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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
101c8f1c-7206-4f02-a7f3-89af9d535cb8 | human-centered-trust-framework-an-hci | 2305.03306 | null | https://arxiv.org/abs/2305.03306v2 | https://arxiv.org/pdf/2305.03306v2.pdf | Human-centered trust framework: An HCI perspective | The rationale of this work is based on the current user trust discourse of Artificial Intelligence (AI). We aim to produce novel HCI approaches that use trust as a facilitator for the uptake (or appropriation) of current technologies. We propose a framework (HCTFrame) to guide non-experts to unlock the full potential o... | ['David Lamas', 'Paulo Martins', 'Jose Cravino', 'Sonia Sousa'] | 2023-05-05 | null | null | null | null | ['misconceptions'] | ['miscellaneous'] | [-1.25434905e-01 6.90044701e-01 -1.53880352e-02 -5.63958704e-01
2.59717077e-01 -3.09442759e-01 5.03979504e-01 5.53751826e-01
-2.07896337e-01 1.60657942e-01 6.62865460e-01 -7.92092144e-01
-1.41755827e-02 -4.27519642e-02 -2.59299636e-01 -1.85508981e-01
5.24399996e-01 -1.04707554e-01 -2.33473077e-01 -4.65810180... | [9.085637092590332, 6.321040630340576] |
35c78124-4d50-4447-b859-2b2096904034 | improve-text-classification-accuracy-with | 2212.07649 | null | https://arxiv.org/abs/2212.07649v1 | https://arxiv.org/pdf/2212.07649v1.pdf | Improve Text Classification Accuracy with Intent Information | Text classification, a core component of task-oriented dialogue systems, attracts continuous research from both the research and industry community, and has resulted in tremendous progress. However, existing method does not consider the use of label information, which may weaken the performance of text classification s... | ['Yifeng Xie'] | 2022-12-15 | null | null | null | null | ['task-oriented-dialogue-systems'] | ['natural-language-processing'] | [ 3.04074407e-01 -5.50889075e-02 -4.29745823e-01 -5.34034431e-01
-7.04981238e-02 -4.29512560e-01 8.64478767e-01 5.45988739e-01
-6.94454730e-01 6.02025628e-01 4.31818664e-01 -4.26851571e-01
2.65934944e-01 -6.77323639e-01 3.98337692e-01 -6.56876266e-01
5.07958651e-01 9.17044580e-02 3.05308729e-01 -2.88396627... | [10.559078216552734, 7.547170162200928] |
21b8b19b-be37-4ad5-ab74-c0d1dd5375e7 | automatic-discovery-and-optimization-of-parts | 1412.6598 | null | http://arxiv.org/abs/1412.6598v2 | http://arxiv.org/pdf/1412.6598v2.pdf | Automatic Discovery and Optimization of Parts for Image Classification | Part-based representations have been shown to be very useful for image
classification. Learning part-based models is often viewed as a two-stage
problem. First, a collection of informative parts is discovered, using
heuristics that promote part distinctiveness and diversity, and then
classifiers are trained on the vect... | ['Andrea Vedaldi', 'Pedro Felzenszwalb', 'Sobhan Naderi Parizi', 'Andrew Zisserman'] | 2014-12-20 | null | null | null | null | ['l2-regularization'] | ['methodology'] | [ 4.21993345e-01 5.32132626e-01 -3.68904620e-01 -4.73963350e-01
-1.04402030e+00 -5.37814200e-01 6.11412466e-01 9.38185528e-02
6.79349061e-03 7.44197369e-01 2.86405444e-01 4.58920509e-01
-5.00979982e-02 -6.44986153e-01 -1.03104758e+00 -9.35203731e-01
-1.48723766e-01 7.37805009e-01 2.39217535e-01 -1.07866175... | [9.494291305541992, 1.0529749393463135] |
6fb2eaa3-a7f4-4abf-9ce0-de013d2700f4 | hiera-a-hierarchical-vision-transformer | 2306.00989 | null | https://arxiv.org/abs/2306.00989v1 | https://arxiv.org/pdf/2306.00989v1.pdf | Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles | Modern hierarchical vision transformers have added several vision-specific components in the pursuit of supervised classification performance. While these components lead to effective accuracies and attractive FLOP counts, the added complexity actually makes these transformers slower than their vanilla ViT counterparts... | ['Christoph Feichtenhofer', 'Yanghao Li', 'Jitendra Malik', 'Judy Hoffman', 'Omid Poursaeed', 'Arkabandhu Chowdhury', 'Vaibhav Aggarwal', 'Po-Yao Huang', 'Haoqi Fan', 'Chen Wei', 'Daniel Bolya', 'Yuan-Ting Hu', 'Chaitanya Ryali'] | 2023-06-01 | null | null | null | null | ['video-recognition', 'action-classification', 'action-recognition-in-videos', 'action-recognition-in-videos-2'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 1.44689560e-01 7.24651515e-02 -3.36445756e-02 -4.39155161e-01
-8.81944120e-01 -5.55603981e-01 7.22120583e-01 -1.87555954e-01
-2.85428166e-01 1.72411457e-01 6.20268658e-02 -7.83322573e-01
2.47408360e-01 -5.08017242e-01 -8.51466894e-01 -5.23336172e-01
3.46610487e-01 4.92143631e-01 6.15846395e-01 4.07100394... | [9.534127235412598, 1.493985891342163] |
55c853fc-dabe-4091-afa0-9dcd5df18afe | unsupervised-human-pose-estimation-through | 2105.04154 | null | https://arxiv.org/abs/2105.04154v1 | https://arxiv.org/pdf/2105.04154v1.pdf | Unsupervised Human Pose Estimation through Transforming Shape Templates | Human pose estimation is a major computer vision problem with applications ranging from augmented reality and video capture to surveillance and movement tracking. In the medical context, the latter may be an important biomarker for neurological impairments in infants. Whilst many methods exist, their application has be... | ['Bernhard Kainz', 'Tomoki Arichi', 'Anna Lukens', 'Simon Ellershaw', 'Athanasios Vlontzos', 'Luca Schmidtke'] | 2021-05-10 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Schmidtke_Unsupervised_Human_Pose_Estimation_Through_Transforming_Shape_Templates_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Schmidtke_Unsupervised_Human_Pose_Estimation_Through_Transforming_Shape_Templates_CVPR_2021_paper.pdf | cvpr-2021-1 | ['template-matching'] | ['computer-vision'] | [ 4.22765166e-01 4.23198044e-01 -8.25799257e-02 -5.30714095e-01
-4.97378707e-01 -5.90281546e-01 4.60224450e-01 1.12512782e-01
-5.47572851e-01 5.36014199e-01 3.04951578e-01 3.00449193e-01
-1.92172959e-01 -2.44770065e-01 -7.48080969e-01 -3.47103149e-01
-1.46898374e-01 7.56102145e-01 1.50929049e-01 5.87675050... | [7.037136077880859, -1.0859593152999878] |
809b36bc-0167-4ea4-82a8-74768058de6d | unsupervised-video-summarization-via | null | null | https://link.springer.com/chapter/10.1007/978-3-030-37731-1_40 | https://link.springer.com/chapter/10.1007/978-3-030-37731-1_40 | Unsupervised Video Summarization via Attention-Driven Adversarial Learning | This paper presents a new video summarization approach that integrates an attention mechanism to identify the significant parts of the video, and is trained unsupervisingly via generative adversarial learning. Starting from the SUM-GAN model, we first develop an improved version of it (called SUM-GAN-sl) that has a sig... | ['Ioannis Patras', 'Vasileios Mezaris', 'Alexandros I. Metsai', 'Eleni Adamantidou', 'Evlampios Apostolidis'] | 2019-12-24 | null | null | null | multimedia-modeling-mmm-2019-12 | ['unsupervised-video-summarization'] | ['computer-vision'] | [ 4.13168073e-01 7.14968860e-01 3.04711014e-01 6.90303836e-03
-1.08499885e+00 -4.32482928e-01 7.09688127e-01 -5.95047534e-01
-1.34194970e-01 7.46168852e-01 5.60031474e-01 -1.54879823e-01
4.63698864e-01 -6.72110498e-01 -1.19314432e+00 -7.74845183e-01
2.13462934e-01 4.31755543e-01 1.78549588e-01 -1.69270664... | [10.590965270996094, 0.2715260982513428] |
d9676bcf-f1bb-4544-85a1-d11bfcef41d2 | data-augmentation-and-squeeze-and-excitation | 2206.12059 | null | https://arxiv.org/abs/2206.12059v1 | https://arxiv.org/pdf/2206.12059v1.pdf | Data Augmentation and Squeeze-and-Excitation Network on Multiple Dimension for Sound Event Localization and Detection in Real Scenes | Performance of sound event localization and detection (SELD) in real scenes is limited by small size of SELD dataset, due to difficulty in obtaining sufficient amount of realistic multi-channel audio data recordings with accurate label. We used two main strategies to solve problems arising from the small real SELD data... | ['Yong-Hwa Park', 'Seung-Deok Choi', 'Deokki Min', 'Seong-Hu Kim', 'Hyeonuk Nam', 'Byeong-Yun Ko'] | 2022-06-24 | null | null | null | null | ['sound-event-localization-and-detection'] | ['audio'] | [ 3.31769735e-01 -2.37468213e-01 5.12745738e-01 -1.08265102e-01
-1.23416376e+00 -5.71361899e-01 3.18163544e-01 1.67592540e-01
-3.45916718e-01 8.64666522e-01 3.29892069e-01 3.54610793e-02
1.68409869e-02 -4.17145044e-01 -4.92341518e-01 -5.11942744e-01
-3.47129852e-01 -2.71558940e-01 4.98695165e-01 2.71827243... | [15.198298454284668, 5.236056804656982] |
c336c5ca-f5b8-4594-a98e-7b05a92eec38 | fs-net-fast-shape-based-network-for-category | 2103.07054 | null | https://arxiv.org/abs/2103.07054v2 | https://arxiv.org/pdf/2103.07054v2.pdf | FS-Net: Fast Shape-based Network for Category-Level 6D Object Pose Estimation with Decoupled Rotation Mechanism | In this paper, we focus on category-level 6D pose and size estimation from monocular RGB-D image. Previous methods suffer from inefficient category-level pose feature extraction which leads to low accuracy and inference speed. To tackle this problem, we propose a fast shape-based network (FS-Net) with efficient categor... | ['Ales Leonardis', 'Linlin Shen', 'Jinming Duan', 'Hyung Jin Chang', 'Xi Jia', 'Wei Chen'] | 2021-03-12 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Chen_FS-Net_Fast_Shape-Based_Network_for_Category-Level_6D_Object_Pose_Estimation_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Chen_FS-Net_Fast_Shape-Based_Network_for_Category-Level_6D_Object_Pose_Estimation_CVPR_2021_paper.pdf | cvpr-2021-1 | ['6d-pose-estimation-using-rgbd'] | ['computer-vision'] | [-1.69607297e-01 -1.93345115e-01 -1.07176900e-01 -3.60517859e-01
-4.67926323e-01 -4.46091413e-01 2.62066454e-01 -2.01103404e-01
-3.86706144e-01 2.78416842e-01 -7.52211735e-02 2.49674208e-02
-2.01776368e-03 -8.43813658e-01 -1.22467852e+00 -8.69166315e-01
1.17742650e-01 5.23249149e-01 9.27264467e-02 1.69966444... | [7.456226348876953, -2.747234582901001] |
20360a71-ff6d-4a1f-881a-e19ffd0f9f31 | continuous-emotional-intensity-controllable | 2211.0616 | null | https://arxiv.org/abs/2211.06160v2 | https://arxiv.org/pdf/2211.06160v2.pdf | Semi-supervised learning for continuous emotional intensity controllable speech synthesis with disentangled representations | Recent text-to-speech models have reached the level of generating natural speech similar to what humans say. But there still have limitations in terms of expressiveness. The existing emotional speech synthesis models have shown controllability using interpolated features with scaling parameters in emotional latent spac... | ['Kyogu Lee', 'Yoseob Han', 'Juheon Lee', 'Yoori Oh'] | 2022-11-11 | null | null | null | null | ['emotional-speech-synthesis'] | ['speech'] | [-7.44139180e-02 4.59428042e-01 -1.83588654e-01 -3.92080754e-01
-2.33587012e-01 -4.00590092e-01 7.97088802e-01 -3.18218708e-01
4.79390882e-02 8.51313949e-01 6.34222806e-01 3.24077010e-01
-1.70551836e-02 -7.92898893e-01 -2.93894470e-01 -7.82614291e-01
7.41039962e-02 1.53387532e-01 -3.09715271e-01 -4.67380702... | [14.666726112365723, 6.424015998840332] |
77e1deec-7a84-493f-8920-ea3441a94b92 | fine-grained-human-feedback-gives-better | 2306.01693 | null | https://arxiv.org/abs/2306.01693v1 | https://arxiv.org/pdf/2306.01693v1.pdf | Fine-Grained Human Feedback Gives Better Rewards for Language Model Training | Language models (LMs) often exhibit undesirable text generation behaviors, including generating false, toxic, or irrelevant outputs. Reinforcement learning from human feedback (RLHF) - where human preference judgments on LM outputs are transformed into a learning signal - has recently shown promise in addressing these ... | ['Hannaneh Hajishirzi', 'Mari Ostendorf', 'Noah A. Smith', 'Prithviraj Ammanabrolu', 'Alane Suhr', 'Nouha Dziri', 'Weijia Shi', 'Yushi Hu', 'Zeqiu Wu'] | 2023-06-02 | null | null | null | null | ['long-form-question-answering'] | ['natural-language-processing'] | [ 1.48975983e-01 1.23849250e-01 -2.57278562e-01 -4.85831261e-01
-9.49906528e-01 -6.84044838e-01 3.74232769e-01 4.18467611e-01
-4.77662295e-01 9.98093545e-01 4.70630080e-01 -6.84423983e-01
-3.67473103e-02 -7.22949088e-01 -8.17826211e-01 -2.35325798e-01
4.52641457e-01 4.60309654e-01 1.00530498e-01 -4.31679785... | [11.648268699645996, 8.622105598449707] |
3e7ec6e9-9ae8-4a20-aff6-3517a0ae2e38 | evidence-aggregation-for-answer-re-ranking-in | 1711.05116 | null | http://arxiv.org/abs/1711.05116v2 | http://arxiv.org/pdf/1711.05116v2.pdf | Evidence Aggregation for Answer Re-Ranking in Open-Domain Question Answering | A popular recent approach to answering open-domain questions is to first
search for question-related passages and then apply reading comprehension
models to extract answers. Existing methods usually extract answers from single
passages independently. But some questions require a combination of evidence
from across diff... | ['Wei zhang', 'Shiyu Chang', 'Jing Jiang', 'Xiaoxiao Guo', 'Tim Klinger', 'Shuohang Wang', 'Murray Campbell', 'Mo Yu', 'Gerald Tesauro', 'Zhiguo Wang'] | 2017-11-14 | evidence-aggregation-for-answer-re-ranking-in-1 | https://openreview.net/forum?id=rJl3yM-Ab | https://openreview.net/pdf?id=rJl3yM-Ab | iclr-2018-1 | ['triviaqa'] | ['miscellaneous'] | [-2.35124547e-02 2.40920499e-01 1.36425063e-01 -4.24877614e-01
-1.90199900e+00 -1.05885148e+00 5.39613485e-01 5.24516284e-01
-4.78203356e-01 1.14087868e+00 6.40059829e-01 -5.33999562e-01
-4.11574602e-01 -9.88526702e-01 -6.56509221e-01 7.22540170e-02
6.39579296e-01 1.16867232e+00 1.11490107e+00 -8.38905275... | [11.330344200134277, 8.001605033874512] |
