Image Retrieval | Content Based Medical Image Retrieval (CBMIR) is considered as a common technique to retrieve relevant images by comparing the features contained in the query image with the features contained in the image located in the database. Currently, the study related to CBMIR on breast cancer image however remains challenging ... | ['Fauzi Dwi Setiawan Sumadi', 'Lailatul Husniah', 'Trfebi Shina Sabrila', 'Kharisma Muzaki Ghufron', 'Agus Eko Minarno'] | 2021-08-04 | null | null | null | international-seminar-on-intelligent | ['medical-image-retrieval', 'medical-image-retrieval'] | ['computer-vision', 'medical'] | [-8.67924392e-02 -2.57266700e-01 -1.92845955e-01 -6.85965791e-02
-6.43194675e-01 1.86574087e-02 4.54894900e-01 5.83914220e-01
-7.78255939e-01 5.54655254e-01 3.71766001e-01 1.23063035e-01
-6.47560358e-01 -9.29821253e-01 -1.62727222e-01 -8.83789897e-01
2.85303473e-01 1.29610971e-01 6.34869114e-02 -2.61353731... | [14.36076545715332, -1.5549200773239136] |
3d1805c1-eb35-491a-ba8f-70cb3bfda4e1 | on-the-robustness-of-ensemble-based-machine | 2209.14013 | null | https://arxiv.org/abs/2209.14013v2 | https://arxiv.org/pdf/2209.14013v2.pdf | On the Robustness of Random Forest Against Untargeted Data Poisoning: An Ensemble-Based Approach | Machine learning is becoming ubiquitous. From finance to medicine, machine learning models are boosting decision-making processes and even outperforming humans in some tasks. This huge progress in terms of prediction quality does not however find a counterpart in the security of such models and corresponding prediction... | ['Chan Yeob Yeun', 'Ernesto Damiani', 'Nicola Bena', 'Alessandro Balestrucci', 'Claudio A. Ardagna', 'Marco Anisetti'] | 2022-09-28 | null | null | null | null | ['data-poisoning'] | ['adversarial'] | [ 3.46324921e-01 -2.17752885e-02 -1.02000520e-01 -1.61103718e-02
-5.07304192e-01 -7.33149946e-01 7.83685207e-01 4.57827240e-01
-5.02640784e-01 9.09014523e-01 -6.44190013e-02 -7.38061130e-01
5.01515940e-02 -1.22520435e+00 -5.89935005e-01 -1.08771789e+00
-2.75829315e-01 5.39289594e-01 5.16608715e-01 -3.90686393... | [5.813967227935791, 7.503772735595703] |
7002fa82-e8af-4cdb-b7d6-fafea64d0aed | answering-unanswered-questions-through | 2305.17393 | null | https://arxiv.org/abs/2305.17393v2 | https://arxiv.org/pdf/2305.17393v2.pdf | Answering Unanswered Questions through Semantic Reformulations in Spoken QA | Spoken Question Answering (QA) is a key feature of voice assistants, usually backed by multiple QA systems. Users ask questions via spontaneous speech which can contain disfluencies, errors, and informal syntax or phrasing. This is a major challenge in QA, causing unanswered questions or irrelevant answers, and leading... | ['Shervin Malmasi', 'Oleg Rokhlenko', 'Besnik Fetahu', 'Zhiyu Chen', 'Pedro Faustini'] | 2023-05-27 | null | null | null | null | ['specificity'] | ['natural-language-processing'] | [ 5.93837388e-02 7.29239285e-01 4.13483024e-01 -5.31723976e-01
-1.54436743e+00 -1.07578707e+00 1.81301907e-01 -5.07159419e-02
-2.03763664e-01 8.36038589e-01 9.87888336e-01 -7.72863328e-01
-1.71029434e-01 -4.60209221e-01 -2.04953477e-01 3.19715977e-01
6.33896530e-01 8.00535679e-01 7.58737743e-01 -1.23338807... | [11.76576042175293, 7.997804641723633] |
5b669513-95d2-46f4-90c2-e76e94550a6b | summarize-then-answer-generating-concise | 2109.06853 | null | https://arxiv.org/abs/2109.06853v1 | https://arxiv.org/pdf/2109.06853v1.pdf | Summarize-then-Answer: Generating Concise Explanations for Multi-hop Reading Comprehension | How can we generate concise explanations for multi-hop Reading Comprehension (RC)? The current strategies of identifying supporting sentences can be seen as an extractive question-focused summarization of the input text. However, these extractive explanations are not necessarily concise i.e. not minimally sufficient fo... | ['Kentaro Inui', 'Niranjan Balasubramanian', 'Steven Sinha', 'Harsh Trivedi', 'Naoya Inoue'] | 2021-09-14 | null | https://aclanthology.org/2021.emnlp-main.490 | https://aclanthology.org/2021.emnlp-main.490.pdf | emnlp-2021-11 | ['multi-hop-reading-comprehension'] | ['natural-language-processing'] | [ 7.52935946e-01 1.25452709e+00 -2.28454217e-01 -4.82604235e-01
-1.16828048e+00 -5.51248133e-01 6.11865878e-01 4.77110326e-01
-2.45291263e-01 1.06581450e+00 6.23050749e-01 -6.98667765e-01
-2.85408437e-01 -4.87080425e-01 -8.34312141e-01 -6.46678880e-02
2.44541258e-01 5.57622492e-01 -4.95229661e-02 -8.10957700... | [12.2748441696167, 9.26167106628418] |