1809136a-629e-42eb-b0df-17f9566d2a18 | toward-imitating-visual-attention-of-experts | 1903.0632 | null | http://arxiv.org/abs/1903.06320v1 | http://arxiv.org/pdf/1903.06320v1.pdf | Toward Imitating Visual Attention of Experts in Software Development Tasks | Expert programmers' eye-movements during source code reading are valuable
sources that are considered to be associated with their domain expertise. We
advocate a vision of new intelligent systems incorporating expertise of experts
for software development tasks, such as issue localization, comment generation,
and code ... | ['Hideaki Hata', 'Nishanth Koganti', 'Yoshiharu Ikutani', 'Kenichi Matsumoto', 'Takatomi Kubo'] | 2019-03-15 | null | null | null | null | ['comment-generation'] | ['natural-language-processing'] | [ 5.94443493e-02 7.25623071e-01 4.51480635e-02 -2.55227029e-01
-2.26385161e-01 -3.90321910e-01 4.71918523e-01 -1.17553256e-01
-1.23132899e-01 3.12971145e-01 -6.64890036e-02 -4.21955526e-01
3.23731601e-01 -2.11177379e-01 -9.09751832e-01 -1.37886703e-01
3.43044609e-01 -1.28715739e-01 1.86348811e-01 -1.78157732... | [7.747600078582764, 7.842690944671631] |
4eca75ef-211d-4970-9a20-0b69f2f1bd72 | perspective-plane-program-induction-from-a-1 | 2006.14708 | null | https://arxiv.org/abs/2006.14708v1 | https://arxiv.org/pdf/2006.14708v1.pdf | Perspective Plane Program Induction from a Single Image | We study the inverse graphics problem of inferring a holistic representation for natural images. Given an input image, our goal is to induce a neuro-symbolic, program-like representation that jointly models camera poses, object locations, and global scene structures. Such high-level, holistic scene representations furt... | ['Joshua B. Tenenbaum', 'Xiuming Zhang', 'Yikai Li', 'Jiajun Wu', 'William T. Freeman', 'Jiayuan Mao'] | 2020-06-25 | perspective-plane-program-induction-from-a | http://openaccess.thecvf.com/content_CVPR_2020/html/Li_Perspective_Plane_Program_Induction_From_a_Single_Image_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Li_Perspective_Plane_Program_Induction_From_a_Single_Image_CVPR_2020_paper.pdf | cvpr-2020-6 | ['program-induction', 'shape-from-texture'] | ['computer-code', 'computer-vision'] | [ 5.76882541e-01 3.21601965e-02 -1.88462034e-01 -5.10801017e-01
-6.30360723e-01 -7.08007514e-01 5.75591445e-01 6.10465221e-02
-4.32826765e-02 5.83182387e-02 4.49213356e-01 -1.40088573e-01
-3.09602432e-02 -6.07090652e-01 -1.57153273e+00 -3.99315357e-01
5.76184630e-01 4.61679369e-01 -7.29777012e-03 5.25397249... | [9.065896034240723, -3.0427165031433105] |
c8e19d8d-5d2e-474a-8119-ba4c41963d71 | neuraldome-a-neural-modeling-pipeline-on | 2212.07626 | null | https://arxiv.org/abs/2212.07626v1 | https://arxiv.org/pdf/2212.07626v1.pdf | NeuralDome: A Neural Modeling Pipeline on Multi-View Human-Object Interactions | Humans constantly interact with objects in daily life tasks. Capturing such processes and subsequently conducting visual inferences from a fixed viewpoint suffers from occlusions, shape and texture ambiguities, motions, etc. To mitigate the problem, it is essential to build a training dataset that captures free-viewpoi... | ['Jingya Wang', 'Lan Xu', 'Jingyi Yu', 'Ye Shi', 'Qianyang Wu', 'Xinru Xu', 'Hongdi Yang', 'Haimin Luo', 'Juze Zhang'] | 2022-12-15 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhang_NeuralDome_A_Neural_Modeling_Pipeline_on_Multi-View_Human-Object_Interactions_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhang_NeuralDome_A_Neural_Modeling_Pipeline_on_Multi-View_Human-Object_Interactions_CVPR_2023_paper.pdf | cvpr-2023-1 | ['human-object-interaction-detection'] | ['computer-vision'] | [-5.20132668e-02 -3.06540072e-01 4.01881754e-01 -5.22343278e-01
-1.19910985e-01 -3.33400428e-01 4.44663733e-01 -5.50144434e-01
1.09108523e-01 3.54957819e-01 8.48801881e-02 5.11307754e-02
1.93086162e-01 -6.03380680e-01 -6.79248512e-01 -2.77513832e-01
5.66619337e-02 7.62334347e-01 5.10160685e-01 3.03425714... | [6.959239482879639, -1.061302661895752] |
48723ebb-3c16-480a-9179-054632bd7e1d | voar-a-visual-and-integrated-ontology | null | null | https://aclanthology.org/L14-1658 | https://aclanthology.org/L14-1658.pdf | VOAR: A Visual and Integrated Ontology Alignment Environment | Ontology alignment is a key process for enabling interoperability between ontology-based systems in the Linked Open Data age. From two input ontologies, this process generates an alignment (set of correspondences) between them. In this paper we present VOAR, a new web-based environment for ontology alignment visualizat... | ['Renata Vieira', 'Cassia Trojahn', 'Bernardo Severo'] | 2014-05-01 | null | null | null | lrec-2014-5 | ['ontology-matching'] | ['knowledge-base'] | [ 9.01630521e-02 1.60487592e-01 1.09933138e-01 -3.38158876e-01
-2.01025680e-01 -8.38681281e-01 6.67652309e-01 1.09237635e+00
-3.61093223e-01 3.01668614e-01 7.63436481e-02 -1.93138048e-01
-7.27625132e-01 -1.27351236e+00 -1.37964353e-01 -1.09358355e-01
-6.60216361e-02 8.34429979e-01 5.76505899e-01 -6.05547130... | [9.198094367980957, 8.027632713317871] |
166b05ec-3905-43cd-adbb-548386f266b7 | nuwa-visual-synthesis-pre-training-for-neural | 2111.12417 | null | https://arxiv.org/abs/2111.12417v1 | https://arxiv.org/pdf/2111.12417v1.pdf | NÜWA: Visual Synthesis Pre-training for Neural visUal World creAtion | This paper presents a unified multimodal pre-trained model called N\"UWA that can generate new or manipulate existing visual data (i.e., images and videos) for various visual synthesis tasks. To cover language, image, and video at the same time for different scenarios, a 3D transformer encoder-decoder framework is desi... | ['Nan Duan', 'Daxin Jiang', 'Yuejian Fang', 'Fan Yang', 'Lei Ji', 'Jian Liang', 'Chenfei Wu'] | 2021-11-24 | null | null | null | null | ['video-prediction', 'text-to-video-generation'] | ['computer-vision', 'natural-language-processing'] | [ 2.25329652e-01 5.43164611e-02 -2.73108035e-01 -9.96461138e-02
-8.27347755e-01 -4.79112208e-01 8.62951517e-01 -6.18830204e-01
2.68508047e-02 6.18339300e-01 5.32744288e-01 -1.69923052e-01
7.04270661e-01 -6.23076737e-01 -1.12162292e+00 -5.20782053e-01
4.86237019e-01 3.31141293e-01 7.13464692e-02 -1.28484428... | [10.851738929748535, -0.37665703892707825] |
897b0ba0-9776-45e6-9bd5-d6444f91abc7 | true-global-optimality-of-the-pressure-vessel | 1403.7793 | null | http://arxiv.org/abs/1403.7793v1 | http://arxiv.org/pdf/1403.7793v1.pdf | True Global Optimality of the Pressure Vessel Design Problem: A Benchmark for Bio-Inspired Optimisation Algorithms | The pressure vessel design problem is a well-known design benchmark for
validating bio-inspired optimization algorithms. However, its global optimality
is not clear and there has been no mathematical proof put forward. In this
paper, a detailed mathematical analysis of this problem is provided that proves
that 6059.714... | ['Xin-She Yang', 'Mehmet Karamanoglu', 'Nawaz Khan', 'Christian Huyck'] | 2014-03-30 | null | null | null | null | ['cantilever-beam'] | ['miscellaneous'] | [ 3.38717327e-02 1.57980267e-02 -2.31109574e-01 -1.84411518e-02
-1.29480034e-01 -3.58011663e-01 -3.54181916e-01 -1.63452715e-01
-2.56732583e-01 1.19875026e+00 -2.60396868e-01 -3.67811561e-01
-6.41456425e-01 -5.93153059e-01 -5.26671588e-01 -1.08945966e+00
-1.89848080e-01 -8.48076791e-02 -1.27085134e-01 -4.41150278... | [5.8980021476745605, 3.4167845249176025] |
9bb19c0c-5a1c-4e3b-b8d4-15043094357a | learning-from-small-data-through-sampling-an | 2003.14297 | null | https://arxiv.org/abs/2003.14297v5 | https://arxiv.org/pdf/2003.14297v5.pdf | Generative Latent Implicit Conditional Optimization when Learning from Small Sample | We revisit the long-standing problem of learning from a small sample, to which end we propose a novel method called GLICO (Generative Latent Implicit Conditional Optimization). GLICO learns a mapping from the training examples to a latent space and a generator that generates images from vectors in the latent space. Unl... | ['Idan Azuri', 'Daphna Weinshall'] | 2020-03-31 | null | null | null | null | ['small-data'] | ['computer-vision'] | [ 3.20251077e-01 4.65463847e-01 -3.33419859e-01 -2.86424130e-01
-9.00577962e-01 -5.60925305e-01 7.74203539e-01 -1.74966797e-01
-3.58737558e-01 1.00540352e+00 2.05105934e-02 5.50682545e-02
3.00962448e-01 -8.48578095e-01 -8.76812577e-01 -9.64817226e-01
2.54705220e-01 7.02689588e-01 -8.51129293e-02 3.40887904... | [11.475314140319824, -0.0660107210278511] |
7ae8e101-f2d8-4b75-9196-45136b514f66 | hfn-heterogeneous-feature-network-for | 2211.00277 | null | https://arxiv.org/abs/2211.00277v2 | https://arxiv.org/pdf/2211.00277v2.pdf | HFN: Heterogeneous Feature Network for Multivariate Time Series Anomaly Detection | Network or physical attacks on industrial equipment or computer systems may cause massive losses. Therefore, a quick and accurate anomaly detection (AD) based on monitoring data, especially the multivariate time-series (MTS) data, is of great significance. As the key step of anomaly detection for MTS data, learning the... | ['Xiandong Ma', 'Qiucheng Miao', 'Canqun Yang', 'Chengkun Wu', 'Jun Zhan'] | 2022-11-01 | null | null | null | null | ['supervised-anomaly-detection', 'semi-supervised-anomaly-detection', 'graph-structure-learning'] | ['computer-vision', 'computer-vision', 'graphs'] | [ 1.56567529e-01 -1.46824941e-01 -4.22939323e-02 -1.31646439e-01
-2.46668532e-01 -1.64085105e-01 2.04497427e-01 7.30736911e-01
3.45727772e-01 3.78800690e-01 -2.94872373e-02 -3.74895155e-01
-4.61920947e-01 -9.96680439e-01 -5.46393692e-01 -8.69875073e-01
-5.42294562e-01 1.16636202e-01 1.33823186e-01 -1.94101378... | [7.278781890869141, 2.681631326675415] |
ec76d10b-8a0d-4770-a4c2-02a5dd52bd75 | neural-symbolic-computing-an-effective | 1905.06088 | null | https://arxiv.org/abs/1905.06088v1 | https://arxiv.org/pdf/1905.06088v1.pdf | Neural-Symbolic Computing: An Effective Methodology for Principled Integration of Machine Learning and Reasoning | Current advances in Artificial Intelligence and machine learning in general, and deep learning in particular have reached unprecedented impact not only across research communities, but also over popular media channels. However, concerns about interpretability and accountability of AI have been raised by influential thi... | ['Luis C. Lamb', "Artur d'Avila Garcez", 'Marco Gori', 'Son N. Tran', 'Luciano Serafini', 'Michael Spranger'] | 2019-05-15 | null | null | null | null | ['explainable-models'] | ['computer-vision'] | [ 2.11277366e-01 8.22268307e-01 -1.99117079e-01 -4.47931975e-01
1.23327449e-01 -4.37271625e-01 8.87416005e-01 1.23657800e-01
-7.83191025e-02 5.84454179e-01 3.06156665e-01 -5.12162209e-01
-6.50768340e-01 -8.73465478e-01 -6.97353065e-01 -3.62807661e-01
6.83618411e-02 6.54247284e-01 -1.77424535e-01 -4.14892942... | [9.129398345947266, 6.655424118041992] |
9de858cc-dc4c-41de-92ef-183b8cd4a612 | neural-pipeline-for-zero-shot-data-to-text-1 | 2203.16279 | null | https://arxiv.org/abs/2203.16279v1 | https://arxiv.org/pdf/2203.16279v1.pdf | Neural Pipeline for Zero-Shot Data-to-Text Generation | In data-to-text (D2T) generation, training on in-domain data leads to overfitting to the data representation and repeating training data noise. We examine how to avoid finetuning pretrained language models (PLMs) on D2T generation datasets while still taking advantage of surface realization capabilities of PLMs. Inspir... | ['Ondřej Dušek', 'Zdeněk Kasner'] | 2022-03-30 | null | https://aclanthology.org/2022.acl-long.271 | https://aclanthology.org/2022.acl-long.271.pdf | acl-2022-5 | ['data-to-text-generation'] | ['natural-language-processing'] | [ 3.03465456e-01 1.09856021e+00 -1.09908640e-01 -3.07633400e-01
-1.06188726e+00 -3.81144673e-01 1.15473640e+00 3.31571072e-01
-1.23327605e-01 1.06104100e+00 6.93331838e-01 -2.67207742e-01
1.92174613e-01 -1.34233809e+00 -1.26699221e+00 1.79517999e-01
1.77031621e-01 1.10565972e+00 6.36092573e-02 -5.49436271... | [11.419304847717285, 8.777178764343262] |
094b2805-b10e-4ea6-8980-0b08426e8b75 | contrastive-learning-for-sleep-staging-based | 2305.03178 | null | https://arxiv.org/abs/2305.03178v1 | https://arxiv.org/pdf/2305.03178v1.pdf | Contrastive Learning for Sleep Staging based on Inter Subject Correlation | In recent years, multitudes of researches have applied deep learning to automatic sleep stage classification. Whereas actually, these works have paid less attention to the issue of cross-subject in sleep staging. At the same time, emerging neuroscience theories on inter-subject correlations can provide new insights for... | ['Bei Wang', 'Tongxu Zhang'] | 2023-05-05 | null | null | null | null | ['sleep-staging', 'automatic-sleep-stage-classification'] | ['medical', 'medical'] | [-2.16600984e-01 -2.80759513e-01 -5.21851659e-01 -6.35402799e-01
-3.79454404e-01 4.22467962e-02 4.11222637e-01 -4.38232422e-02
-7.89362133e-01 7.56990790e-01 6.94509828e-03 -2.80440296e-03
-3.38478059e-01 -1.19404361e-01 3.73004074e-03 -7.32629180e-01
-3.80747944e-01 3.88510264e-02 4.03988222e-03 -8.13820362... | [13.537264823913574, 3.5275843143463135] |
4fa76e9a-c3fd-42a0-90c8-d70d60772ec1 | isic-2017-skin-lesion-segmentation-using-deep | 1807.09083 | null | http://arxiv.org/abs/1807.09083v1 | http://arxiv.org/pdf/1807.09083v1.pdf | ISIC 2017 Skin Lesion Segmentation Using Deep Encoder-Decoder Network | This paper summarizes our method and validation results for part 1 of the
ISBI Challenge 2018. Our algorithm makes use of deep encoder-decoder network
and novel skin lesion data augmentation to segment the challenge objective.