38fa9cb4-c816-4aec-af11-43195c61d2ff | a-singing-voice-database-in-basque-for | null | null | https://aclanthology.org/L16-1120 | https://aclanthology.org/L16-1120.pdf | A Singing Voice Database in Basque for Statistical Singing Synthesis of Bertsolaritza | This paper describes the characteristics and structure of a Basque singing voice database of bertsolaritza. Bertsolaritza is a popular singing style from Basque Country sung exclusively in Basque that is improvised and a capella. The database is designed to be used in statistical singing voice synthesis for bertsolarit... | ['David Tavarez', 'Daniel Erro', 'Inma Hernaez', 'Ibon Saratxaga', 'Eva Navas', 'Xabier Sarasola'] | 2016-05-01 | a-singing-voice-database-in-basque-for-1 | https://aclanthology.org/L16-1120 | https://aclanthology.org/L16-1120.pdf | lrec-2016-5 | ['singing-voice-synthesis'] | ['speech'] | [ 1.23303011e-01 -2.42481977e-01 3.18398803e-01 -4.99817431e-02
-6.01658583e-01 -8.55625451e-01 6.03768647e-01 -4.05596673e-01
-3.30109037e-02 6.56996310e-01 2.92573392e-01 -1.23511948e-01
-2.79745936e-01 -2.48369619e-01 -4.84133884e-02 -6.89821661e-01
-3.98329534e-02 7.31904805e-01 7.42049143e-02 -7.19070792... | [15.125420570373535, 6.242941379547119] |
f74018a3-6564-4fab-af82-6021aad5df4a | ballgan-3d-aware-image-synthesis-with-a | 2301.09091 | null | https://arxiv.org/abs/2301.09091v1 | https://arxiv.org/pdf/2301.09091v1.pdf | BallGAN: 3D-aware Image Synthesis with a Spherical Background | 3D-aware GANs aim to synthesize realistic 3D scenes such that they can be rendered in arbitrary perspectives to produce images. Although previous methods produce realistic images, they suffer from unstable training or degenerate solutions where the 3D geometry is unnatural. We hypothesize that the 3D geometry is underd... | ['Youngjung Uh', 'Hyeran Byun', 'Hyunsu Kim', 'Young Sun Choi', 'Jeongmin Bae', 'Yunji Seo', 'Minjung Shin'] | 2023-01-22 | null | null | null | null | ['3d-aware-image-synthesis'] | ['computer-vision'] | [ 3.39897335e-01 2.79018104e-01 3.48664492e-01 -2.03135788e-01
-5.15469551e-01 -5.17587900e-01 5.98072946e-01 -8.66595209e-01
1.46211892e-01 7.09513009e-01 -7.35923797e-02 -1.48666292e-01
5.07288218e-01 -8.65201294e-01 -8.81338239e-01 -9.21885550e-01
6.72911167e-01 4.98257637e-01 4.37937319e-01 -1.24145253... | [9.354724884033203, -3.121927261352539] |
13de4271-5372-42d5-8304-c7d8ab7e33cd | squash-root-microbiome-transplants-and | 2109.07521 | null | https://arxiv.org/abs/2109.07521v1 | https://arxiv.org/pdf/2109.07521v1.pdf | Squash root microbiome transplants and metagenomic inspection for in situ arid adaptations | Arid zones contain a diverse set of microbes capable of survival under dry conditions, some of which can form relationships with plants under drought stress conditions to improve plant health. We studied squash (Cucurbita pepo L.) root microbiome under historically arid and humid sites, both in situ and performing a co... | ['Luis D. Alcaraz', 'Daniel Piñero', 'Hugo R. Barajas', 'Miguel F. Romero', 'Rocío Cruz-Ortega', 'Felipe García-Oliva', 'Cristóbal Hernández-Álvarez'] | 2021-09-15 | null | null | null | null | ['plant-phenotyping'] | ['computer-vision'] | [ 3.44933271e-01 -1.83605418e-01 -1.63991541e-01 4.26273197e-01
5.04101753e-01 -1.00935435e+00 2.70992577e-01 5.82494617e-01
3.21517736e-02 8.93741608e-01 7.27019161e-02 -5.93106747e-01
-1.47759318e-01 -1.21363616e+00 -5.46230495e-01 -1.04786301e+00
-6.83549762e-01 2.98904926e-01 -5.75438738e-02 -5.29923081... | [5.057920455932617, 4.940194606781006] |
c955b1cb-d2b7-400e-8858-280c6a692010 | mus-cdb-mixed-uncertainty-sampling-with-class | 2212.02804 | null | https://arxiv.org/abs/2212.02804v3 | https://arxiv.org/pdf/2212.02804v3.pdf | MUS-CDB: Mixed Uncertainty Sampling with Class Distribution Balancing for Active Annotation in Aerial Object Detection | Recent aerial object detection models rely on a large amount of labeled training data, which requires unaffordable manual labeling costs in large aerial scenes with dense objects. Active learning is effective in reducing the data labeling cost by selectively querying the informative and representative unlabelled sample... | ['Sheng-Jun Huang', 'Ying-Peng Tang', 'Jing-Wei Zhang', 'Dong Liang'] | 2022-12-06 | null | null | null | null | ['active-object-detection'] | ['computer-vision'] | [ 2.23816141e-01 5.74160330e-02 -5.12998819e-01 -3.98298979e-01
-9.60969567e-01 -4.32757646e-01 1.28689811e-01 1.89085364e-01
-4.59526211e-01 6.52293682e-01 -3.09300244e-01 -1.42793730e-01
-3.08594674e-01 -8.53178740e-01 -6.05578721e-01 -1.00860655e+00
1.44242585e-01 2.69016832e-01 5.80465078e-01 1.16074085... | [9.212244033813477, 1.0190373659133911] |
115d9989-d728-4b8f-b83e-3cd7c6ea45e1 | shapestacks-learning-vision-based-physical | 1804.08018 | null | http://arxiv.org/abs/1804.08018v2 | http://arxiv.org/pdf/1804.08018v2.pdf | ShapeStacks: Learning Vision-Based Physical Intuition for Generalised Object Stacking | Physical intuition is pivotal for intelligent agents to perform complex
tasks. In this paper we investigate the passive acquisition of an intuitive
understanding of physical principles as well as the active utilisation of this
intuition in the context of generalised object stacking. To this end, we
provide: a simulatio... | ['Ingmar Posner', 'Andrea Vedaldi', 'Oliver Groth', 'Fabian B. Fuchs'] | 2018-04-21 | shapestacks-learning-vision-based-physical-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Oliver_Groth_ShapeStacks_Learning_Vision-Based_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Oliver_Groth_ShapeStacks_Learning_Vision-Based_ECCV_2018_paper.pdf | eccv-2018-9 | ['physical-intuition'] | ['reasoning'] | [-1.67070925e-02 5.50889552e-01 1.26285836e-01 -6.91090673e-02
-2.41355091e-01 -1.05056906e+00 1.14289725e+00 4.82362658e-01
-6.09273724e-02 3.49326253e-01 2.63593405e-01 -5.33456147e-01
-3.87323648e-01 -7.48572767e-01 -1.08708096e+00 -7.03922093e-01
-6.12117231e-01 8.13427448e-01 7.69902349e-01 -7.57048070... | [8.406485557556152, 0.9822599291801453] |
d01f8335-aec9-45d8-8876-f7419f83c349 | abstractive-text-summarization-using | 2302.13117 | null | https://arxiv.org/abs/2302.13117v1 | https://arxiv.org/pdf/2302.13117v1.pdf | Abstractive Text Summarization using Attentive GRU based Encoder-Decoder | In todays era huge volume of information exists everywhere. Therefore, it is very crucial to evaluate that information and extract useful, and often summarized, information out of it so that it may be used for relevant purposes. This extraction can be achieved through a crucial technique of artificial intelligence, nam... | ['Samiran Chattopadhyay', 'Debarshi Kumar Sanyal', 'Suchandan Das', 'Tohida Rehman'] | 2023-02-25 | null | null | null | null | ['abstractive-text-summarization'] | ['natural-language-processing'] | [ 4.51257735e-01 5.03461957e-01 -2.77510226e-01 -2.09979460e-01
-1.03249216e+00 -4.25432533e-01 7.74300039e-01 8.44030559e-01
-5.27827263e-01 1.33319771e+00 1.08757591e+00 -2.18778029e-01
2.18101069e-01 -4.97313231e-01 -4.35357422e-01 -2.98154473e-01
3.97111773e-01 3.07356447e-01 1.35228887e-01 -5.11183441... | [12.522771835327148, 9.522319793701172] |
3398f88d-b7f8-4dcb-82cc-269ed7250395 | didfuse-deep-image-decomposition-for-infrared | 2003.09210 | null | https://arxiv.org/abs/2003.09210v3 | https://arxiv.org/pdf/2003.09210v3.pdf | DIDFuse: Deep Image Decomposition for Infrared and Visible Image Fusion | Infrared and visible image fusion, a hot topic in the field of image processing, aims at obtaining fused images keeping the advantages of source images. This paper proposes a novel auto-encoder (AE) based fusion network. The core idea is that the encoder decomposes an image into background and detail feature maps with ... | ['Chun-Xia Zhang', 'Junmin Liu', 'Jiangshe Zhang', 'Zixiang Zhao', 'Shuang Xu', 'Pengfei Li'] | 2020-03-20 | null | null | null | null | ['infrared-and-visible-image-fusion'] | ['computer-vision'] | [ 5.26244044e-01 -4.74046052e-01 2.07095087e-01 -7.47090131e-02
-8.42146754e-01 -2.24902168e-01 4.37915146e-01 -2.14124352e-01
-4.05793861e-02 6.37382329e-01 1.85429722e-01 1.48754239e-01
-1.47540420e-01 -8.43274057e-01 -4.76711899e-01 -1.23485672e+00
3.89140874e-01 -4.61088896e-01 4.47892137e-02 -4.20639664... | [10.604340553283691, -1.9327218532562256] |
9c4c474e-9888-482d-8c4e-d57582077c36 | wavedm-wavelet-based-diffusion-models-for | 2305.13819 | null | https://arxiv.org/abs/2305.13819v1 | https://arxiv.org/pdf/2305.13819v1.pdf | WaveDM: Wavelet-Based Diffusion Models for Image Restoration | Latest diffusion-based methods for many image restoration tasks outperform traditional models, but they encounter the long-time inference problem. To tackle it, this paper proposes a Wavelet-Based Diffusion Model (WaveDM) with an Efficient Conditional Sampling (ECS) strategy. WaveDM learns the distribution of clean ima... | ['Shifeng Chen', 'Jiaxi Lv', 'Yu Dong', 'Jianzhuang Liu', 'Jiancheng Huang', 'Yi Huang'] | 2023-05-23 | null | null | null | null | ['deblurring', 'image-restoration'] | ['computer-vision', 'computer-vision'] | [ 3.89232278e-01 -5.07542670e-01 1.95760369e-01 -2.19230697e-01
-1.11698663e+00 -7.10247830e-02 5.66846550e-01 -3.24500471e-01
-5.06586730e-01 6.68731153e-01 3.53266090e-01 -2.98301786e-01
-1.27489135e-01 -9.20845807e-01 -6.26913726e-01 -1.42252660e+00
5.12150396e-03 1.85414210e-01 3.60130042e-01 9.94994715... | [11.588811874389648, -2.391327142715454] |
52a3a9f8-77d1-4ca8-b3a3-665d282dd988 | experimental-exploration-of-compact | 1911.09010 | null | https://arxiv.org/abs/1911.09010v1 | https://arxiv.org/pdf/1911.09010v1.pdf | Experimental Exploration of Compact Convolutional Neural Network Architectures for Non-temporal Real-time Fire Detection | In this work we explore different Convolutional Neural Network (CNN) architectures and their variants for non-temporal binary fire detection and localization in video or still imagery. We consider the performance of experimentally defined, reduced complexity deep CNN architectures for this task and evaluate the effects... | ['Ganesh Samarth C. A.', 'Toby P. Breckon', 'Neelanjan Bhowmik'] | 2019-11-20 | null | null | null | null | ['fire-detection'] | ['time-series'] | [ 4.74551946e-01 -5.49333930e-01 4.04181093e-01 1.65738583e-01