Besides, we also propose an effective testing strategy by applying multi-model
comparison. | ['Ngoc-Quang Nguyen'] | 2018-07-24 | null | null | null | null | ['skin-lesion-segmentation'] | ['medical'] | [ 6.28981769e-01 1.43298581e-01 -8.77810359e-01 -3.49620581e-01
-1.51193237e+00 -1.10742785e-01 4.16711211e-01 -1.27126977e-01
-6.82946980e-01 8.01613510e-01 1.61862209e-01 -3.66021693e-02
2.92901218e-01 -2.98509330e-01 -7.29725420e-01 -3.56876105e-01
6.89364895e-02 4.34388131e-01 2.81990349e-01 -5.79875968... | [15.663786888122559, -2.950857162475586] |
5d3cde83-7192-416b-9b5a-5b6f6a2cfd68 | bytesing-a-chinese-singing-voice-synthesis | 2004.11012 | null | https://arxiv.org/abs/2004.11012v1 | https://arxiv.org/pdf/2004.11012v1.pdf | ByteSing: A Chinese Singing Voice Synthesis System Using Duration Allocated Encoder-Decoder Acoustic Models and WaveRNN Vocoders | This paper presents ByteSing, a Chinese singing voice synthesis (SVS) system based on duration allocated Tacotron-like acoustic models and WaveRNN neural vocoders. Different from the conventional SVS models, the proposed ByteSing employs Tacotron-like encoder-decoder structures as the acoustic models, in which the CBHG... | ['Yu Gu', 'Yuan Wan', 'Yuxuan Wang', 'Yang Zhang', 'Benlai Tang', 'Zejun Ma', 'Yonghui Rao', 'Xiang Yin', 'Jitong Chen'] | 2020-04-23 | null | null | null | null | ['singing-voice-synthesis'] | ['speech'] | [-2.39140049e-01 -7.19973668e-02 -1.79858521e-01 1.38654366e-01
-1.83470309e-01 -1.57149896e-01 8.48654285e-02 -7.93361664e-01
6.93050250e-02 6.44980907e-01 7.15839684e-01 -2.31545255e-01
7.79773518e-02 -3.76006693e-01 -2.63722092e-01 -5.69281757e-01
3.86233360e-01 -5.04566766e-02 2.74799932e-02 -2.77344376... | [15.529462814331055, 6.193066120147705] |
00d735ba-8cb0-48ff-9074-c0bfdd58c8a9 | structure-plp-slam-efficient-sparse-mapping | 2207.06058 | null | https://arxiv.org/abs/2207.06058v3 | https://arxiv.org/pdf/2207.06058v3.pdf | Structure PLP-SLAM: Efficient Sparse Mapping and Localization using Point, Line and Plane for Monocular, RGB-D and Stereo Cameras | This paper presents a visual SLAM system that uses both points and lines for robust camera localization, and simultaneously performs a piece-wise planar reconstruction (PPR) of the environment to provide a structural map in real-time. One of the biggest challenges in parallel tracking and mapping with a monocular camer... | ['Didier Stricker', 'Alain Pagani', 'Jiaxuan Wang', 'Fangwen Shu'] | 2022-07-13 | null | null | null | null | ['camera-localization'] | ['computer-vision'] | [-3.09044600e-01 -3.63643587e-01 1.78159531e-02 -3.89316708e-01
-5.72127819e-01 -8.86405051e-01 4.66451824e-01 1.46065816e-01
-5.36133349e-01 3.58701974e-01 -1.65155306e-01 -2.15153009e-01
1.33462891e-01 -7.77665317e-01 -9.63433385e-01 -1.51873097e-01
4.79192324e-02 9.76583183e-01 5.26898623e-01 -1.39826670... | [7.3963189125061035, -2.21466064453125] |
bea4973c-2381-4115-8836-55e06974603f | sparse-insar-data-3d-inpainting-for-ground | 2203.02407 | null | https://arxiv.org/abs/2203.02407v1 | https://arxiv.org/pdf/2203.02407v1.pdf | Sparse InSAR Data 3D Inpainting for Ground Deformation Detection Along the Rail Corridor | Monitoring of ground movement close to the rail corridor, such as that associated with landslips caused by ground subsidence and/or uplift, is of great interest for the detection and prevention of possible railway faults. Interferometric synthetic-aperture radar (InSAR) data can be used to measure ground deformation, b... | ['Nantheera Anantrasirichai', 'Alin Achim', 'David Bull', 'Juliet Biggs', 'Odysseas Pappas'] | 2022-03-04 | null | null | null | null | ['3d-inpainting'] | ['computer-vision'] | [ 5.10104060e-01 -3.41908723e-01 1.85041860e-01 -2.42427826e-01
-8.30881596e-01 -2.36489609e-01 3.54294598e-01 1.44625753e-01
-5.40438652e-01 8.88424456e-01 8.20936784e-02 -1.79248694e-02
-5.57590127e-01 -1.31933689e+00 -7.29642868e-01 -9.39035356e-01
-4.92332160e-01 5.48905373e-01 2.51196742e-01 -6.86089396... | [10.015395164489746, -1.953804850578308] |
d90db96d-ebc4-4deb-a1ac-8c8a213b31ee | local-implicit-grid-representations-for-3d | 2003.08981 | null | https://arxiv.org/abs/2003.08981v1 | https://arxiv.org/pdf/2003.08981v1.pdf | Local Implicit Grid Representations for 3D Scenes | Shape priors learned from data are commonly used to reconstruct 3D objects from partial or noisy data. Yet no such shape priors are available for indoor scenes, since typical 3D autoencoders cannot handle their scale, complexity, or diversity. In this paper, we introduce Local Implicit Grid Representations, a new 3D sh... | ['Thomas Funkhouser', 'Matthias Nießner', 'Chiyu Max Jiang', 'Avneesh Sud', 'Jingwei Huang', 'Ameesh Makadia'] | 2020-03-19 | null | null | null | null | ['3d-shape-representation'] | ['computer-vision'] | [ 1.26719266e-01 3.40461761e-01 1.80276394e-01 -2.77920216e-01
-6.73498690e-01 -4.09265369e-01 3.96273971e-01 1.61838140e-02
4.30832595e-01 3.29393566e-01 3.49892974e-01 5.54473773e-02
-7.62171894e-02 -1.22709692e+00 -1.15915668e+00 -7.45261967e-01
6.16027825e-02 7.39302218e-01 1.42008513e-01 7.89193902... | [8.70195484161377, -3.584632396697998] |
f56f94ce-88fa-4dd9-8419-02b950a405dc | adversarial-pretraining-of-self-supervised | 2210.13463 | null | https://arxiv.org/abs/2210.13463v1 | https://arxiv.org/pdf/2210.13463v1.pdf | Adversarial Pretraining of Self-Supervised Deep Networks: Past, Present and Future | In this paper, we review adversarial pretraining of self-supervised deep networks including both convolutional neural networks and vision transformers. Unlike the adversarial training with access to labeled examples, adversarial pretraining is complicated as it only has access to unlabeled examples. To incorporate adve... | ['Mubarak Shah', 'Guo-Jun Qi'] | 2022-10-23 | null | null | null | null | ['miscellaneous'] | ['miscellaneous'] | [ 4.78441983e-01 2.88647741e-01 1.94609433e-01 -4.07016397e-01
-4.87537920e-01 -1.07381761e+00 6.01519644e-01 -3.16655278e-01
-4.91340697e-01 7.90476918e-01 -3.72651778e-02 -4.75092500e-01
2.59841800e-01 -1.01296115e+00 -1.06657827e+00 -8.11118782e-01
-2.25067630e-01 2.15343207e-01 1.36735260e-01 -2.10302576... | [5.572829723358154, 7.92638635635376] |
65410f14-a96b-4054-80a7-507d15b67822 | learning-generative-models-for-active | 2208.08713 | null | https://arxiv.org/abs/2208.08713v2 | https://arxiv.org/pdf/2208.08713v2.pdf | Learning Generative Models for Active Inference using Tensor Networks | Active inference provides a general framework for behavior and learning in autonomous agents. It states that an agent will attempt to minimize its variational free energy, defined in terms of beliefs over observations, internal states and policies. Traditionally, every aspect of a discrete active inference model must b... | ['Bart Dhoedt', 'Tim Verbelen', 'Bram Vanhecke', 'Samuel T. Wauthier'] | 2022-08-18 | null | null | null | null | ['tensor-networks'] | ['methodology'] | [ 1.25061888e-02 2.58949310e-01 3.33541930e-02 -4.93103862e-01
-2.70999581e-01 -6.79200351e-01 1.01855612e+00 6.77254573e-02
-5.01396775e-01 7.28173494e-01 1.76573396e-01 -2.77725190e-01
-1.84673429e-01 -1.15246832e+00 -6.04784727e-01 -9.40950036e-01
-3.30266744e-01 9.79213297e-01 6.90974807e-03 -3.55112702... | [5.7667646408081055, 4.775277614593506] |
5f6b987e-3b36-437d-992b-13a07ab942e2 | dumlp-pin-a-dual-mlp-dot-product-permutation | 2203.04007 | null | https://arxiv.org/abs/2203.04007v2 | https://arxiv.org/pdf/2203.04007v2.pdf | DuMLP-Pin: A Dual-MLP-dot-product Permutation-invariant Network for Set Feature Extraction | Existing permutation-invariant methods can be divided into two categories according to the aggregation scope, i.e. global aggregation and local one. Although the global aggregation methods, e. g., PointNet and Deep Sets, get involved in simpler structures, their performance is poorer than the local aggregation ones lik... | ['Shuo Zhang', 'Huanjun Deng', 'Mingyang Li', 'Zhidong Deng', 'Wenlei Liu', 'Ziyu Zhu', 'Jiajun Fei'] | 2022-03-08 | null | null | null | null | ['point-cloud-classification'] | ['computer-vision'] | [ 1.00407518e-01 -1.23541981e-01 -3.94860059e-02 -3.55881929e-01
-7.84788489e-01 -6.35597944e-01 3.86069626e-01 4.02353853e-02
-1.37699574e-01 7.61445105e-01 -3.88857096e-01 -2.86066115e-01
-5.34170806e-01 -1.11284602e+00 -9.46060896e-01 -9.98151660e-01
-7.00969025e-02 5.80100894e-01 4.38045621e-01 -1.60520211... | [7.932216167449951, -3.4199299812316895] |
7a113e86-794f-49b2-bde6-29befd0b4643 | diffusionstr-diffusion-model-for-scene-text | 2306.16707 | null | https://arxiv.org/abs/2306.16707v1 | https://arxiv.org/pdf/2306.16707v1.pdf | DiffusionSTR: Diffusion Model for Scene Text Recognition | This paper presents Diffusion Model for Scene Text Recognition (DiffusionSTR), an end-to-end text recognition framework using diffusion models for recognizing text in the wild. While existing studies have viewed the scene text recognition task as an image-to-text transformation, we rethought it as a text-text one under... | ['Masato Fujitake'] | 2023-06-29 | null | null | null | null | ['scene-text-recognition'] | ['computer-vision'] | [ 5.15220702e-01 -6.24694049e-01 1.54115081e-01 -4.30140704e-01
-3.77573967e-01 -5.09005845e-01 1.30434263e+00 -2.60071725e-01
-3.72733116e-01 -3.49896044e-01 3.86153638e-01 -3.33030164e-01
2.47133896e-01 -4.51201826e-01 -3.54061544e-01 -7.72542775e-01
6.31677270e-01 5.61842561e-01 4.61417317e-01 1.35243580... | [11.988736152648926, 2.3028056621551514] |
c191d6ed-b9c5-4bce-b784-dd3df7603e1e | quantifying-gender-biases-towards-politicians | 2112.12014 | null | https://arxiv.org/abs/2112.12014v2 | https://arxiv.org/pdf/2112.12014v2.pdf | Quantifying Gender Biases Towards Politicians on Reddit | Despite attempts to increase gender parity in politics, global efforts have struggled to ensure equal female representation. This is likely tied to implicit gender biases against women in authority. In this work, we present a comprehensive study of gender biases that appear in online political discussion. To this end, ... | ['Isabelle Augenstein', 'Karolina Stańczak', 'Sara Marjanovic'] | 2021-12-22 | null | null | null | null | ['gender-bias-detection', 'gender-bias-detection'] | ['miscellaneous', 'natural-language-processing'] | [-1.45225197e-01 5.05383730e-01 -8.57870996e-01 -6.09188974e-01
-5.00567257e-01 -1.14410257e+00 1.26534414e+00 4.86683577e-01
-7.04114676e-01 6.88061178e-01 1.25335503e+00 -6.54259622e-01
8.74237809e-03 -6.33530974e-01 -1.62252918e-01 -5.94717264e-01
6.46199882e-01 4.08600777e-01 -4.90644664e-01 -5.65505028... | [9.033770561218262, 10.009323120117188] |
2f6a8d42-7432-4ec2-bd97-c95aae8cfaec | towards-unsupervised-visual-reasoning-do-off | 2212.10292 | null | https://arxiv.org/abs/2212.10292v1 | https://arxiv.org/pdf/2212.10292v1.pdf | Towards Unsupervised Visual Reasoning: Do Off-The-Shelf Features Know How to Reason? | Recent advances in visual representation learning allowed to build an abundance of powerful off-the-shelf features that are ready-to-use for numerous downstream tasks. This work aims to assess how well these features preserve information about the objects, such as their spatial location, their visual properties and the... | ['David Picard', 'Tomasz Trzciński', 'Tom Monnier', 'Monika Wysoczańska'] | 2022-12-20 | null | null | null | null | ['visual-reasoning', 'visual-reasoning'] | ['computer-vision', 'reasoning'] | [-3.57193276e-02 2.23581731e-01 1.16787516e-01 -4.52197105e-01
-4.79433537e-01 -7.44225800e-01 9.34095383e-01 6.22269571e-01
-6.02204680e-01 2.91672826e-01 4.90920156e-01 -3.03365022e-01
-3.30855936e-01 -7.87628591e-01 -7.64218867e-01 -5.65927625e-01
1.55429110e-01 4.11228597e-01 3.36968005e-01 -4.52332854... | [10.672818183898926, 1.9343249797821045] |
9779c813-0f7b-45bf-8a2c-0dae88278a46 | doubly-reparameterized-importance-weighted | 2206.11352 | null | https://arxiv.org/abs/2206.11352v1 | https://arxiv.org/pdf/2206.11352v1.pdf | Doubly Reparameterized Importance Weighted Structure Learning for Scene Graph Generation | As a structured prediction task, scene graph generation, given an input image, aims to explicitly model objects and their relationships by constructing a visually-grounded scene graph. In the current literature, such task is universally solved via a message passing neural network based mean field variational Bayesian m... | ['Josef Kittler', 'Miroslaw Bober', 'Daqi Liu'] | 2022-06-22 | null | null | null | null | ['scene-graph-generation'] | ['computer-vision'] | [ 5.44115305e-01 4.16920990e-01 2.08611730e-02 -3.02929968e-01
-9.83658016e-01 -8.78381953e-02 9.01180446e-01 6.05472177e-03
-4.76794809e-01 8.93055797e-01 1.48314029e-01 1.28911352e-02
-1.57301471e-01 -7.04775989e-01 -1.06126511e+00 -9.40068126e-01
4.29624975e-01 6.24801159e-01 4.66210842e-02 2.71505892... | [7.173470973968506, 3.690725803375244] |
28130a68-cc7d-46de-ba53-e5a6d8ffc6c1 | quantum-circuit-components-for-cognitive | 2302.03012 | null | https://arxiv.org/abs/2302.03012v3 | https://arxiv.org/pdf/2302.03012v3.pdf | Quantum Circuit Components for Cognitive Decision-Making | This paper demonstrates that some non-classical models of human decision-making can be run successfully as circuits on quantum computers. Since the 1960s, many observed cognitive behaviors have been shown to violate rules based on classical probability and set theory. For example, the order in which questions are posed... | ['Emmanuel Pothos', 'Jyoti Rani', 'Dominic Widdows'] | 2023-02-06 | null | null | null | null | ['decision-making-under-uncertainty', 'decision-making-under-uncertainty'] | ['medical', 'reasoning'] | [ 3.11496884e-01 1.83359385e-01 4.37779188e-01 -2.90517777e-01
-7.32749999e-02 -8.56503010e-01 6.85116529e-01 1.90170974e-01
-5.62094688e-01 5.35315275e-01 8.29910859e-02 -7.75191426e-01
-4.26623762e-01 -1.47116828e+00 -2.87705541e-01 -4.83643591e-01
1.21637680e-01 6.08995199e-01 -2.16995589e-02 -6.80164933... | [5.653796195983887, 4.940537929534912] |
a636a643-9e64-4a82-b48d-2f5aa049c65b | talking-face-generation-by-adversarially | 1807.0786 | null | http://arxiv.org/abs/1807.07860v2 | http://arxiv.org/pdf/1807.07860v2.pdf | Talking Face Generation by Adversarially Disentangled Audio-Visual Representation | Talking face generation aims to synthesize a sequence of face images that
correspond to a clip of speech. This is a challenging task because face
appearance variation and semantics of speech are coupled together in the subtle
movements of the talking face regions. Existing works either construct specific
face appearanc... | ['Yu Liu', 'Ping Luo', 'Hang Zhou', 'Ziwei Liu', 'Xiaogang Wang'] | 2018-07-20 | null | null | null | null | ['talking-face-generation'] | ['computer-vision'] | [ 3.75034779e-01 2.92833060e-01 -1.39355510e-01 -4.12203342e-01
-8.61510217e-01 -6.22171521e-01 8.72114897e-01 -1.04787004e+00
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2.66347468e-01 -3.15435559e-01 -7.31038690e-01 -1.08605874e+00
1.03251569e-01 3.28358203e-01 -2.44469002e-01 -2.04655170... | [13.216524124145508, -0.3931722939014435] |
caaff2d0-c34e-45d3-8c79-ae872caa5476 | a-keypoint-based-global-association-network | 2204.07335 | null | https://arxiv.org/abs/2204.07335v1 | https://arxiv.org/pdf/2204.07335v1.pdf | A Keypoint-based Global Association Network for Lane Detection | Lane detection is a challenging task that requires predicting complex topology shapes of lane lines and distinguishing different types of lanes simultaneously. Earlier works follow a top-down roadmap to regress predefined anchors into various shapes of lane lines, which lacks enough flexibility to fit complex shapes of... | ['Tianzhu Zhang', 'Chen Qian', 'Fei Wang', 'Tianrui Hui', 'Shaofei Huang', 'Yinchao Ma', 'Jinsheng Wang'] | 2022-04-15 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Wang_A_Keypoint-Based_Global_Association_Network_for_Lane_Detection_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Wang_A_Keypoint-Based_Global_Association_Network_for_Lane_Detection_CVPR_2022_paper.pdf | cvpr-2022-1 | ['lane-detection'] | ['computer-vision'] | [-3.73073667e-01 -3.43114197e-01 -4.23373163e-01 -4.33557034e-01
-4.39682722e-01 -6.67372346e-01 4.38589424e-01 -6.93697948e-03
-1.31031945e-01 4.92840677e-01 9.99534577e-02 -5.33027887e-01
-1.65281892e-01 -9.03792679e-01 -6.68137074e-01 -6.18514776e-01
-2.08053246e-01 1.19307794e-01 6.74303710e-01 -4.18257892... | [8.019418716430664, -1.565313696861267] |
e3296d46-65d6-4db6-8c64-ca20f8ffba41 | cut-and-paste-neural-rendering-1 | 2010.05907 | null | https://arxiv.org/abs/2010.05907v2 | https://arxiv.org/pdf/2010.05907v2.pdf | Cut-and-Paste Object Insertion by Enabling Deep Image Prior for Reshading | We show how to insert an object from one image to another and get realistic results in the hard case, where the shading of the inserted object clashes with the shading of the scene. Rendering objects using an illumination model of the scene doesn't work, because doing so requires a geometric and material model of the o... | ['David A. Forsyth', 'Anand Bhattad'] | 2020-10-12 | cut-and-paste-neural-rendering | https://openreview.net/forum?id=IfEkus1dpU | https://openreview.net/pdf?id=IfEkus1dpU | null | ['image-harmonization'] | ['computer-vision'] | [ 5.84774613e-01 1.85499161e-01 9.32195783e-01 -4.13157642e-01
-4.60270435e-01 -4.82488096e-01 5.55307329e-01 -1.11231823e-02
9.38460007e-02 3.97369176e-01 7.53424168e-02 -7.01670274e-02
3.09830934e-01 -7.79615343e-01 -9.86744821e-01 -9.06502008e-01
4.62762207e-01 3.61085415e-01 4.96420532e-01 -3.44310433... | [9.850878715515137, -3.0560662746429443] |
5c84e56b-39d1-49f6-8b5e-8dbd65357a75 | mask-editor-an-image-annotation-tool-for | 1809.06461 | null | http://arxiv.org/abs/1809.06461v1 | http://arxiv.org/pdf/1809.06461v1.pdf | Mask Editor : an Image Annotation Tool for Image Segmentation Tasks | Deep convolutional neural network (DCNN) is the state-of-the-art method for
image segmentation, which is one of key challenging computer vision tasks.