-1.06488258e-01 -6.47707880e-01 7.19036460e-01 -3.06622714e-01
-1.16475904e+00 7.62414753e-01 -2.94719070e-01 -5.88497035e-02
-1.78951845e-01 -8.61498296e-01 -2.57585168e-01 -7.29164779e-01
-2.92393446e-01 -5.85142784e-02 6.60099983e-01 -3.10826153... | [8.7676420211792, -1.0500773191452026] |
b6571b17-1d86-48e3-8e83-40618fbd422f | pac-hubert-self-supervised-music-source | 2304.02160 | null | https://arxiv.org/abs/2304.02160v1 | https://arxiv.org/pdf/2304.02160v1.pdf | Pac-HuBERT: Self-Supervised Music Source Separation via Primitive Auditory Clustering and Hidden-Unit BERT | In spite of the progress in music source separation research, the small amount of publicly-available clean source data remains a constant limiting factor for performance. Thus, recent advances in self-supervised learning present a largely-unexplored opportunity for improving separation models by leveraging unlabelled m... | ['Jonathan Le Roux', 'François G. Germain', 'Gordon Wichern', 'Ke Chen'] | 2023-04-04 | null | null | null | null | ['music-source-separation'] | ['music'] | [ 4.85847384e-01 -2.47790694e-01 -1.73035219e-01 -1.13680139e-01
-1.38724566e+00 -8.38752270e-01 4.79106277e-01 8.95960480e-02
-2.91898936e-01 4.64200556e-01 4.51560348e-01 -4.53549810e-02
-5.64122617e-01 -2.23483786e-01 -5.54239154e-01 -8.25882435e-01
-1.01826154e-01 2.97172070e-01 7.01287538e-02 -2.19372675... | [15.467473030090332, 5.519401550292969] |
df52d006-25e6-47c8-813c-b2e2ce92d923 | uni6dv3-5d-anchor-mechanism-for-6d-pose | 2210.10959 | null | https://arxiv.org/abs/2210.10959v5 | https://arxiv.org/pdf/2210.10959v5.pdf | Geo6D: Geometric Constraints Learning for 6D Pose Estimation | Existing direct 6D pose estimation methods regress target 6D poses without the need for post-processing, making them effective and easy to develop. However, due to the lack of geometric constraints, the precision of these approaches remains limited, and more training data are required to achieve better performance. To ... | ['Xiaoke Jiang', 'Liwei Wu', 'Rui Zhao', 'Guoqiang Jin', 'Donghai Li', 'Zhenyu He', 'Tianpeng Bao', 'Ye Zheng', 'Mingshan Sun', 'Jianqiu Chen'] | 2022-10-20 | null | null | null | null | ['6d-pose-estimation-1'] | ['computer-vision'] | [-3.13167542e-01 -1.90723464e-01 -2.67991513e-01 -3.94061506e-01
-6.01554096e-01 -4.04695988e-01 5.94795823e-01 -1.38954833e-01
-1.65107876e-01 1.16238944e-01 -4.49252911e-02 -5.28808795e-02
-8.32443312e-03 -5.08771837e-01 -8.58914137e-01 -5.01923859e-01
2.48760745e-01 4.37406540e-01 3.50695699e-01 -5.18910140... | [7.546899318695068, -2.6276497840881348] |
33026028-36ec-4246-88a6-a880d7e29431 | ood-cv-v2-an-extended-benchmark-for | 2304.10266 | null | https://arxiv.org/abs/2304.10266v1 | https://arxiv.org/pdf/2304.10266v1.pdf | OOD-CV-v2: An extended Benchmark for Robustness to Out-of-Distribution Shifts of Individual Nuisances in Natural Images | Enhancing the robustness of vision algorithms in real-world scenarios is challenging. One reason is that existing robustness benchmarks are limited, as they either rely on synthetic data or ignore the effects of individual nuisance factors. We introduce OOD-CV-v2, a benchmark dataset that includes out-of-distribution e... | ['Adam Kortylewski', 'Alan Yuille', 'Christian Theobalt', 'Oliver Zendel', 'Shaozuo Yu', 'Siwei Yang', 'Artur Jesslen', 'Wufei Ma', 'Jiahao Wang', 'Bingchen Zhao'] | 2023-04-17 | null | null | null | null | ['3d-pose-estimation'] | ['computer-vision'] | [-1.02166720e-01 -5.65027058e-01 1.82323664e-01 -2.70591527e-01
-5.95908165e-01 -1.03851509e+00 8.79486024e-01 -6.74420074e-02
-4.87730265e-01 3.59116018e-01 2.88115233e-01 -7.85375610e-02
2.30807319e-01 -3.72188747e-01 -8.75319004e-01 -6.00183010e-01
-2.18787611e-01 3.91703881e-02 6.01550937e-01 -3.07855219... | [8.085175514221191, -1.3930728435516357] |
9f65cf71-e3fe-4289-a531-32561952704b | reinforcement-learning-based-sequential-batch | 2112.10944 | null | https://arxiv.org/abs/2112.10944v2 | https://arxiv.org/pdf/2112.10944v2.pdf | Reinforcement Learning based Sequential Batch-sampling for Bayesian Optimal Experimental Design | Engineering problems that are modeled using sophisticated mathematical methods or are characterized by expensive-to-conduct tests or experiments, are encumbered with limited budget or finite computational resources. Moreover, practical scenarios in the industry, impose restrictions, based on logistics and preference, o... | ['Sayan Ghosh', 'Piyush Pandita', 'Yonatan Ashenafi'] | 2021-12-21 | null | null | null | null | ['policy-gradient-methods'] | ['methodology'] | [ 1.78610414e-01 -1.54201075e-01 -2.16590241e-01 -1.10034451e-01
-5.57110965e-01 -4.28295642e-01 3.31358820e-01 1.01106904e-01
-5.78400731e-01 1.02303183e+00 -4.51146275e-01 -4.59624141e-01