However, DCNN requires a lot of training images with corresponding image masks
to get a good segmentation result. Image annotation software which is easy to
use and allo... | ['Zhiyu Chen', 'Chuanhai Zhang', 'Kurt Loken', 'Zhiyong Xiao', 'Gary Kunkel'] | 2018-09-17 | null | null | null | null | ['image-cropping'] | ['computer-vision'] | [ 5.18120170e-01 -1.09293222e-01 2.02780679e-01 -3.43184739e-01
1.26683384e-01 -6.43195570e-01 2.17316430e-02 -7.70624354e-02
-3.87740225e-01 3.46065253e-01 -6.37647927e-01 -7.31121540e-01
1.52868152e-01 -9.72776949e-01 -6.44300222e-01 -3.35945874e-01
4.10937726e-01 3.88968438e-01 7.50933170e-01 -1.43201351... | [9.624958038330078, -0.041415661573410034] |
abb1bb15-2009-47eb-b434-25546b2970a8 | gans-n-roses-stable-controllable-diverse | 2106.06561 | null | https://arxiv.org/abs/2106.06561v1 | https://arxiv.org/pdf/2106.06561v1.pdf | GANs N' Roses: Stable, Controllable, Diverse Image to Image Translation (works for videos too!) | We show how to learn a map that takes a content code, derived from a face image, and a randomly chosen style code to an anime image. We derive an adversarial loss from our simple and effective definitions of style and content. This adversarial loss guarantees the map is diverse -- a very wide range of anime can be prod... | ['David Forsyth', 'Min Jin Chong'] | 2021-06-11 | null | null | null | null | ['multimodal-generation'] | ['natural-language-processing'] | [ 5.69156289e-01 2.70107746e-01 1.34866595e-01 -5.20929158e-01
-7.74185956e-01 -9.78288770e-01 7.78347611e-01 -8.72743547e-01
-1.46672633e-02 9.27123189e-01 1.75968304e-01 1.39835432e-01
3.74381781e-01 -7.32663155e-01 -1.13194203e+00 -5.79566300e-01
-1.56701375e-02 5.95289052e-01 -2.17147484e-01 -3.35296333... | [11.7262601852417, -0.3665771484375] |
1e748cdd-3ed1-43af-8f9e-3ccbdec037f4 | private-multi-winner-voting-for-machine-1 | 2211.1541 | null | https://arxiv.org/abs/2211.15410v1 | https://arxiv.org/pdf/2211.15410v1.pdf | Private Multi-Winner Voting for Machine Learning | Private multi-winner voting is the task of revealing $k$-hot binary vectors satisfying a bounded differential privacy (DP) guarantee. This task has been understudied in machine learning literature despite its prevalence in many domains such as healthcare. We propose three new DP multi-winner mechanisms: Binary, $\tau$,... | ['Xiao Wang', 'Nicolas Papernot', 'Somesh Jha', 'Muhammad Ahmad Kaleem', 'Ali Shahin Shamsabadi', 'Vinith Menon Suriyakumar', 'Natalie Dullerud', 'Christopher A Choquette-Choo', 'Adam Dziedzic'] | 2022-11-23 | private-multi-winner-voting-for-machine | https://openreview.net/forum?id=JedTK_aOaRa | https://openreview.net/pdf?id=JedTK_aOaRa | null | ['multi-label-learning'] | ['methodology'] | [ 5.10194004e-01 3.36050659e-01 -7.75363803e-01 -8.39341700e-01
-1.48249245e+00 -9.76734221e-01 3.00491899e-01 3.24865401e-01
-7.87821770e-01 9.01753008e-01 1.20337784e-01 -5.39572656e-01
-1.35454506e-01 -4.93337959e-01 -8.00587296e-01 -1.15161264e+00
-1.42278299e-01 4.13080245e-01 -3.55130613e-01 3.97997558... | [5.932338237762451, 6.678913593292236] |
185232b1-1f99-4263-9692-7b474ada2ae8 | weakly-supervised-video-moment-retrieval-from | 1904.03282 | null | https://arxiv.org/abs/1904.03282v2 | https://arxiv.org/pdf/1904.03282v2.pdf | Weakly Supervised Video Moment Retrieval From Text Queries | There have been a few recent methods proposed in text to video moment retrieval using natural language queries, but requiring full supervision during training. However, acquiring a large number of training videos with temporal boundary annotations for each text description is extremely time-consuming and often not scal... | ['Amit K. Roy-Chowdhury', 'Niluthpol Chowdhury Mithun', 'Sujoy Paul'] | 2019-04-05 | weakly-supervised-video-moment-retrieval-from-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Mithun_Weakly_Supervised_Video_Moment_Retrieval_From_Text_Queries_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Mithun_Weakly_Supervised_Video_Moment_Retrieval_From_Text_Queries_CVPR_2019_paper.pdf | cvpr-2019-6 | ['moment-retrieval'] | ['computer-vision'] | [ 2.04583764e-01 -4.04808402e-01 -5.93730092e-01 -4.56392676e-01
-1.06535244e+00 -5.27013600e-01 5.85654438e-01 9.25558731e-02
-5.73926687e-01 5.29713809e-01 4.15703386e-01 1.86399445e-01
-3.59316655e-02 -1.96449861e-01 -7.61510730e-01 -6.15296423e-01
4.76221330e-02 2.76506543e-01 2.58967519e-01 1.31372482... | [10.14433479309082, 0.8042119145393372] |
3675a577-fba1-4d52-9f34-3ae17e8f57d8 | mae-gebd-winning-the-cvpr-2023-loveu-gebd | 2306.15704 | null | https://arxiv.org/abs/2306.15704v1 | https://arxiv.org/pdf/2306.15704v1.pdf | MAE-GEBD:Winning the CVPR'2023 LOVEU-GEBD Challenge | The Generic Event Boundary Detection (GEBD) task aims to build a model for segmenting videos into segments by detecting general event boundaries applicable to various classes. In this paper, based on last year's MAE-GEBD method, we have improved our model performance on the GEBD task by adjusting the data processing st... | ['Jie Tang', 'Xu Cheng', 'Feng Hu', 'Zuwei Huang', 'Youzeng Li', 'Rui He', 'Yuanxi Sun'] | 2023-06-27 | null | null | null | null | ['boundary-detection', 'pseudo-label'] | ['computer-vision', 'miscellaneous'] | [-7.40646720e-02 -6.14107996e-02 -1.65169001e-01 -4.01584715e-01
-7.45838881e-01 -4.45659131e-01 4.21347857e-01 1.46888927e-01
-7.42453396e-01 5.10546148e-01 3.42169702e-02 3.01951729e-02
1.33864209e-01 -6.64036930e-01 -7.15072274e-01 -5.49781263e-01
-3.09095562e-01 4.51247573e-01 9.62050438e-01 1.40804812... | [8.668274879455566, 0.3287557363510132] |
6d8d2088-f472-4b02-9c26-522435a065dd | part-aware-measurement-for-robust-multi-view-1 | 2106.11589 | null | https://arxiv.org/abs/2106.11589v1 | https://arxiv.org/pdf/2106.11589v1.pdf | Part-Aware Measurement for Robust Multi-View Multi-Human 3D Pose Estimation and Tracking | This paper introduces an approach for multi-human 3D pose estimation and tracking based on calibrated multi-view. The main challenge lies in finding the cross-view and temporal correspondences correctly even when several human pose estimations are noisy. Compare to previous solutions that construct 3D poses from multip... | ['Chu-Song Chen', 'Jia-Da Li', 'Ching-Hsien Hsu', 'Yao-Chih Lee', 'Jia-Hong Lee', 'Hau Chu'] | 2021-06-22 | part-aware-measurement-for-robust-multi-view | https://openaccess.thecvf.com/content/CVPR2021W/AMFG/html/Chu_Part-Aware_Measurement_for_Robust_Multi-View_Multi-Human_3D_Pose_Estimation_and_CVPRW_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021W/AMFG/papers/Chu_Part-Aware_Measurement_for_Robust_Multi-View_Multi-Human_3D_Pose_Estimation_and_CVPRW_2021_paper.pdf | null | ['3d-pose-estimation', '3d-human-pose-tracking'] | ['computer-vision', 'computer-vision'] | [-2.53243923e-01 -1.65941268e-01 -5.78338280e-02 -2.86484420e-01
-9.74693775e-01 -4.59442288e-01 2.77164370e-01 -1.54798463e-01
-3.19001436e-01 4.68680412e-01 1.84086084e-01 4.67990279e-01
6.10270649e-02 -3.79218370e-01 -8.88612866e-01 -2.55285770e-01
7.40920156e-02 7.54638374e-01 6.06309295e-01 -3.37804645... | [7.064300537109375, -1.0143779516220093] |
77b0117d-703a-4b5f-984d-a5b2ffb2f965 | information-retrieval-for-label-noise | 2203.06408 | null | https://arxiv.org/abs/2203.06408v1 | https://arxiv.org/pdf/2203.06408v1.pdf | Information retrieval for label noise document ranking by bag sampling and group-wise loss | Long Document retrieval (DR) has always been a tremendous challenge for reading comprehension and information retrieval. The pre-training model has achieved good results in the retrieval stage and Ranking for long documents in recent years. However, there is still some crucial problem in long document ranking, such as ... | ['Fan Wang', 'Xing Hu', 'Jiajia Ding', 'Chunyu Li'] | 2022-03-12 | null | null | null | null | ['document-ranking'] | ['natural-language-processing'] | [ 5.26084080e-02 -4.91310954e-01 -2.62395829e-01 -5.15047729e-01
-1.43281710e+00 -3.60171765e-01 5.16358376e-01 4.03668016e-01
-5.34845650e-01 5.41572571e-01 4.34444815e-01 2.63695300e-01
-3.78007889e-01 -6.27059221e-01 -4.53218579e-01 -9.16110039e-01
2.23658279e-01 6.12729907e-01 2.79993117e-01 -2.59936661... | [11.485816955566406, 7.59336519241333] |
868742a2-5aef-40d1-a656-d40b6c8ac37f | mask-and-infill-applying-masked-language | 1908.08039 | null | https://arxiv.org/abs/1908.08039v1 | https://arxiv.org/pdf/1908.08039v1.pdf | "Mask and Infill" : Applying Masked Language Model to Sentiment Transfer | This paper focuses on the task of sentiment transfer on non-parallel text, which modifies sentiment attributes (e.g., positive or negative) of sentences while preserving their attribute-independent content. Due to the limited capability of RNNbased encoder-decoder structure to capture deep and long-range dependencies a... | ['Xing Wu', 'Tao Zhang', 'Liangjun Zang', 'Songlin Hu', 'Jizhong Han'] | 2019-08-21 | null | null | null | null | ['text-infilling'] | ['natural-language-processing'] | [ 4.37405229e-01 2.15856835e-01 -1.10534832e-01 -9.23448026e-01
-7.03012109e-01 -6.26471043e-01 5.69489360e-01 4.81861942e-02
-5.39821804e-01 9.96870935e-01 7.10566878e-01 -3.91017467e-01
7.52237856e-01 -8.55497718e-01 -8.29189539e-01 -5.66405356e-01
7.42505550e-01 2.17676222e-01 -1.68093190e-01 -6.86830163... | [11.515189170837402, 8.987691879272461] |
763046a5-f70b-440e-9a58-f2c6f8d005c0 | document-level-event-extraction-via-parallel | null | null | https://aclanthology.org/2021.acl-long.492 | https://aclanthology.org/2021.acl-long.492.pdf | Document-level Event Extraction via Parallel Prediction Networks | Document-level event extraction (DEE) is indispensable when events are described throughout a document. We argue that sentence-level extractors are ill-suited to the DEE task where event arguments always scatter across sentences and multiple events may co-exist in a document. It is a challenging task because it require... | ['Taifeng Wang', 'Jun Zhao', 'Kang Liu', 'Yubo Chen', 'Dianbo Sui', 'Hang Yang'] | 2021-08-01 | null | null | null | acl-2021-5 | ['document-level-event-extraction'] | ['natural-language-processing'] | [ 2.90865272e-01 3.79173197e-02 -9.44802314e-02 -5.82748532e-01
-1.54760110e+00 -6.51086450e-01 7.32884407e-01 3.62671852e-01
-4.54662025e-01 8.03409994e-01 6.03019059e-01 -2.60179043e-01
5.52043580e-02 -8.34429681e-01 -9.74050999e-01 -3.46468508e-01
1.04939282e-01 3.66224051e-01 5.76954894e-02 2.93057337... | [9.070380210876465, 9.15941047668457] |
f64c398a-93ac-4afe-a1a1-cbab746d7778 | carvenet-carving-point-block-for-complex-3d | 2107.13452 | null | https://arxiv.org/abs/2107.13452v1 | https://arxiv.org/pdf/2107.13452v1.pdf | CarveNet: Carving Point-Block for Complex 3D Shape Completion | 3D point cloud completion is very challenging because it heavily relies on the accurate understanding of the complex 3D shapes (e.g., high-curvature, concave/convex, and hollowed-out 3D shapes) and the unknown & diverse patterns of the partially available point clouds. In this paper, we propose a novel solution,i.e., P... | ['Yang Liu', 'Wei Feng', 'Lei Ma', 'Di Lin', 'Felix Juefei-Xu', 'Zhijie Wang', 'Qing Guo'] | 2021-07-28 | null | null | null | null | ['point-cloud-completion'] | ['computer-vision'] | [-2.76602566e-01 -1.74315497e-01 2.97304094e-01 -2.50741065e-01
-3.22804779e-01 -6.80023432e-01 3.34259182e-01 -2.10399270e-01
3.37998271e-02 -5.43506667e-02 -3.59420590e-02 -3.26257795e-01
2.55745519e-02 -8.66424143e-01 -1.07064033e+00 -5.23025334e-01
7.22103715e-02 6.44297421e-01 1.24221094e-01 -2.12681338... | [8.305142402648926, -3.532452344894409] |
e573f314-bcdb-446f-92b9-66fa7fd17081 | context-aware-language-modeling-for-goal-1 | 2204.10198 | null | https://arxiv.org/abs/2204.10198v2 | https://arxiv.org/pdf/2204.10198v2.pdf | Context-Aware Language Modeling for Goal-Oriented Dialogue Systems | Goal-oriented dialogue systems face a trade-off between fluent language generation and task-specific control. While supervised learning with large language models is capable of producing realistic text, how to steer such responses towards completing a specific task without sacrificing language quality remains an open q... | ['Mengjiao Yang', 'Sergey Levine', 'Yi Su', 'Justin Fu', 'Charlie Snell'] | 2022-04-18 | null | https://aclanthology.org/2022.findings-naacl.181 | https://aclanthology.org/2022.findings-naacl.181.pdf | findings-naacl-2022-7 | ['goal-oriented-dialogue-systems'] | ['natural-language-processing'] | [ 2.58850515e-01 4.46850181e-01 -3.33142281e-01 -5.72280169e-01
-8.81684422e-01 -6.73538446e-01 8.60734701e-01 1.10901967e-01
-5.47922313e-01 9.22924101e-01 4.62465525e-01 -4.72622246e-01
-2.75193732e-02 -4.90080446e-01 -2.07723692e-01 -3.54522288e-01
1.06445670e-01 8.75193596e-01 1.01262145e-01 -7.78466046... | [13.048102378845215, 8.048705101013184] |
7e240495-2e20-4a91-a406-2c6601ae1b7e | real-time-slam-pipeline-in-dynamics | 2303.02272 | null | https://arxiv.org/abs/2303.02272v1 | https://arxiv.org/pdf/2303.02272v1.pdf | Real-time SLAM Pipeline in Dynamics Environment | Inspired by the recent success of application of dense data approach by using ORB-SLAM and RGB-D SLAM, we propose a better pipeline of real-time SLAM in dynamics environment. Different from previous SLAM which can only handle static scenes, we are presenting a solution which use RGB-D SLAM as well as YOLO real-time obj... | ['Lingjie Kong', 'Alex Fu'] | 2023-03-04 | null | null | null | null | ['real-time-object-detection'] | ['computer-vision'] | [-5.05315103e-02 -3.65748823e-01 4.40244555e-01 -5.45035899e-01
-2.83502400e-01 -6.91373348e-01 6.63964391e-01 1.00777142e-01
-5.92139781e-01 6.75549746e-01 -1.22906737e-01 -1.11929797e-01
-2.72443205e-01 -8.66432250e-01 -5.67866683e-01 -2.24300861e-01
-2.12323502e-01 8.68682444e-01 7.86209345e-01 -5.77300787... | [7.2951226234436035, -2.254598379135132] |
db778a28-da02-41ca-b94c-976a3a4f7fc9 | towards-unified-keyframe-propagation-models | 2205.09731 | null | https://arxiv.org/abs/2205.09731v1 | https://arxiv.org/pdf/2205.09731v1.pdf | Towards Unified Keyframe Propagation Models | Many video editing tasks such as rotoscoping or object removal require the propagation of context across frames. While transformers and other attention-based approaches that aggregate features globally have demonstrated great success at propagating object masks from keyframes to the whole video, they struggle to propag... | ['Soumyadip Sengupta', 'Peter Michael', 'Patrick Esser'] | 2022-05-19 | null | null | null | null | ['video-inpainting'] | ['computer-vision'] | [ 2.91460723e-01 -1.69726819e-01 1.05566248e-01 -1.34007305e-01
-9.84527647e-01 -5.27501643e-01 6.68999791e-01 1.55611917e-01
-3.09612364e-01 5.78721404e-01 5.08354604e-01 2.80941248e-01
-5.68450429e-02 -5.87505579e-01 -1.11525989e+00 -5.14979362e-01
-2.31846124e-01 1.77432045e-01 4.66772735e-01 -1.84993073... | [10.746176719665527, -1.2579433917999268] |
6b57c578-c637-4098-8441-d847878ff4cc | translation-based-supervision-for-policy | null | null | https://aclanthology.org/2021.emnlp-main.130 | https://aclanthology.org/2021.emnlp-main.130.pdf | Translation-based Supervision for Policy Generation in Simultaneous Neural Machine Translation | In simultaneous machine translation, finding an agent with the optimal action sequence of reads and writes that maintain a high level of translation quality while minimizing the average lag in producing target tokens remains an extremely challenging problem. We propose a novel supervised learning approach for training ... | ['Anoop Sarkar', 'Hassan S. Shavarani', 'Ashkan Alinejad'] | null | null | null | null | emnlp-2021-11 | ['action-generation'] | ['computer-vision'] | [ 5.83526492e-01 3.32037181e-01 -6.22521460e-01 -2.39754900e-01
-1.57371867e+00 -7.69079864e-01 9.72868621e-01 8.67379084e-02
-4.09503758e-01 1.18754101e+00 -5.21758050e-02 -5.74405551e-01
3.92380476e-01 -8.11498523e-01 -1.01978362e+00 -7.25057721e-01
2.88473845e-01 1.14900565e+00 6.80024996e-02 -7.09078684... | [11.807792663574219, 9.198775291442871] |
fb84bc10-d6d3-4641-b675-53fc62878816 | efficient-hybrid-transformer-learning-global | 2109.08937 | null | https://arxiv.org/abs/2109.08937v4 | https://arxiv.org/pdf/2109.08937v4.pdf | UNetFormer: A UNet-like Transformer for Efficient Semantic Segmentation of Remote Sensing Urban Scene Imagery | Semantic segmentation of remotely sensed urban scene images is required in a wide range of practical applications, such as land cover mapping, urban change detection, environmental protection, and economic assessment.Driven by rapid developments in deep learning technologies, the convolutional neural network (CNN) has ... | ['Peter M. Atkinson', 'Xiaoliang Meng', 'Shenghui Fang', 'Rui Li', 'Chenxi Duan', 'Ce Zhang', 'Libo Wang'] | 2021-09-18 | null | null | null | null | ['scene-segmentation'] | ['computer-vision'] | [ 1.19125254e-01 -3.24642479e-01 -1.98164582e-01 -4.03061897e-01
-4.42791998e-01 -1.90430120e-01 2.53830522e-01 -1.90384895e-01
-5.88418722e-01 4.34842557e-01 -1.71792373e-01 -5.85148036e-01
-5.29966026e-04 -1.27911973e+00 -6.43605888e-01 -7.10755885e-01
1.70477301e-01 1.55790865e-01 4.13308859e-01 -1.26024321... | [9.244217872619629, -0.7299501895904541] |
adb3625e-5cb6-4106-ab74-675707f81b9a | hand-gesture-recognition-using-802-11ad | 2211.0709 | null | https://arxiv.org/abs/2211.07090v1 | https://arxiv.org/pdf/2211.07090v1.pdf | Hand gesture recognition using 802.11ad mmWave sensor in the mobile device | We explore the feasibility of AI assisted hand-gesture recognition using 802.11ad 60GHz (mmWave) technology in smartphones. Range-Doppler information (RDI) is obtained by using pulse Doppler radar for gesture recognition. We built a prototype system, where radar sensing and WLAN communication waveform can coexist by ti... | ['Hao Xu', 'Chirag Patel', 'Daniel Fontijne', 'Ilia Karmanov', 'Yin Huang', 'Andrian Beletchi', 'Jiuyuan Lu', 'Yuwei Ren'] | 2022-11-14 | null | null | null | null | ['hand-gesture-recognition', 'hand-gesture-recognition-1', 'gesture-recognition'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 4.65193689e-01 -4.30576950e-01 -1.03475727e-01 -5.08186758e-01
-3.87740880e-01 -2.60511935e-01 4.77385044e-01 -7.55558550e-01
-6.44439459e-01 4.61580694e-01 6.90197051e-02 -3.96588296e-01
-9.87560451e-02 -7.34171987e-01 -1.06606930e-01 -6.57324135e-01
-3.67447227e-01 1.39603436e-01 1.81205738e-02 7.82348365... | [6.677103519439697, 0.20469580590724945] |
7f4af9ca-4eb6-443c-b6ee-81f7fb3acc2b | multi-head-linear-attention-generative | 2012.10898 | null | https://arxiv.org/abs/2012.10898v1 | https://arxiv.org/pdf/2012.10898v1.pdf | Multi-Head Linear Attention Generative Adversarial Network for Thin Cloud Removal | In remote sensing images, the existence of the thin cloud is an inevitable and ubiquitous phenomenon that crucially reduces the quality of imageries and limits the scenarios of application. Therefore, thin cloud removal is an indispensable procedure to enhance the utilization of remote sensing images. Generally, even t... | ['Rui Li', 'Chenxi Duan'] | 2020-12-20 | null | null | null | null | ['cloud-removal'] | ['computer-vision'] | [ 3.72424006e-01 -2.46004105e-01 4.50080723e-01 -1.91663578e-01
-3.97261828e-01 -3.46265733e-01 2.01545596e-01 -4.49449599e-01
-3.78835574e-02 6.60025358e-01 -1.09750554e-01 -3.60042155e-01
1.50823817e-01 -1.08036542e+00 -6.70337737e-01 -1.43586624e+00
6.01605713e-01 -3.89829278e-02 -1.23290889e-01 -2.31509835... | [10.026091575622559, -1.9400556087493896] |
37546ace-f49a-47a6-8594-a3b1f9d05b3c | rcps-rectified-contrastive-pseudo-supervision | 2301.055 | null | https://arxiv.org/abs/2301.05500v1 | https://arxiv.org/pdf/2301.05500v1.pdf | RCPS: Rectified Contrastive Pseudo Supervision for Semi-Supervised Medical Image Segmentation | Medical image segmentation methods are generally designed as fully-supervised to guarantee model performance, which require a significant amount of expert annotated samples that are high-cost and laborious. Semi-supervised image segmentation can alleviate the problem by utilizing a large number of unlabeled images alon... | ['Lichi Zhang', 'Ying Mao', 'Xuehai Wu', 'Qian Wang', 'Sheng Wang', 'Zengxin Qi', 'Xiangyu Zhao'] | 2023-01-13 | null | null | null | null | ['semi-supervised-medical-image-segmentation'] | ['computer-vision'] | [ 5.69311500e-01 2.79929370e-01 -4.96155709e-01 -6.72020674e-01
-9.30716455e-01 -2.17764527e-01 2.13648397e-02 2.07560193e-02
-4.87151533e-01 7.74949253e-01 -6.32371530e-02 -4.52118292e-02
-2.06703931e-01 -5.22499382e-01 -5.39915025e-01 -9.86393273e-01
4.53835517e-01 5.14611959e-01 3.57381195e-01 1.78519174... | [14.589546203613281, -2.020796775817871] |
31779f06-c97c-4972-95ab-61bdb7ce23be | on-mitigating-hard-clusters-for-face | 2207.11895 | null | https://arxiv.org/abs/2207.11895v1 | https://arxiv.org/pdf/2207.11895v1.pdf | On Mitigating Hard Clusters for Face Clustering | Face clustering is a promising way to scale up face recognition systems using large-scale unlabeled face images. It remains challenging to identify small or sparse face image clusters that we call hard clusters, which is caused by the heterogeneity, \ie, high variations in size and sparsity, of the clusters. Consequent... | ['Qianru Sun', 'Yun Liang', 'Tao Wang', 'Jianqiang Huang', 'Chen Shen', 'Chong Chen', 'Huasong Zhong', 'Yingjie Chen'] | 2022-07-25 | null | null | null | null | ['face-clustering'] | ['computer-vision'] | [-2.19156042e-01 -1.52645335e-01 -2.85377890e-01 -5.54531634e-01
-6.08841360e-01 -3.82751673e-01 5.77678740e-01 -2.56013423e-01
1.50286973e-01 2.72255391e-01 -4.54322211e-02 -7.36897960e-02
-1.69240981e-01 -6.23504519e-01 -4.93461341e-01 -1.09546924e+00
-2.63872355e-01 7.41987944e-01 2.29026884e-01 3.25358361... | [13.463974952697754, 1.0476027727127075] |
09a95933-ddc6-4633-99f2-32ae5aa58aec | needle-tip-force-estimation-by-deep-learning | 2006.16675 | null | https://arxiv.org/abs/2006.16675v1 | https://arxiv.org/pdf/2006.16675v1.pdf | Needle tip force estimation by deep learning from raw spectral OCT data | Purpose. Needle placement is a challenging problem for applications such as biopsy or brachytherapy. Tip force sensing can provide valuable feedback for needle navigation inside the tissue. For this purpose, fiber-optical sensors can be directly integrated into the needle tip. Optical coherence tomography (OCT) can be ... | ['A. Schlaefer', 'T. Saathoff', 'N. Gessert', 'M. Gromniak'] | 2020-06-30 | null | null | null | null | ['robust-design'] | ['miscellaneous'] | [ 1.51216358e-01 1.02482907e-01 -7.31439888e-02 -3.10210615e-01
-6.49937987e-01 -6.60096884e-01 -2.38444030e-01 -4.08792309e-02
-6.65769339e-01 7.69412577e-01 -1.83048695e-01 -3.00450593e-01
7.92911798e-02 -5.04119217e-01 -1.11604023e+00 -6.75178230e-01
3.19045037e-01 4.29928780e-01 4.46168967e-02 6.36426881... | [13.861222267150879, -3.043362617492676] |
d033a500-02c7-45d7-b787-726f0d36e957 | cuffless-blood-pressure-estimation-from | null | null | https://arxiv.org/abs/1811.02214v1 | https://arxiv.org/abs/1811.02214v1 | Cuffless Blood Pressure Estimation from Electrocardiogram and Photoplethysmogram Using Waveform Based ANN-LSTM Network | Goal: Although photoplethysmogram (PPG) and electrocardiogram (ECG) signals can be used to estimate blood
pressure (BP) by extracting various features, the changes in morphological contours of both PPG and ECG signals due
to various diseases of circulatory system and interaction of other physiological systems make th... | ['Md. Sayed Tanveer and Md. Kamrul Hasan∗'] | 2018-11-06 | null | null | null | journal-2018-11 | ['blood-pressure-estimation'] | ['medical'] | [ 1.95999011e-01 -6.54579978e-03 2.21819356e-01 -4.92504627e-01
-4.56052236e-02 -7.79505761e-04 -3.58828813e-01 1.85287386e-01
-3.51202905e-01 1.13496971e+00 -1.89715058e-01 -4.14380252e-01
-1.79688036e-01 -7.57464647e-01 -1.41764209e-01 -6.38635099e-01
-5.16076744e-01 -4.97472696e-02 -1.40957788e-01 2.05803700... | [14.084672927856445, 2.9885177612304688] |
9eddf2c7-7e06-4f8a-a030-84bc5b3722e2 | a-robust-stereo-camera-localization-method | 1912.05023 | null | https://arxiv.org/abs/1912.05023v1 | https://arxiv.org/pdf/1912.05023v1.pdf | A Robust Stereo Camera Localization Method with Prior LiDAR Map Constrains | In complex environments, low-cost and robust localization is a challenging problem. For example, in a GPSdenied environment, LiDAR can provide accurate position information, but the cost is high. In general, visual SLAM based localization methods become unreliable when the sunlight changes greatly. Therefore, inexpensi... | ['Cheng-Zhong Xu', 'Lujia Wang', 'Dong Han', 'Zuhao Zou'] | 2019-12-02 | null | null | null | null | ['camera-localization'] | ['computer-vision'] | [-1.55056417e-01 -6.49459839e-01 -1.25283793e-01 -4.89961594e-01
-4.72996801e-01 -5.16576111e-01 3.95341694e-01 9.86606553e-02
-5.79174280e-01 8.07476997e-01 -4.23512459e-01 -1.64785177e-01
-9.20795426e-02 -7.56416082e-01 -7.49314129e-01 -5.71943462e-01
2.39269182e-01 5.98282337e-01 3.32291037e-01 -3.44661586... | [7.440664768218994, -2.1982641220092773] |
7b2779e4-b527-4711-93ec-cb6f0720fbad | joint-self-attention-and-scale-aggregation | 2008.02763 | null | https://arxiv.org/abs/2008.02763v1 | https://arxiv.org/pdf/2008.02763v1.pdf | Joint Self-Attention and Scale-Aggregation for Self-Calibrated Deraining Network | In the field of multimedia, single image deraining is a basic pre-processing work, which can greatly improve the visual effect of subsequent high-level tasks in rainy conditions. In this paper, we propose an effective algorithm, called JDNet, to solve the single image deraining problem and conduct the segmentation and ... | ['Zhixun Su', 'Yutong Wu', 'Cong Wang', 'Junyang Chen'] | 2020-08-06 | null | null | null | null | ['single-image-deraining'] | ['computer-vision'] | [-1.48778975e-01 -3.20020616e-01 3.45806926e-01 -6.45764530e-01
-2.36236215e-01 -2.19076782e-01 1.30160749e-01 -3.32420141e-01
-5.18185377e-01 5.80134690e-01 -9.43965930e-03 -1.44119099e-01
1.66355595e-01 -9.18844938e-01 -8.99031699e-01 -7.72898495e-01
-5.66851422e-02 -4.09126788e-01 5.15453696e-01 -3.02264571... | [10.843006134033203, -3.007000207901001] |
5814b772-ec6e-48aa-b6c7-6df91ccf7d55 | nighthazeformer-single-nighttime-haze-removal | 2305.09533 | null | https://arxiv.org/abs/2305.09533v1 | https://arxiv.org/pdf/2305.09533v1.pdf | NightHazeFormer: Single Nighttime Haze Removal Using Prior Query Transformer | Nighttime image dehazing is a challenging task due to the presence of multiple types of adverse degrading effects including glow, haze, blurry, noise, color distortion, and so on. However, most previous studies mainly focus on daytime image dehazing or partial degradations presented in nighttime hazy scenes, which may ... | ['ErKang Chen', 'Wenqi Ren', 'Tian Ye', 'Sixiang Chen', 'Zhongsheng Yan', 'Yun Liu'] | 2023-05-16 | null | null | null | null | ['image-dehazing'] | ['computer-vision'] | [ 1.09634221e-01 -5.71525097e-01 8.03245366e-01 -5.19808114e-01