-6.22449338e-01 -7.54693449e-01 -8.38096142e-01 -7.29127288e-01
-1.18715443e-01 4.34925765e-01 -1.10649273e-01 5.62802479... | [4.424655437469482, 2.4504895210266113] |
64c2fe83-b110-40ce-b8de-0dc87c744c65 | spectral-reconstruction-with-deep-neural | 1905.04305 | null | https://arxiv.org/abs/1905.04305v2 | https://arxiv.org/pdf/1905.04305v2.pdf | Spectral Reconstruction with Deep Neural Networks | We explore artificial neural networks as a tool for the reconstruction of spectral functions from imaginary time Green's functions, a classic ill-conditioned inverse problem. Our ansatz is based on a supervised learning framework in which prior knowledge is encoded in the training data and the inverse transformation ma... | ['Felix P. G. Ziegler', 'Nicolas Wink', 'Manuel Scherzer', 'Alexander Rothkopf', 'Sebastian J. Wetzel', 'Julian M. Urban', 'Lukas Kades', 'Jan M. Pawlowski'] | 2019-05-10 | null | null | null | null | ['spectral-reconstruction'] | ['computer-vision'] | [ 6.01730704e-01 3.07422012e-01 2.27176994e-01 -3.44585925e-01
-6.87095404e-01 -2.88099915e-01 8.11842024e-01 -3.04532409e-01
-5.91206551e-01 1.00659466e+00 -4.22066487e-02 -4.10237074e-01
-7.80775845e-01 -6.72104836e-01 -5.01296461e-01 -1.26850379e+00
-8.73090476e-02 7.90524721e-01 1.75038241e-02 -1.90062791... | [12.145406723022461, -2.478773593902588] |
14693a14-6d7f-4db1-9997-65aead2ad932 | a-hybrid-data-driven-physics-constrained | 2205.06494 | null | https://arxiv.org/abs/2205.06494v2 | https://arxiv.org/pdf/2205.06494v2.pdf | A hybrid data driven-physics constrained Gaussian process regression framework with deep kernel for uncertainty quantification | Gaussian process regression (GPR) has been a well-known machine learning method for various applications such as uncertainty quantifications (UQ). However, GPR is inherently a data-driven method, which requires sufficiently large dataset. If appropriate physics constraints (e.g. expressed in partial differential equati... | ['Tieyong Zeng', 'Cheng Chang'] | 2022-05-13 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [ 2.32028961e-02 1.16892932e-02 2.47713774e-01 -4.98390824e-01
-9.73208845e-01 -1.60552889e-01 6.59974098e-01 1.82422489e-01
-5.45792520e-01 8.88993382e-01 -6.97317719e-02 -9.77174342e-02
-3.14008623e-01 -1.14655864e+00 -7.78358817e-01 -1.08216560e+00
2.90221781e-01 8.47173750e-01 3.42434436e-01 3.50781322... | [6.7585859298706055, 3.6082468032836914] |
603f7736-016e-4418-a12c-6843245012d7 | npr-nocturnal-place-recognition-in-street | 2304.00276 | null | https://arxiv.org/abs/2304.00276v2 | https://arxiv.org/pdf/2304.00276v2.pdf | NPR: Nocturnal Place Recognition in Streets | Visual Place Recognition (VPR) is the task of retrieving database images similar to a query photo by comparing it to a large database of known images. In real-world applications, extreme illumination changes caused by query images taken at night pose a significant obstacle that VPR needs to overcome. However, a trainin... | ['Hong Zhang', 'Yihong Wu', 'Jinqiang Cui', 'Feng Lu', 'Yujie Fu', 'Bingxi Liu'] | 2023-04-01 | null | null | null | null | ['visual-place-recognition'] | ['computer-vision'] | [ 2.20554933e-01 -5.87326765e-01 1.60986204e-02 -4.93672013e-01
-1.16398776e+00 -9.03761685e-01 7.20406353e-01 -8.00734852e-03
-3.10952663e-01 7.33297229e-01 -5.67144807e-03 -2.39245176e-01
1.25090033e-01 -7.63661146e-01 -8.91468465e-01 -5.30347288e-01
2.18301013e-01 3.09138328e-01 4.14295554e-01 -2.65532583... | [7.646080493927002, -1.8372936248779297] |
1ac07ab1-8fba-4189-81d6-42bd7f828a7b | unsupervised-intrinsic-image-decomposition | 2303.10820 | null | https://arxiv.org/abs/2303.10820v2 | https://arxiv.org/pdf/2303.10820v2.pdf | Unsupervised Intrinsic Image Decomposition with LiDAR Intensity | Intrinsic image decomposition (IID) is the task that decomposes a natural image into albedo and shade. While IID is typically solved through supervised learning methods, it is not ideal due to the difficulty in observing ground truth albedo and shade in general scenes. Conversely, unsupervised learning methods are curr... | ['Jun Shimamura', 'Shingo Ando', 'Takuhiro Kaneko', 'Taiga Yoshida', 'Yasuhiro Yao', 'Shogo Sato'] | 2023-03-20 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Sato_Unsupervised_Intrinsic_Image_Decomposition_With_LiDAR_Intensity_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Sato_Unsupervised_Intrinsic_Image_Decomposition_With_LiDAR_Intensity_CVPR_2023_paper.pdf | cvpr-2023-1 | ['intrinsic-image-decomposition'] | ['computer-vision'] | [ 2.75272816e-01 -4.16099101e-01 -5.80328032e-02 -5.11084199e-01
-6.35056555e-01 -2.60469586e-01 3.08346301e-01 -4.30520140e-02
-4.80764091e-01 7.37468004e-01 -3.14918816e-01 -7.94718340e-02
3.09850294e-02 -9.35843050e-01 -4.86658901e-01 -9.17527676e-01
2.96005189e-01 2.37709552e-01 -3.21846381e-02 1.42861500... | [9.983009338378906, -2.6689023971557617] |
54f610fa-54ba-4093-adb6-0cae97a5124e | metric-learning-and-adaptive-boundary-for-out | 2204.10849 | null | https://arxiv.org/abs/2204.10849v1 | https://arxiv.org/pdf/2204.10849v1.pdf | Metric Learning and Adaptive Boundary for Out-of-Domain Detection | Conversational agents are usually designed for closed-world environments. Unfortunately, users can behave unexpectedly. Based on the open-world environment, we often encounter the situation that the training and test data are sampled from different distributions. Then, data from different distributions are called out-o... | ['Jan Šedivý', 'Ondřej Kobza', 'Petr Marek', 'Jakub Konrád', 'Jan Pichl', 'Tommaso Gargiani', 'Petr Lorenc'] | 2022-04-22 | null | null | null | null | ['open-intent-detection'] | ['natural-language-processing'] | [-6.51694313e-02 3.24396826e-02 5.04606552e-02 -5.09292722e-01
-7.20340312e-01 -6.23713374e-01 8.28717589e-01 -2.69525703e-02
-2.52687693e-01 9.01579738e-01 2.49401674e-01 -7.19951689e-02
1.20844394e-01 -6.34018242e-01 -1.04388520e-01 -7.35414207e-01
-1.68762475e-01 9.55527663e-01 3.41719806e-01 -1.55830517... | [12.529240608215332, 7.613171100616455] |
09c004d1-5869-47c3-9734-b9fb8f479d20 | learning-to-decouple-relations-few-shot | 2010.10894 | null | https://arxiv.org/abs/2010.10894v1 | https://arxiv.org/pdf/2010.10894v1.pdf | Learning to Decouple Relations: Few-Shot Relation Classification with Entity-Guided Attention and Confusion-Aware Training | This paper aims to enhance the few-shot relation classification especially for sentences that jointly describe multiple relations. Due to the fact that some relations usually keep high co-occurrence in the same context, previous few-shot relation classifiers struggle to distinguish them with few annotated instances. To... | ['Tiejun Zhao', 'BoWen Zhou', 'Xiaodong He', 'Youzheng Wu', 'Guangyi Liu', 'Junwei Bao', 'Yingyao Wang'] | 2020-10-21 | null | https://aclanthology.org/2020.coling-main.510 | https://aclanthology.org/2020.coling-main.510.pdf | coling-2020-8 | ['few-shot-relation-classification', 'few-shot-relation-classification'] | ['methodology', 'natural-language-processing'] | [ 1.41590178e-01 5.51024735e-01 -2.92017758e-01 -5.33939123e-01
-8.23263466e-01 -2.77408540e-01 5.63798308e-01 4.41670507e-01
-1.55837074e-01 8.05861115e-01 2.60212988e-01 -4.66023862e-01
-2.13816270e-01 -7.74391830e-01 -3.72562349e-01 -6.94272339e-01
2.08347267e-03 5.89839339e-01 1.61363721e-01 -4.85066712... | [9.332364082336426, 8.587111473083496] |
b248baff-fce1-4601-803f-053b54318d6d | rotating-features-for-object-discovery | 2306.00600 | null | https://arxiv.org/abs/2306.00600v1 | https://arxiv.org/pdf/2306.00600v1.pdf | Rotating Features for Object Discovery | The binding problem in human cognition, concerning how the brain represents and connects objects within a fixed network of neural connections, remains a subject of intense debate. Most machine learning efforts addressing this issue in an unsupervised setting have focused on slot-based methods, which may be limiting due... | ['Max Welling', 'Francesco Locatello', 'Phillip Lippe', 'Sindy Löwe'] | 2023-06-01 | null | null | null | null | ['object-discovery'] | ['computer-vision'] | [-1.20439842e-01 2.28490368e-01 -2.02622071e-01 -6.04890883e-01
-4.38977420e-01 -4.79189515e-01 8.94524395e-01 2.66178578e-01
-3.61176223e-01 8.63090515e-01 3.87132838e-02 2.82413792e-02
-5.33041596e-01 -8.38930964e-01 -6.44064307e-01 -7.75131822e-01
-1.93985403e-01 7.71257460e-01 3.50906461e-01 -3.22035439... | [9.418134689331055, 2.640986919403076] |
df3b6dea-1e88-4cd8-921c-e43aaa517dbe | u-tilise-a-sequence-to-sequence-model-for | 2305.13277 | null | https://arxiv.org/abs/2305.13277v1 | https://arxiv.org/pdf/2305.13277v1.pdf | U-TILISE: A Sequence-to-sequence Model for Cloud Removal in Optical Satellite Time Series | Satellite image time series in the optical and infrared spectrum suffer from frequent data gaps due to cloud cover, cloud shadows, and temporary sensor outages. It has been a long-standing problem of remote sensing research how to best reconstruct the missing pixel values and obtain complete, cloud-free image sequences... | ['Konrad Schindler', 'Vivien Sainte Fare Garnot', 'Corinne Stucker'] | 2023-05-22 | null | null | null | null | ['cloud-removal'] | ['computer-vision'] | [ 6.76075161e-01 -4.66669828e-01 1.43925056e-01 -5.27530611e-01
-8.29202592e-01 -5.61604321e-01 4.49774742e-01 -8.09873492e-02
-4.78888482e-01 7.53818274e-01 1.77255139e-01 -3.20462346e-01
4.62058485e-02 -9.05265510e-01 -8.92376184e-01 -1.05320215e+00
-5.25890172e-01 -1.53025985e-01 -3.23393606e-02 -2.38709571... | [9.764838218688965, -1.6961698532104492] |