-7.17371941e-01 -3.93208802e-01 5.00764370e-01 -5.28022289e-01
-7.49507546e-02 8.16946030e-01 4.82518941e-01 -5.53589240e-02
5.18117808e-02 -6.89985275e-01 -6.27873719e-01 -1.23761797e+00
4.65978622e-01 -2.13648289e-01 3.81983429e-01 -4.91357923... | [10.940521240234375, -3.1801352500915527] |
bfacb815-50e1-4866-b9ab-f47010530f04 | language-aware-multilingual-machine | 2302.05008 | null | https://arxiv.org/abs/2302.05008v1 | https://arxiv.org/pdf/2302.05008v1.pdf | Language-Aware Multilingual Machine Translation with Self-Supervised Learning | Multilingual machine translation (MMT) benefits from cross-lingual transfer but is a challenging multitask optimization problem. This is partly because there is no clear framework to systematically learn language-specific parameters. Self-supervised learning (SSL) approaches that leverage large quantities of monolingua... | ['Vedanuj Goswami', 'Jean Maillard', 'Haoran Xu'] | 2023-02-10 | null | null | null | null | ['cross-lingual-transfer'] | ['natural-language-processing'] | [ 3.90016399e-02 -2.60433942e-01 -4.67394978e-01 -4.10255373e-01
-1.85314727e+00 -6.15337312e-01 7.49920547e-01 -1.28492385e-01
-6.16494536e-01 1.01703978e+00 2.34210044e-01 -6.95072174e-01
3.74449223e-01 -1.18571930e-01 -1.20187676e+00 -6.53063238e-01
1.99015766e-01 6.29534304e-01 -2.86073178e-01 -3.98076892... | [11.45691967010498, 10.200719833374023] |
29e1297a-9679-469d-b627-901e1c13adf4 | tuning-multilingual-transformers-for-language | null | null | https://aclanthology.org/W19-3712 | https://aclanthology.org/W19-3712.pdf | Tuning Multilingual Transformers for Language-Specific Named Entity Recognition | Our paper addresses the problem of multilingual named entity recognition on the material of 4 languages: Russian, Bulgarian, Czech and Polish. We solve this task using the BERT model. We use a hundred languages multilingual model as base for transfer to the mentioned Slavic languages. Unsupervised pre-training of the B... | ['Alexey Sorokin', 'Mikhail Arkhipov', 'Yuri Kuratov', 'Maria Trofimova'] | 2019-08-01 | null | null | null | ws-2019-8 | ['multilingual-named-entity-recognition'] | ['natural-language-processing'] | [-6.82434261e-01 6.50657166e-05 -3.46605986e-01 -5.39492965e-01
-1.20337796e+00 -9.22860503e-01 7.10848212e-01 6.23214692e-02
-1.24885118e+00 1.36821783e+00 4.06195641e-01 -7.50013828e-01
6.46881402e-01 -4.76350427e-01 -7.55665600e-01 -8.58418718e-02
-1.12375543e-01 9.75116611e-01 1.11077344e-02 -3.68782669... | [9.986825942993164, 9.856061935424805] |
929327f2-3aaf-419d-abe7-9a2dcebf41bb | opinion-spam-recognition-method-for-online | 1807.11024 | null | http://arxiv.org/abs/1807.11024v1 | http://arxiv.org/pdf/1807.11024v1.pdf | Opinion Spam Recognition Method for Online Reviews using Ontological Features | Nowadays, there are a lot of people using social media opinions to make their
decision on buying products or services. Opinion spam detection is a hard
problem because fake reviews can be made by organizations as well as
individuals for different purposes. They write fake reviews to mislead readers
or automated detecti... | ['V. M. Ngo', 'L. H. Nguyen', 'N. T. H. Pham'] | 2018-07-29 | null | null | null | null | ['spam-detection'] | ['natural-language-processing'] | [-0.21535438 0.3774083 -0.11628152 -0.35599458 0.14038733 -0.6330953
0.5009818 0.74402165 -0.13888776 0.8801478 -0.21014097 -0.38066313
0.34021792 -1.2017398 -0.2517093 -0.21911736 0.6391769 0.7099188
0.9065988 -0.8353332 0.9675474 0.22005716 -1.3261628 0.43045154
1.1405032 0.78649545 -0.51... | [7.861884117126465, 10.054539680480957] |
36c150b0-afa3-43d5-bde5-bd44b2907f69 | fastwordbug-a-fast-method-to-generate | 2002.0076 | null | https://arxiv.org/abs/2002.00760v1 | https://arxiv.org/pdf/2002.00760v1.pdf | FastWordBug: A Fast Method To Generate Adversarial Text Against NLP Applications | In this paper, we present a novel algorithm, FastWordBug, to efficiently generate small text perturbations in a black-box setting that forces a sentiment analysis or text classification mode to make an incorrect prediction. By combining the part of speech attributes of words, we propose a scoring method that can quickl... | ['Wang minghua', 'Dou Goodman', 'Lv Zhonghou'] | 2020-01-31 | null | null | null | null | ['adversarial-text'] | ['adversarial'] | [ 8.55717883e-02 8.88068229e-02 -2.02395201e-01 -1.91635534e-01
-7.99602509e-01 -7.35478997e-01 4.97439981e-01 4.72674042e-01
-3.20825934e-01 6.04007363e-01 2.63606995e-01 -5.87173641e-01
5.05519748e-01 -7.12260723e-01 -7.26628840e-01 -5.05180597e-01
3.08618218e-01 4.27019835e-01 2.89078474e-01 -4.57992166... | [6.02384090423584, 8.062111854553223] |
ba844be9-1d1c-4a49-a18a-5cfa4d14cc18 | task-decoupled-framework-for-reference-based | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Huang_Task_Decoupled_Framework_for_Reference-Based_Super-Resolution_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Huang_Task_Decoupled_Framework_for_Reference-Based_Super-Resolution_CVPR_2022_paper.pdf | Task Decoupled Framework for Reference-Based Super-Resolution | Reference-based super-resolution(RefSR) has achieved impressive progress on the recovery of high-frequency details thanks to an additional reference high-resolution(HR) image input. Although the superiority compared with Single-Image Super-Resolution(SISR), existing RefSR methods easily result in the reference-unde... | ['Dazhi He', 'Yan-Feng Wang', 'Ya zhang', 'Siheng Chen', 'Yu Fu', 'Xiaoyun Zhang', 'Yixuan Huang'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['reference-based-super-resolution'] | ['computer-vision'] | [ 5.52749276e-01 -1.28319278e-01 2.93329190e-02 -1.58497974e-01
-1.35789955e+00 -2.05822941e-02 4.83145475e-01 -7.67236531e-01
-7.73662776e-02 8.58325303e-01 3.41689020e-01 3.89861584e-01
-4.62902747e-02 -8.09586525e-01 -6.09878361e-01 -9.42333341e-01
4.08232659e-01 -1.03152230e-01 4.82799619e-01 -4.96560425... | [10.956046104431152, -2.0663340091705322] |
de0b4e5d-ae25-4ae1-9d3e-18be00a92b12 | how-do-multilingual-encoders-learn-cross | 2207.05737 | null | https://arxiv.org/abs/2207.05737v1 | https://arxiv.org/pdf/2207.05737v1.pdf | How Do Multilingual Encoders Learn Cross-lingual Representation? | NLP systems typically require support for more than one language. As different languages have different amounts of supervision, cross-lingual transfer benefits languages with little to no training data by transferring from other languages. From an engineering perspective, multilingual NLP benefits development and maint... | ['Shijie Wu'] | 2022-07-12 | null | null | null | null | ['multilingual-nlp'] | ['natural-language-processing'] | [-4.63316470e-01 4.58563268e-02 -6.79107368e-01 -5.07559121e-01
-1.38610506e+00 -1.06367004e+00 7.02470839e-01 -1.11187309e-01
-4.59487915e-01 1.01088774e+00 4.51291054e-01 -6.67567909e-01
2.95040816e-01 -6.18112385e-01 -1.26526892e+00 -2.08073556e-01
5.61265799e-04 6.10463262e-01 -4.13428754e-01 -6.91132903... | [11.037808418273926, 9.974750518798828] |
8dd1f8ad-96fc-4229-b216-56049f33cb3d | a-nonconvex-low-rank-tensor-completion-model | 2003.10271 | null | https://arxiv.org/abs/2003.10271v2 | https://arxiv.org/pdf/2003.10271v2.pdf | A Nonconvex Low-Rank Tensor Completion Model for Spatiotemporal Traffic Data Imputation | Sparsity and missing data problems are very common in spatiotemporal traffic data collected from various sensing systems. Making accurate imputation is critical to many applications in intelligent transportation systems. In this paper, we formulate the missing data imputation problem in spatiotemporal traffic data in a... | ['Xinyu Chen', 'Lijun Sun', 'Jinming Yang'] | 2020-03-23 | null | null | null | null | ['traffic-data-imputation'] | ['time-series'] | [ 1.25509903e-01 -6.62525892e-01 -4.28243548e-01 -5.27025342e-01
-8.49017024e-01 1.08504377e-01 1.96505293e-01 -6.49489880e-01
-1.87127218e-01 8.54296029e-01 6.34009659e-01 -2.96837419e-01
-5.32237947e-01 -5.62685490e-01 -8.99177492e-01 -9.09472585e-01
6.68781623e-02 3.43631804e-01 -3.13085616e-01 -3.37889016... | [6.57404899597168, 2.126007556915283] |
936ddfa8-d0ea-4e89-b836-5380a1b0abec | evaluating-the-logical-reasoning-ability-of | 2304.03439 | null | https://arxiv.org/abs/2304.03439v3 | https://arxiv.org/pdf/2304.03439v3.pdf | Evaluating the Logical Reasoning Ability of ChatGPT and GPT-4 | Harnessing logical reasoning ability is a comprehensive natural language understanding endeavor. With the release of Generative Pretrained Transformer 4 (GPT-4), highlighted as "advanced" at reasoning tasks, we are eager to learn the GPT-4 performance on various logical reasoning tasks. This report analyses multiple lo... | ['Yue Zhang', 'Qiji Zhou', 'Jian Liu', 'Zhiyang Teng', 'Ruoxi Ning', 'Hanmeng Liu'] | 2023-04-07 | null | null | null | null | ['reading-comprehension', 'logical-reasoning'] | ['natural-language-processing', 'reasoning'] | [-3.68748069e-01 5.11859179e-01 -1.44170508e-01 -5.21541536e-01
-8.40750277e-01 -7.32322156e-01 7.02564657e-01 5.47414012e-02
-1.05304027e-03 7.98219025e-01 3.63360554e-01 -1.09674048e+00
-5.60623586e-01 -1.34636128e+00 -9.88118529e-01 4.03410345e-02
1.92575499e-01 1.07658422e+00 2.63474405e-01 -4.89785939... | [9.625263214111328, 7.407289981842041] |
876a917a-f9b6-4900-b723-f8b4a4e7b178 | supervised-visualization-for-data-exploration | 2006.08701 | null | https://arxiv.org/abs/2006.08701v1 | https://arxiv.org/pdf/2006.08701v1.pdf | Supervised Visualization for Data Exploration | Dimensionality reduction is often used as an initial step in data exploration, either as preprocessing for classification or regression or for visualization. Most dimensionality reduction techniques to date are unsupervised; they do not take class labels into account (e.g., PCA, MDS, t-SNE, Isomap). Such methods requir... | ['Kevin R. Moon', 'Jake S. Rhodes', 'Guy Wolf', 'Adele Cutler'] | 2020-06-15 | null | null | null | null | ['supervised-dimensionality-reduction'] | ['computer-vision'] | [-6.77743321e-03 -2.02486888e-01 -1.44497547e-02 -3.09032112e-01
-2.67685615e-02 -7.75904179e-01 8.05095792e-01 6.72932923e-01
-2.40834922e-01 4.45187300e-01 3.27531755e-01 -3.26457769e-01
-6.03050768e-01 -8.18347633e-01 8.95589963e-02 -9.71396744e-01
-3.25754166e-01 3.98346454e-01 -4.56447825e-02 -8.21292028... | [8.018515586853027, 4.5713934898376465] |
e372be3b-22cb-427a-bf5f-5f5d54bcc8f0 | sheetcopilot-bringing-software-productivity | 2305.19308 | null | https://arxiv.org/abs/2305.19308v1 | https://arxiv.org/pdf/2305.19308v1.pdf | SheetCopilot: Bringing Software Productivity to the Next Level through Large Language Models | Computer end users have spent billions of hours completing daily tasks like tabular data processing and project timeline scheduling. Most of these tasks are repetitive and error-prone, yet most end users lack the skill of automating away these burdensome works. With the advent of large language models (LLMs), directing... | ['Zhaoxiang Zhang', 'Qing Li', 'Yuntao Chen', 'Jingran Su', 'Hongxin Li'] | 2023-05-30 | null | null | null | null | ['code-generation'] | ['computer-code'] | [-4.38157581e-02 1.45016178e-01 -1.99539632e-01 -5.29034197e-01
-8.11629653e-01 -9.79896665e-01 7.20412791e-01 7.97520727e-02
4.98344796e-03 3.22297484e-01 2.16310918e-01 -4.27628040e-01
9.62662250e-02 -3.76991481e-01 -6.32824421e-01 3.05765718e-01
1.70939192e-01 6.38284862e-01 5.55328839e-02 -3.66550803... | [7.975882053375244, 7.706299304962158] |
4d0daca6-aa8b-43d8-86ed-bdd7edbeb9a0 | learning-to-detect-semantic-boundaries-with | 2212.07579 | null | https://arxiv.org/abs/2212.07579v1 | https://arxiv.org/pdf/2212.07579v1.pdf | Learning to Detect Semantic Boundaries with Image-level Class Labels | This paper presents the first attempt to learn semantic boundary detection using image-level class labels as supervision. Our method starts by estimating coarse areas of object classes through attentions drawn by an image classification network. Since boundaries will locate somewhere between such areas of different cla... | ['Suha Kwak', 'Sehyun Hwang', 'Namyup Kim'] | 2022-12-15 | null | null | null | null | ['boundary-detection', 'multiple-instance-learning'] | ['computer-vision', 'methodology'] | [ 6.90861046e-01 7.56835759e-01 -6.86904132e-01 -5.27166426e-01
-1.03388262e+00 -1.90464944e-01 5.67645252e-01 -4.34828140e-02
-1.78716376e-01 7.34853923e-01 -1.93056464e-01 -4.73118797e-02
2.45833978e-01 -8.24774027e-01 -1.16615522e+00 -3.59266400e-01
2.56795973e-01 9.26571369e-01 4.68188792e-01 2.32115775... | [9.54534912109375, 0.5411403179168701] |