9d35b856-feec-4741-a93d-f2a9ac03e7ec | improving-short-video-speech-recognition | 2210.15876 | null | https://arxiv.org/abs/2210.15876v2 | https://arxiv.org/pdf/2210.15876v2.pdf | Random Utterance Concatenation Based Data Augmentation for Improving Short-video Speech Recognition | One of limitations in end-to-end automatic speech recognition (ASR) framework is its performance would be compromised if train-test utterance lengths are mismatched. In this paper, we propose an on-the-fly random utterance concatenation (RUC) based data augmentation method to alleviate train-test utterance length misma... | ['Zejun Ma', 'Lu Lu', 'Tze Yuang Chong', 'Yerbolat Khassanov', 'Van Tung Pham', 'HaiHua Xu', 'Tao Han', 'Yist Y. Lin', 'Yi He'] | 2022-10-28 | null | null | null | null | ['activity-detection'] | ['computer-vision'] | [ 4.44625318e-01 1.38679489e-01 6.26908289e-03 -5.29107928e-01
-1.40386832e+00 -6.82213545e-01 5.50392091e-01 -3.40113372e-01
-4.53597248e-01 5.67817450e-01 4.39152092e-01 -7.62578785e-01
5.48993647e-01 -2.96835694e-03 -3.69050115e-01 -4.81012940e-01
1.93998173e-01 1.02370031e-01 -8.14208910e-02 -1.52866721... | [14.499856948852539, 6.723163604736328] |
7a90e737-e2fa-4994-b5b9-da3e46b4bd1c | md-manifold-a-medical-distance-based | 2305.00553 | null | https://arxiv.org/abs/2305.00553v1 | https://arxiv.org/pdf/2305.00553v1.pdf | MD-Manifold: A Medical-Distance-Based Representation Learning Approach for Medical Concept and Patient Representation | Effectively representing medical concepts and patients is important for healthcare analytical applications. Representing medical concepts for healthcare analytical tasks requires incorporating medical domain knowledge and prior information from patient description data. Current methods, such as feature engineering and ... | ['Wenli Zhang', 'Qing Li', 'Shaodong Wang'] | 2023-04-30 | null | null | null | null | ['feature-engineering'] | ['methodology'] | [ 9.54176858e-02 3.28634143e-01 -4.88481730e-01 -4.54289615e-01
-6.08845294e-01 -1.14143424e-01 3.63907844e-01 1.21289337e+00
4.49969321e-02 3.87100786e-01 8.19265723e-01 -4.77025688e-01
-7.32712030e-01 -1.02536166e+00 1.38078071e-02 -4.61504072e-01
-3.85054827e-01 8.46696615e-01 -7.50293672e-01 -2.49790147... | [7.871105194091797, 6.758248329162598] |
89a67385-19e2-4524-83a6-0411dd3c2cc9 | controlling-extra-textual-attributes-about | 2205.04747 | null | https://arxiv.org/abs/2205.04747v2 | https://arxiv.org/pdf/2205.04747v2.pdf | Controlling Extra-Textual Attributes about Dialogue Participants -- A Case Study of English-to-Polish Neural Machine Translation | Unlike English, morphologically rich languages can reveal characteristics of speakers or their conversational partners, such as gender and number, via pronouns, morphological endings of words and syntax. When translating from English to such languages, a machine translation model needs to opt for a certain interpretati... | ['Carolina Scarton', 'Loïc Barrault', 'Sebastian T. Vincent'] | 2022-05-10 | null | null | null | null | ['morphological-analysis'] | ['natural-language-processing'] | [ 2.85874099e-01 2.93549359e-01 -3.25900048e-01 -6.37846112e-01
-1.27272177e+00 -9.69079375e-01 1.07248378e+00 4.04914320e-02
-5.64117074e-01 1.08649325e+00 5.46461701e-01 -4.42615926e-01
1.86007485e-01 -6.29035890e-01 -2.48294368e-01 -4.06752497e-01
6.06895268e-01 1.41833806e+00 -2.27313757e-01 -5.90704620... | [11.429649353027344, 10.188562393188477] |
2651403a-fb39-441e-902c-e91f7d017ca7 | ad-nev-a-scalable-multi-level-neuroevolution | 2305.16497 | null | https://arxiv.org/abs/2305.16497v1 | https://arxiv.org/pdf/2305.16497v1.pdf | AD-NEV: A Scalable Multi-level Neuroevolution Framework for Multivariate Anomaly Detection | Anomaly detection tools and methods present a key capability in modern cyberphysical and failure prediction systems. Despite the fast-paced development in deep learning architectures for anomaly detection, model optimization for a given dataset is a cumbersome and time consuming process. Neuroevolution could be an effe... | ['Roberto Corizzo', 'Kamil Faber', 'Dominik Zurek', 'Marcin Pietron'] | 2023-05-25 | null | null | null | null | ['time-series-anomaly-detection'] | ['time-series'] | [-1.27337929e-02 -5.70422053e-01 4.27620292e-01 -5.54904155e-02
-1.01119377e-01 -3.02122414e-01 3.35488975e-01 3.11793655e-01
-4.01149422e-01 5.25582433e-01 -5.67900300e-01 -3.30921024e-01
-4.38348711e-01 -7.83159971e-01 -3.79223287e-01 -9.37841356e-01
-3.26155245e-01 6.32538378e-01 1.78443447e-01 -4.20812041... | [7.6291584968566895, 2.51727032661438] |
55cd2b45-7353-45b9-8bba-c7544ce92223 | architecturing-binarized-neural-networks-for | 2303.15005 | null | https://arxiv.org/abs/2303.15005v1 | https://arxiv.org/pdf/2303.15005v1.pdf | Architecturing Binarized Neural Networks for Traffic Sign Recognition | Traffic signs support road safety and managing the flow of traffic, hence are an integral part of any vision system for autonomous driving. While the use of deep learning is well-known in traffic signs classification due to the high accuracy results obtained using convolutional neural networks (CNNs) (state of the art ... | ['Mădălina Eraşcu', 'Andreea Postovan'] | 2023-03-27 | null | null | null | null | ['traffic-sign-recognition'] | ['computer-vision'] | [ 1.64724495e-02 7.62906224e-02 -1.60232589e-01 -4.58994895e-01