818b551f-8c54-4a35-8663-57b765093afd | efficient-real-time-camera-based-estimation | 1909.01206 | null | https://arxiv.org/abs/1909.01206v1 | https://arxiv.org/pdf/1909.01206v1.pdf | Efficient Real-Time Camera Based Estimation of Heart Rate and Its Variability | Remote photo-plethysmography (rPPG) uses a remotely placed camera to estimating a person's heart rate (HR). Similar to how heart rate can provide useful information about a person's vital signs, insights about the underlying physio/psychological conditions can be obtained from heart rate variability (HRV). HRV is a mea... | ['Amogh Gudi', 'Roelof Lochmans', 'Jan van Gemert', 'Marian Bittner'] | 2019-09-03 | null | null | null | null | ['photoplethysmography-ppg', 'heart-rate-variability'] | ['medical', 'medical'] | [ 1.45740509e-01 -3.93521905e-01 1.56448305e-01 -3.51829916e-01
-3.65740478e-01 -5.50355613e-01 -1.23679757e-01 4.98970412e-02
-1.46454915e-01 7.02131808e-01 2.35046580e-01 3.86658400e-01
2.61083513e-01 -9.83162761e-01 1.54520199e-01 -4.53023940e-01
-2.06276238e-01 1.55002236e-01 -1.48296058e-01 1.04617052... | [13.894272804260254, 2.8623275756835938] |
c61a275d-a12a-4a4c-b8e9-fd7cf4a42e79 | scalable-transformer-for-pde-surrogate | 2305.1756 | null | https://arxiv.org/abs/2305.17560v1 | https://arxiv.org/pdf/2305.17560v1.pdf | Scalable Transformer for PDE Surrogate Modeling | Transformer has shown state-of-the-art performance on various applications and has recently emerged as a promising tool for surrogate modeling of partial differential equations (PDEs). Despite the introduction of linear-complexity variant, applying attention to a large number of grid points can result in instability an... | ['Amir Barati Farimani', 'Dule Shu', 'Zijie Li'] | 2023-05-27 | null | null | null | null | ['pde-surrogate-modeling'] | ['miscellaneous'] | [-2.32222483e-01 -2.16684178e-01 4.06628489e-01 1.33725345e-01
-7.46270478e-01 -4.98824060e-01 5.28121054e-01 -1.61242828e-01
-4.85883653e-01 7.78691888e-01 1.04255661e-01 -4.25230175e-01
-2.66080320e-01 -6.16450548e-01 -9.02770698e-01 -9.45282280e-01
-2.20334440e-01 5.16721606e-01 -2.85457790e-01 3.14994678... | [6.55389404296875, 3.4138762950897217] |
ced08641-c2b5-4ec9-87de-c5d9860ef599 | av-nerf-learning-neural-fields-for-real-world | 2302.02088 | null | https://arxiv.org/abs/2302.02088v2 | https://arxiv.org/pdf/2302.02088v2.pdf | AV-NeRF: Learning Neural Fields for Real-World Audio-Visual Scene Synthesis | Human perception of the complex world relies on a comprehensive analysis of multi-modal signals, and the co-occurrences of audio and video signals provide humans with rich cues. This paper focuses on novel audio-visual scene synthesis in the real world. Given a video recording of an audio-visual scene, the task is to s... | ['Chenliang Xu', 'Anurag Kumar', 'Yapeng Tian', 'Chao Huang', 'Susan Liang'] | 2023-02-04 | null | null | null | null | ['audio-generation'] | ['audio'] | [ 2.59682089e-01 -2.35767812e-01 5.10340512e-01 -2.23097235e-01
-1.04350793e+00 -5.12421846e-01 3.86916131e-01 -1.51151642e-01
-6.65352046e-02 3.60244244e-01 5.55134535e-01 2.75698364e-01
2.31718704e-01 -5.00297368e-01 -1.12608862e+00 -6.47007406e-01
1.08958684e-01 -2.85025716e-01 3.13936949e-01 -1.27151757... | [14.98573112487793, 5.089306831359863] |
5e12ffc6-b23b-42cf-b5eb-1ad804a44bbe | simple-yet-powerful-native-language | null | null | https://aclanthology.org/W13-1720 | https://aclanthology.org/W13-1720.pdf | Simple Yet Powerful Native Language Identification on TOEFL11 | null | ['Po-Hsiang Lai', 'Ching-Yi Wu', 'Vincent Ng', 'Yang Liu'] | 2013-06-01 | null | null | null | ws-2013-6 | ['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... | [-7.2176055908203125, 3.658822536468506] |
b29b98c9-a973-4dae-a633-779905c7fcd0 | a-few-brief-notes-on-deepimpact-coil-and-a | 2106.14807 | null | https://arxiv.org/abs/2106.14807v1 | https://arxiv.org/pdf/2106.14807v1.pdf | A Few Brief Notes on DeepImpact, COIL, and a Conceptual Framework for Information Retrieval Techniques | Recent developments in representational learning for information retrieval can be organized in a conceptual framework that establishes two pairs of contrasts: sparse vs. dense representations and unsupervised vs. learned representations. Sparse learned representations can further be decomposed into expansion and term w... | ['Xueguang Ma', 'Jimmy Lin'] | 2021-06-28 | null | null | null | null | ['passage-ranking'] | ['natural-language-processing'] | [ 2.44367778e-01 -1.51443586e-01 -4.93795246e-01 -1.61995426e-01
-1.16928864e+00 -6.32032216e-01 1.05387723e+00 4.75371510e-01
-2.82082856e-01 4.62783724e-01 9.31089818e-01 -1.32029220e-01
-8.03992748e-01 -5.40558577e-01 -3.33524615e-01 -5.75255990e-01
-3.08659017e-01 8.12253952e-01 1.58109348e-02 -7.43509412... | [11.465315818786621, 7.599825859069824] |
44fb00a9-d6a8-4bf1-aab5-7c2fae2ca9da | data-might-be-enough-bridge-real-world | 2303.10828 | null | https://arxiv.org/abs/2303.10828v1 | https://arxiv.org/pdf/2303.10828v1.pdf | Data Might be Enough: Bridge Real-World Traffic Signal Control Using Offline Reinforcement Learning | Applying reinforcement learning (RL) to traffic signal control (TSC) has become a promising solution. However, most RL-based methods focus solely on optimization within simulators and give little thought to deployment issues in the real world. Online RL-based methods, which require interaction with the environment, are... | ['Jianming Deng', 'Liang Zhang'] | 2023-03-20 | null | null | null | null | ['offline-rl'] | ['playing-games'] | [-3.49026531e-01 -3.68947744e-01 -3.84259313e-01 -1.60682797e-01
-7.54894376e-01 -4.61143404e-01 3.67246866e-01 -1.29212335e-01
-3.34246516e-01 9.13525641e-01 -3.65944952e-01 -7.87781179e-01
-1.41027421e-01 -9.28345501e-01 -7.12349534e-01 -5.89123905e-01
-4.95448411e-01 4.36282516e-01 8.56717646e-01 -6.48773134... | [5.256855010986328, 1.376437783241272] |
e1e08e6d-c33f-471b-8f18-814aedd5ae10 | morph-kgc-scalable-knowledge-graph | null | null | https://content.iospress.com/articles/semantic-web/sw223135 | https://content.iospress.com/download/semantic-web/sw223135?id=semantic-web%2Fsw223135 | Morph-KGC: Scalable knowledge graph materialization with mapping partitions | Knowledge graphs are often constructed from heterogeneous data sources, using declarative rules that map them to a target ontology and materializing them into RDF. When these data sources are large, the materialization of the entire knowledge graph may be computationally expensive and not suitable for those cases where... | ['Oscar Corcho', 'María S. Pérez', 'Jhon Toledo', 'David Chaves-Fraga', 'Julián Arenas-Guerrero'] | 2022-08-25 | null | null | null | semantic-web-2022-8 | ['knowledge-graphs-data-curation', 'data-integration'] | ['knowledge-base', 'knowledge-base'] | [-4.03594878e-03 5.22973597e-01 -9.97201800e-02 -2.30942607e-01
-2.18917191e-01 -5.54306269e-01 5.55401206e-01 9.30582225e-01
-4.47126210e-01 8.43401313e-01 -9.59060267e-02 -3.12883914e-01
-4.61524904e-01 -1.64322269e+00 -8.34935546e-01 1.55599033e-02
-1.25178784e-01 9.62419987e-01 1.03953731e+00 -4.71039750... | [9.019852638244629, 7.7522292137146] |
4e2aaa2a-6c14-453f-babf-bf074a008b53 | deeprls-a-recurrent-network-architecture-with | 2112.05505 | null | https://arxiv.org/abs/2112.05505v1 | https://arxiv.org/pdf/2112.05505v1.pdf | DeepRLS: A Recurrent Network Architecture with Least Squares Implicit Layers for Non-blind Image Deconvolution | In this work, we study the problem of non-blind image deconvolution and propose a novel recurrent network architecture that leads to very competitive restoration results of high image quality. Motivated by the computational efficiency and robustness of existing large scale linear solvers, we manage to express the solut... | ['Stamatios Lefkimmiatis', 'Daniil Selikhanovych', 'Iaroslav Koshelev'] | 2021-12-10 | null | null | null | null | ['image-deconvolution'] | ['computer-vision'] | [ 2.57298410e-01 1.67280864e-02 3.20469290e-01 -6.96549490e-02
-7.09633708e-01 -1.93823978e-01 4.68572736e-01 -3.44336331e-01
-5.83617151e-01 6.27978444e-01 6.36047050e-02 -3.56594056e-01
-2.43061736e-01 -4.33689624e-01 -7.21382856e-01 -9.25987303e-01
2.71497071e-01 2.62941897e-01 1.25222147e-01 -1.43902004... | [11.681173324584961, -2.5352649688720703] |
0cff8bb7-8a08-4d9d-8e64-acbd9a58e702 | towards-characterizing-domain-counterfactuals | 2306.11281 | null | https://arxiv.org/abs/2306.11281v1 | https://arxiv.org/pdf/2306.11281v1.pdf | Towards Characterizing Domain Counterfactuals For Invertible Latent Causal Models | Learning latent causal models from data has many important applications such as robustness, model extrapolation, and counterfactuals. Most prior theoretic work has focused on full causal discovery (i.e., recovering the true latent variables) but requires strong assumptions such as linearity or fails to have any analysi... | ['David I. Inouye', 'Murat Kocaoglu', 'Ruqi Bai', 'Zeyu Zhou', 'Sean Kulinski'] | 2023-06-20 | null | null | null | null | ['causal-discovery'] | ['knowledge-base'] | [ 4.25682694e-01 6.27199590e-01 -8.08234930e-01 -1.53258830e-01
-1.77714556e-01 -7.68018782e-01 9.17614281e-01 -2.04868123e-01
1.43192306e-01 1.03022826e+00 6.57622695e-01 -8.08291614e-01
-6.35696471e-01 -9.46333468e-01 -1.19074285e+00 -6.78644300e-01
-6.14928961e-01 6.02505982e-01 -1.40976354e-01 2.46202633... | [8.053357124328613, 5.436249732971191] |
ea5adbe2-62a4-4162-b2ea-49fe74ad6af2 | v2v4real-a-real-world-large-scale-dataset-for | 2303.07601 | null | https://arxiv.org/abs/2303.07601v2 | https://arxiv.org/pdf/2303.07601v2.pdf | V2V4Real: A Real-world Large-scale Dataset for Vehicle-to-Vehicle Cooperative Perception | Modern perception systems of autonomous vehicles are known to be sensitive to occlusions and lack the capability of long perceiving range. It has been one of the key bottlenecks that prevents Level 5 autonomy. Recent research has demonstrated that the Vehicle-to-Vehicle (V2V) cooperative perception system has great pot... | ['Jiaqi Ma', 'Bolei Zhou', 'Hongkai Yu', 'Rui Song', 'Xiaoyu Dong', 'Hao Xiang', 'Zonglin Meng', 'Zhengzhong Tu', 'Shuo Zhang', 'Hanzhao Li', 'Jinlong Li', 'Xin Xia', 'Runsheng Xu'] | 2023-03-14 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Xu_V2V4Real_A_Real-World_Large-Scale_Dataset_for_Vehicle-to-Vehicle_Cooperative_Perception_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Xu_V2V4Real_A_Real-World_Large-Scale_Dataset_for_Vehicle-to-Vehicle_Cooperative_Perception_CVPR_2023_paper.pdf | cvpr-2023-1 | ['3d-object-tracking'] | ['computer-vision'] | [-3.17330211e-01 5.84053509e-02 -1.87056392e-01 -6.16477728e-01
-8.28047633e-01 -8.79092932e-01 6.79333925e-01 5.08942753e-02
-4.89370942e-01 3.81292641e-01 -4.45339233e-01 -3.92559648e-01
9.05185416e-02 -7.89141357e-01 -9.36258972e-01 -5.96313477e-01
-1.67338759e-01 5.48322499e-01 1.06913197e+00 -7.51610160... | [7.801602840423584, -1.9075980186462402] |
9bf3cf58-fcca-42af-beed-f52e463d3a1d | context-aware-pretraining-for-efficient-blind | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wang_Context-Aware_Pretraining_for_Efficient_Blind_Image_Decomposition_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wang_Context-Aware_Pretraining_for_Efficient_Blind_Image_Decomposition_CVPR_2023_paper.pdf | Context-Aware Pretraining for Efficient Blind Image Decomposition | In this paper, we study Blind Image Decomposition (BID), which is to uniformly remove multiple types of degradation at once without foreknowing the noise type. There remain two practical challenges: (1) Existing methods typically require massive data supervision, making them infeasible to real-world scenarios. (2) ... | ['Yi Yang', 'Yifan Sun', 'Ruijie Quan', 'Zhedong Zheng', 'Chao Wang'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['image-reconstruction'] | ['computer-vision'] | [ 5.60413003e-01 -2.79288799e-01 -1.31449506e-01 -2.49178484e-01
-9.08485591e-01 -2.62472749e-01 3.65899473e-01 -2.78656453e-01
-1.72151700e-01 4.51071501e-01 2.68210351e-01 -2.94505179e-01
-1.78932473e-01 -5.22491634e-01 -7.35792100e-01 -1.19564080e+00
2.64365762e-01 -2.49005869e-01 1.49449125e-01 -6.27019629... | [11.067758560180664, -2.343198299407959] |
e215ac16-b2a3-43ff-bb23-f0256fb796a4 | online-learning-of-order-flow-and-market | 2307.02375 | null | https://arxiv.org/abs/2307.02375v1 | https://arxiv.org/pdf/2307.02375v1.pdf | Online Learning of Order Flow and Market Impact with Bayesian Change-Point Detection Methods | Financial order flow exhibits a remarkable level of persistence, wherein buy (sell) trades are often followed by subsequent buy (sell) trades over extended periods. This persistence can be attributed to the division and gradual execution of large orders. Consequently, distinct order flow regimes might emerge, which can... | ['Piero Mazzarisi', 'Fabrizio Lillo', 'Ioanna-Yvonni Tsaknaki'] | 2023-07-05 | null | null | null | null | ['change-point-detection'] | ['time-series'] | [-3.74196380e-01 -6.88338995e-01 -3.11290711e-01 -1.56070605e-01
-4.07125980e-01 -1.14418304e+00 9.12692904e-01 4.23839808e-01
7.39970505e-02 6.01749599e-01 1.56533554e-01 -7.04505026e-01
-6.20907962e-01 -7.39756346e-01 -5.66920817e-01 -2.78390884e-01
-4.59055215e-01 5.26055396e-01 3.31906796e-01 3.70714664... | [4.841994285583496, 4.073649883270264] |
6a8ae512-dbe1-4077-b75e-7adcf71ecc8b | learning-from-synthetic-data-using-a-stacked | 1509.05463 | null | http://arxiv.org/abs/1509.05463v2 | http://arxiv.org/pdf/1509.05463v2.pdf | Learning from Synthetic Data Using a Stacked Multichannel Autoencoder | Learning from synthetic data has many important and practical applications.