1.45441275e-02 -2.31483430e-01 7.16594040e-01 -3.96266818e-01
-9.10709083e-01 6.89674795e-01 -4.63118345e-01 -7.85570741e-01
-2.21514210e-01 -8.70331705e-01 -7.16862142e-01 -7.30316460e-01
9.29705426e-02 3.02493908e-02 5.35639882e-01 -3.39042515... | [7.996129035949707, -0.7704320549964905] |
782264f0-aa81-436d-8371-df1c55c9533a | networked-restless-bandits-with-positive | 2212.05144 | null | https://arxiv.org/abs/2212.05144v1 | https://arxiv.org/pdf/2212.05144v1.pdf | Networked Restless Bandits with Positive Externalities | Restless multi-armed bandits are often used to model budget-constrained resource allocation tasks where receipt of the resource is associated with an increased probability of a favorable state transition. Prior work assumes that individual arms only benefit if they receive the resource directly. However, many allocatio... | ['John P. Dickerson', 'Christine Herlihy'] | 2022-12-09 | null | null | null | null | ['multi-armed-bandits'] | ['miscellaneous'] | [ 1.15407921e-01 2.77935505e-01 -1.40252864e+00 2.21326083e-01
-3.02132040e-01 -7.86686957e-01 5.07348001e-01 -9.98731144e-03
-2.54003227e-01 1.36921191e+00 2.94213206e-01 -6.80165291e-01
-8.22633088e-01 -9.89054739e-01 -6.72061503e-01 -6.20350838e-01
-4.47462589e-01 1.12821913e+00 -1.63857147e-01 7.59271830... | [4.487096309661865, 3.261888265609741] |
1bfbfd7a-ece2-4eb2-b564-b4de0431e253 | persian-keyphrase-generation-using-sequence | 2009.12271 | null | https://arxiv.org/abs/2009.12271v1 | https://arxiv.org/pdf/2009.12271v1.pdf | Persian Keyphrase Generation Using Sequence-to-Sequence Models | Keyphrases are a very short summary of an input text and provide the main subjects discussed in the text. Keyphrase extraction is a useful upstream task and can be used in various natural language processing problems, for example, text summarization and information retrieval, to name a few. However, not all the keyphra... | ['Ehsan Doostmohammadi', 'Mohammad Hadi Bokaei', 'Hossein Sameti'] | 2020-09-25 | null | null | null | null | ['keyphrase-generation'] | ['natural-language-processing'] | [ 5.15411377e-01 1.49019256e-01 -5.04799366e-01 2.12182701e-01
-8.97441208e-01 -7.11890936e-01 1.08958840e+00 1.04740024e+00
-4.96552855e-01 1.26928771e+00 9.63956416e-01 -2.13007271e-01
-1.15174472e-01 -7.36052990e-01 -6.87853634e-01 -5.67420423e-01
1.30048901e-01 4.09162581e-01 2.77835995e-01 -2.97659159... | [12.304964065551758, 8.994425773620605] |
3939f942-774a-49f1-af57-44df44d7aa24 | a-corpus-for-commonsense-inference-in-story | null | null | https://aclanthology.org/2022.lrec-1.375 | https://aclanthology.org/2022.lrec-1.375.pdf | A Corpus for Commonsense Inference in Story Cloze Test | The Story Cloze Test (SCT) is designed for training and evaluating machine learning algorithms for narrative understanding and inferences. The SOTA models can achieve over 90% accuracy on predicting the last sentence. However, it has been shown that high accuracy can be achieved by merely using surface-level features. ... | ['Mei Si', 'Julian Lioanag', 'Ethan Joseph', 'Bingsheng Yao'] | null | null | null | null | lrec-2022-6 | ['cloze-test'] | ['natural-language-processing'] | [ 1.88987941e-01 2.01677829e-01 -3.03536594e-01 -4.75889087e-01
-1.35131681e+00 -7.65596390e-01 9.10281837e-01 1.44241691e-01
-1.98131874e-01 8.74692500e-01 9.21240091e-01 -3.31243038e-01
-1.13707289e-01 -7.83228040e-01 -5.38490653e-01 -2.04528138e-01
4.29477155e-01 6.99940383e-01 2.00709462e-01 -2.73756534... | [11.14405345916748, 8.832623481750488] |
6e801b58-dcdc-444e-93ab-6ce8949e6ecb | transcmd-cross-modal-decoder-equipped-with | 2112.02363 | null | https://arxiv.org/abs/2112.02363v3 | https://arxiv.org/pdf/2112.02363v3.pdf | CAVER: Cross-Modal View-Mixed Transformer for Bi-Modal Salient Object Detection | Most of the existing bi-modal (RGB-D and RGB-T) salient object detection methods utilize the convolution operation and construct complex interweave fusion structures to achieve cross-modal information integration. The inherent local connectivity of the convolution operation constrains the performance of the convolution... | ['Huchuan Lu', 'Lihe Zhang', 'Xiaoqi Zhao', 'Youwei Pang'] | 2021-12-04 | null | null | null | null | ['rgb-d-salient-object-detection'] | ['computer-vision'] | [ 3.02047372e-01 -1.02888420e-01 -4.59447987e-02 -4.36497092e-01
-7.93752730e-01 -3.76912355e-01 5.90245605e-01 4.80380282e-02
-6.60918653e-01 3.41873080e-01 1.82836562e-01 -3.14174205e-01
8.99265707e-03 -6.81674540e-01 -9.47065234e-01 -7.65861869e-01
2.46595189e-01 -6.42348826e-02 4.83930379e-01 -5.20205200... | [9.701240539550781, -0.7358705401420593] |
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