An example of application is photo-sketch recognition. Using synthetic data is
challenging due to the differences in feature distributions between synthetic
and real data, a phenomenon we term synthetic gap. In this paper, we
investigate and fo... | ['Yanwei Fu', 'Shanshan Jiang', 'Gady Agam', 'Xi Zhang', 'Leonid Sigal'] | 2015-09-17 | null | null | null | null | ['sketch-recognition'] | ['computer-vision'] | [ 3.19178104e-01 -1.88349456e-01 1.76785469e-01 -3.97604764e-01
-4.40462112e-01 -1.62108451e-01 5.93821883e-01 -7.74032474e-01
2.95366254e-02 7.49117136e-01 1.32202879e-02 1.11011356e-01
-9.63994041e-02 -7.23983109e-01 -8.32218885e-01 -7.08857477e-01
3.01463872e-01 2.83047557e-01 -2.47946065e-02 -1.35217577... | [11.99551010131836, 0.3630218207836151] |
d72b23c6-fd40-470d-9f2b-6a19f7d362ab | word-class-representations-spontaneously | 2302.07588 | null | https://arxiv.org/abs/2302.07588v1 | https://arxiv.org/pdf/2302.07588v1.pdf | Word class representations spontaneously emerge in a deep neural network trained on next word prediction | How do humans learn language, and can the first language be learned at all? These fundamental questions are still hotly debated. In contemporary linguistics, there are two major schools of thought that give completely opposite answers. According to Chomsky's theory of universal grammar, language cannot be learned becau... | ['Patrick Krauss', 'Andreas Maier', 'Paul Stoewer', 'Achim Schilling', 'Kishore Surendra'] | 2023-02-15 | null | null | null | null | ['language-acquisition'] | ['natural-language-processing'] | [ 5.10884941e-01 3.58223319e-01 -8.39870498e-02 -5.00538766e-01
3.69139671e-01 -7.21126854e-01 7.34338999e-01 5.49431443e-01
-5.20016909e-01 2.37743646e-01 2.63537914e-01 -7.83811271e-01
2.25136001e-02 -1.12336969e+00 -6.66434646e-01 -4.91920322e-01
9.33710765e-03 3.42121750e-01 1.99059378e-02 -5.49374223... | [10.340147972106934, 8.873058319091797] |
4a4aca33-c4d1-449c-8e8d-dbbeaa9b602c | diable-efficient-dialogue-state-tracking-as | 2305.1702 | null | https://arxiv.org/abs/2305.17020v1 | https://arxiv.org/pdf/2305.17020v1.pdf | Diable: Efficient Dialogue State Tracking as Operations on Tables | Sequence-to-sequence state-of-the-art systems for dialogue state tracking (DST) use the full dialogue history as input, represent the current state as a list with all the slots, and generate the entire state from scratch at each dialogue turn. This approach is inefficient, especially when the number of slots is large a... | ['Lluis Marquez', 'Chao Shang', 'Momchil Hardalov', 'Yoshinari Fujinuma', 'Pietro Lesci'] | 2023-05-26 | null | null | null | null | ['dialogue-state-tracking'] | ['natural-language-processing'] | [ 1.47686228e-01 5.65187633e-01 -2.29664251e-01 -3.13748270e-01
-1.24261928e+00 -9.74797666e-01 1.03802598e+00 2.95884997e-01
-4.07038093e-01 9.37453985e-01 7.13572085e-01 -5.11950374e-01
4.19170588e-01 -4.26222295e-01 -2.68880904e-01 -8.21792558e-02
-5.44822365e-02 1.32163858e+00 5.11338711e-01 -9.73470330... | [12.859674453735352, 7.9304118156433105] |
09686b02-95d1-4e99-8342-bc6d713f7a22 | optical-coherence-tomography-image | 2306.1175 | null | https://arxiv.org/abs/2306.11750v1 | https://arxiv.org/pdf/2306.11750v1.pdf | Optical Coherence Tomography Image Enhancement via Block Hankelization and Low Rank Tensor Network Approximation | In this paper, the problem of image super-resolution for Optical Coherence Tomography (OCT) has been addressed. Due to the motion artifacts, OCT imaging is usually done with a low sampling rate and the resulting images are often noisy and have low resolution. Therefore, reconstruction of high resolution OCT images from... | ['Hossein Rabbani', 'Andrzej Cichocki', 'Farnaz Sedighin'] | 2023-06-19 | null | null | null | null | ['image-super-resolution', 'image-enhancement', 'super-resolution'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 3.11581135e-01 -3.08875680e-01 5.91630749e-02 8.49542618e-02
-6.42620146e-01 3.17468792e-02 -7.07516745e-02 -3.74517143e-01
-1.29037723e-01 1.02623391e+00 1.99777618e-01 3.20266962e-01
-4.35880542e-01 -6.22709334e-01 -7.76405856e-02 -9.19644713e-01
-1.02883216e-03 7.06948861e-02 4.36376929e-01 1.87590092... | [11.103544235229492, -2.409626007080078] |
c771d91f-7bc3-446a-8bff-d01f0b42368b | extending-context-window-of-large-language | 2306.15595 | null | https://arxiv.org/abs/2306.15595v2 | https://arxiv.org/pdf/2306.15595v2.pdf | Extending Context Window of Large Language Models via Positional Interpolation | We present Position Interpolation (PI) that extends the context window sizes of RoPE-based pretrained LLMs such as LLaMA models to up to 32768 with minimal fine-tuning (within 1000 steps), while demonstrating strong empirical results on various tasks that require long context, including passkey retrieval, language mode... | ['Yuandong Tian', 'Liangjian Chen', 'Sherman Wong', 'Shouyuan Chen'] | 2023-06-27 | null | null | null | null | ['retrieval', 'document-summarization'] | ['methodology', 'natural-language-processing'] | [ 1.94988191e-01 2.10633188e-01 -6.22693956e-01 -3.79777580e-01
-1.08286750e+00 -5.49081683e-01 4.92154330e-01 2.95715272e-01
-8.02306354e-01 8.67421269e-01 4.60488498e-01 -7.78046310e-01
1.50294993e-02 -4.56558764e-01 -9.39830840e-01 -2.43346363e-01
-3.28591615e-01 -6.81411996e-02 2.90997654e-01 -1.07831836... | [11.138648986816406, 8.189362525939941] |
c3625ea1-fdbd-41d4-ab09-54498c72c3e9 | generalized-representations-learning-for-time | 2209.07027 | null | https://arxiv.org/abs/2209.07027v4 | https://arxiv.org/pdf/2209.07027v4.pdf | Out-of-Distribution Representation Learning for Time Series Classification | Time series classification is an important problem in real world. Due to its non-stationary property that the distribution changes over time, it remains challenging to build models for generalization to unseen distributions. In this paper, we propose to view the time series classification problem from the distribution ... | ['Xing Xie', 'Yiqiang Chen', 'Xinwei Sun', 'Jindong Wang', 'Wang Lu'] | 2022-09-15 | null | null | null | null | ['gesture-recognition'] | ['computer-vision'] | [ 1.41562223e-01 -5.00432491e-01 -3.51004630e-01 -4.48606700e-01
-8.44133615e-01 -1.02026296e+00 5.15485823e-01 -5.81019707e-02
-1.20906726e-01 6.11850381e-01 3.18140864e-01 -1.11632615e-01
-2.30513453e-01 -4.10209805e-01 -6.09537125e-01 -8.38235199e-01
-2.98154086e-01 2.66182572e-01 -4.21751767e-01 2.12952182... | [7.432645320892334, 3.011188507080078] |
3a74e86f-17e1-4335-bad7-a801b3507ddd | graph-based-time-series-anomaly-detection-a | 2302.00058 | null | https://arxiv.org/abs/2302.00058v2 | https://arxiv.org/pdf/2302.00058v2.pdf | Graph-based Time-Series Anomaly Detection: A Survey | With the recent advances in technology, a wide range of systems continue to collect a large amount of data over time and thus generate time series. Time-Series Anomaly Detection (TSAD) is an important task in various time-series applications such as e-commerce, cybersecurity, vehicle maintenance, and healthcare monitor... | ['Narges Armanfard', 'Ali Karami', 'Thi Kieu Khanh Ho'] | 2023-01-31 | null | null | null | null | ['graph-anomaly-detection'] | ['graphs'] | [ 1.72462389e-01 -3.79843235e-01 -2.15346530e-01 -1.23815276e-01
-5.23441983e-03 -3.92755806e-01 3.95253986e-01 8.20666015e-01
2.05449820e-01 1.91042066e-01 -2.60416001e-01 -8.02362442e-01
-4.68433797e-01 -9.69554126e-01 -1.97579503e-01 -5.31429052e-01
-1.14975870e+00 1.46505848e-01 2.79411823e-01 -4.13127571... | [7.260163307189941, 2.8157758712768555] |
4a6fc7af-fab9-4435-8e78-6130fb77b289 | rl-grit-reinforcement-learning-for-grammar | 2105.13114 | null | https://arxiv.org/abs/2105.13114v1 | https://arxiv.org/pdf/2105.13114v1.pdf | RL-GRIT: Reinforcement Learning for Grammar Inference | When working to understand usage of a data format, examples of the data format are often more representative than the format's specification. For example, two different applications might use very different JSON representations, or two PDF-writing applications might make use of very different areas of the PDF specifica... | ['Walt Woods'] | 2021-05-17 | null | null | null | null | ['constituency-parsing'] | ['natural-language-processing'] | [ 3.05851489e-01 3.03649157e-01 -3.08852673e-01 -5.50102234e-01
-5.04976928e-01 -9.91971254e-01 4.59834337e-01 3.01553398e-01
-6.60995990e-02 7.50946641e-01 -3.26090790e-02 -1.23846138e+00
-1.78246230e-01 -1.16645694e+00 -8.35376799e-01 -1.54626086e-01
-8.35239589e-02 4.03220952e-01 4.45571721e-01 -3.93579423... | [8.178014755249023, 7.3883891105651855] |
db811cdd-093e-4a84-b75f-728010593a85 | calculating-and-visualizing-counterfactual | 2306.06506 | null | https://arxiv.org/abs/2306.06506v1 | https://arxiv.org/pdf/2306.06506v1.pdf | Calculating and Visualizing Counterfactual Feature Importance Values | Despite the success of complex machine learning algorithms, mostly justified by an outstanding performance in prediction tasks, their inherent opaque nature still represents a challenge to their responsible application. Counterfactual explanations surged as one potential solution to explain individual decision results.... | ['David Martens', 'Raphael Mazzine Barbosa de Oliveira', 'Bjorge Meulemeester'] | 2023-06-10 | null | null | null | null | ['counterfactual-explanation'] | ['miscellaneous'] | [ 2.76278704e-01 6.04444206e-01 -4.71109390e-01 -2.93310702e-01
-1.52798578e-01 -5.63942134e-01 9.23045516e-01 2.49617234e-01
-1.94519348e-02 1.32130492e+00 3.88163060e-01 -9.13090765e-01
-5.27243435e-01 -7.01839983e-01 -6.12417579e-01 -6.66267812e-01
-3.79355103e-01 2.41874844e-01 -2.44496882e-01 1.26008410... | [8.735918998718262, 5.636493682861328] |
2b1264e8-3495-427a-bc26-7913ed21abe6 | upcycling-models-under-domain-and-category | 2303.0711 | null | https://arxiv.org/abs/2303.07110v1 | https://arxiv.org/pdf/2303.07110v1.pdf | Upcycling Models under Domain and Category Shift | Deep neural networks (DNNs) often perform poorly in the presence of domain shift and category shift. How to upcycle DNNs and adapt them to the target task remains an important open problem. Unsupervised Domain Adaptation (UDA), especially recently proposed Source-free Domain Adaptation (SFDA), has become a promising te... | ['Changjun Jiang', 'DaCheng Tao', 'Guang Chen', 'Cewu Lu', 'Florian Roehrbein', 'Tianpei Zou', 'Sanqing Qu'] | 2023-03-13 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Qu_Upcycling_Models_Under_Domain_and_Category_Shift_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Qu_Upcycling_Models_Under_Domain_and_Category_Shift_CVPR_2023_paper.pdf | cvpr-2023-1 | ['source-free-domain-adaptation', 'universal-domain-adaptation'] | ['computer-vision', 'computer-vision'] | [ 1.72733605e-01 -3.79231423e-01 -1.96548671e-01 -4.94776577e-01
-7.44116068e-01 -8.43136191e-01 5.66624939e-01 4.02408168e-02
-5.93170881e-01 8.46698761e-01 -8.71480256e-02 -1.17358543e-01
-1.11519620e-01 -6.06114686e-01 -6.05733693e-01 -1.04015696e+00
4.07162279e-01 7.05027580e-01 3.72639805e-01 -1.04575157... | [10.37541675567627, 2.9812381267547607] |
56e06a71-9e77-49e2-a5b2-2c4a107fc497 | ltg-at-semeval-2016-task-11-complex-word | null | null | https://aclanthology.org/S16-1154 | https://aclanthology.org/S16-1154.pdf | LTG at SemEval-2016 Task 11: Complex Word Identification with Classifier Ensembles | null | ['Marcos Zampieri', 'Mark Dras', 'Shervin Malmasi'] | 2016-06-01 | null | null | null | semeval-2016-6 | ['complex-word-identification'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.422660827636719, 3.613111972808838] |
ec3b8a3d-0e85-4685-a6b7-4d38f80c8158 | topological-sort-for-sentence-ordering | 2005.00432 | null | https://arxiv.org/abs/2005.00432v1 | https://arxiv.org/pdf/2005.00432v1.pdf | Topological Sort for Sentence Ordering | Sentence ordering is the task of arranging the sentences of a given text in the correct order. Recent work using deep neural networks for this task has framed it as a sequence prediction problem. In this paper, we propose a new framing of this task as a constraint solving problem and introduce a new technique to solve ... | ['Alan W. black', 'Shrimai Prabhumoye', 'Ruslan Salakhutdinov'] | 2020-05-01 | topological-sort-for-sentence-ordering-1 | https://aclanthology.org/2020.acl-main.248 | https://aclanthology.org/2020.acl-main.248.pdf | acl-2020-6 | ['sentence-ordering'] | ['natural-language-processing'] | [ 2.65251160e-01 1.35909513e-01 -7.36607909e-02 -9.24709082e-01
-1.56133369e-01 -5.12894452e-01 7.16572940e-01 3.37655306e-01
-5.63631117e-01 7.49695122e-01 8.46009374e-01 -2.87456512e-01
-3.24586593e-02 -4.96838361e-01 -4.15411830e-01 -9.38833058e-02
-6.67507052e-02 6.68298841e-01 1.42482251e-01 -3.84543061... | [11.885749816894531, 9.28035831451416] |
8270397b-3f5c-49a6-bc51-50839d1ee172 | deep-learning-assessment-of-tumor | 1610.03467 | null | http://arxiv.org/abs/1610.03467v1 | http://arxiv.org/pdf/1610.03467v1.pdf | Deep Learning Assessment of Tumor Proliferation in Breast Cancer Histological Images | Current analysis of tumor proliferation, the most salient prognostic
biomarker for invasive breast cancer, is limited to subjective mitosis counting
by pathologists in localized regions of tissue images. This study presents the
first data-driven integrative approach to characterize the severity of tumor
growth and spre... | ['Manan Shah', 'Dayong Wang', 'Christopher Rubadue', 'David Suster'] | 2016-10-11 | null | null | null | null | ['severity-prediction'] | ['computer-vision'] | [ 4.31461781e-01 5.82666732e-02 -9.82459247e-01 -2.05076654e-02
-1.39590383e+00 -4.62189496e-01 2.96984315e-01 1.06829035e+00
-5.16189754e-01 8.15599978e-01 2.09033534e-01 -5.21692872e-01
-2.92785406e-01 -6.40955687e-01 -1.34083688e-01 -1.28500819e+00
-2.30139732e-01 6.10998809e-01 -2.96531349e-01 6.13153130... | [15.138033866882324, -3.0931997299194336] |
b5dbf310-ed68-480b-81e6-91943b1326a2 | reformulation-of-matching-equation-in | 2112.08742 | null | https://arxiv.org/abs/2112.08742v1 | https://arxiv.org/pdf/2112.08742v1.pdf | Reformulation of Matching Equation in Potential Energy Shaping | Stabilization of an underactuated mechanical system may be accomplished by energy shaping. Interconnection and damping assignment passivity-based control is an approach based on total energy shaping by assigning desired kinetic and potential energy to the system. This method requires solving a partial differential equa... | ['Hamid D. Taghirad', 'M. Reza J. Harandi'] | 2021-12-16 | null | null | null | null | ['total-energy'] | ['miscellaneous'] | [ 7.61292577e-02 8.48541737e-01 -1.57685652e-01 5.92936039e-01
-1.55320108e-01 -6.62128150e-01 3.09927016e-01 1.13743916e-01
-3.19102913e-01 1.06007826e+00 -5.08248687e-01 -1.05673626e-01
-2.96546251e-01 -4.23133194e-01 -5.17061532e-01 -9.34551716e-01
2.50407368e-01 1.37585536e-01 -1.11669816e-01 -6.56100810... | [5.477224826812744, 2.653379440307617] |
60bd6ef8-9eab-487d-b091-02afe8e66a3d | nonnegative-tensor-factorization-for | 1411.501 | null | http://arxiv.org/abs/1411.5010v2 | http://arxiv.org/pdf/1411.5010v2.pdf | Nonnegative Tensor Factorization for Directional Blind Audio Source Separation | We augment the nonnegative matrix factorization method for audio source
separation with cues about directionality of sound propagation. This improves
separation quality greatly and removes the need for training data, with only a
twofold increase in run time. This is the first method which can exploit
directional inform... | ['Noah D. Stein'] | 2014-11-18 | null | null | null | null | ['audio-source-separation'] | ['audio'] | [ 1.16564007e-02 -8.14400613e-02 4.51698601e-01 6.08778000e-02
-9.44777668e-01 -9.03107285e-01 8.56081396e-02 -1.82373554e-01
-3.00359786e-01 5.92040360e-01 4.02328819e-01 -7.91324973e-01
-2.02968821e-01 -5.47834635e-01 -4.13578413e-02 -9.55520809e-01
-5.59695303e-01 -2.48204712e-02 1.45006567e-01 -6.58477619... | [15.20190715789795, 5.633871555328369] |
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