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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
4d641808-c9b7-4afe-a403-ae299faab190 | t2v-ddpm-thermal-to-visible-face-translation | 2209.08814 | null | https://arxiv.org/abs/2209.08814v1 | https://arxiv.org/pdf/2209.08814v1.pdf | T2V-DDPM: Thermal to Visible Face Translation using Denoising Diffusion Probabilistic Models | Modern-day surveillance systems perform person recognition using deep learning-based face verification networks. Most state-of-the-art facial verification systems are trained using visible spectrum images. But, acquiring images in the visible spectrum is impractical in scenarios of low-light and nighttime conditions, a... | ['Vishal M. Patel', 'Nithin Gopalakrishnan Nair'] | 2022-09-19 | null | null | null | null | ['person-recognition', 'thermal-image-denoising'] | ['computer-vision', 'computer-vision'] | [ 4.68384236e-01 -4.61224884e-01 8.80284160e-02 -4.52557027e-01
-7.14738846e-01 -3.29704076e-01 6.72367871e-01 -7.33829021e-01
-2.09566593e-01 4.35336322e-01 -3.19891065e-01 -3.13895524e-01
2.21401509e-02 -7.48710155e-01 -6.21773541e-01 -1.44418371e+00
5.68770170e-01 2.37758994e-01 -3.53719532e-01 1.02239721... | [12.978127479553223, 0.24516700208187103] |
ddf17061-4251-4b23-a430-b35c587f448f | an-empirical-comparison-of-unsupervised | null | null | https://aclanthology.org/2020.acl-main.300 | https://aclanthology.org/2020.acl-main.300.pdf | An Empirical Comparison of Unsupervised Constituency Parsing Methods | Unsupervised constituency parsing aims to learn a constituency parser from a training corpus without parse tree annotations. While many methods have been proposed to tackle the problem, including statistical and neural methods, their experimental results are often not directly comparable due to discrepancies in dataset... | ['Kewei Tu', 'Jun Li', 'Jiong Cai', 'Yong Jiang', 'Yifan Cao'] | 2020-07-01 | null | null | null | acl-2020-6 | ['constituency-parsing'] | ['natural-language-processing'] | [ 1.61877215e-01 1.96923271e-01 -5.55241883e-01 -7.63832092e-01
-1.07108986e+00 -9.01976824e-01 5.08626223e-01 3.36411804e-01
-5.97441673e-01 9.86851156e-01 5.64600587e-01 -6.69667900e-01
3.04803759e-01 -8.22531581e-01 -3.49400431e-01 -3.79314423e-01
1.02402167e-02 3.19729924e-01 3.06160748e-01 -2.82624304... | [10.347565650939941, 9.718179702758789] |
b37f8e48-507e-4c7a-9777-324ceeaa5e79 | application-of-the-ring-theory-in-the | 1402.4069 | null | http://arxiv.org/abs/1402.4069v2 | http://arxiv.org/pdf/1402.4069v2.pdf | Application of the Ring Theory in the Segmentation of Digital Images | Ring theory is one of the branches of the abstract algebra that has been
broadly used in images. However, ring theory has not been very related with
image segmentation. In this paper, we propose a new index of similarity among
images using Zn rings and the entropy function. This new index was applied as a
new stopping ... | ['Roberto Rodríguez', 'Esley Torres', 'Yasel Garcés', 'Osvaldo Pereira'] | 2014-02-17 | null | null | null | null | ['abstract-algebra'] | ['reasoning'] | [ 3.56922418e-01 3.06843966e-01 -9.92464274e-02 -1.21492138e-02
3.34987223e-01 -2.26817712e-01 5.65274894e-01 2.59185821e-01
-7.84050643e-01 5.65107882e-01 -3.45046192e-01 -8.99045467e-02
-6.09070122e-01 -9.59974706e-01 -1.07265808e-01 -6.91125989e-01
-1.76658556e-01 6.26588911e-02 4.70079064e-01 -3.65770936... | [10.64572811126709, -1.9887394905090332] |
b81f8333-0358-473a-a3e9-1a3661c75e46 | learn-from-all-erasing-attention-consistency | 2207.10299 | null | https://arxiv.org/abs/2207.10299v2 | https://arxiv.org/pdf/2207.10299v2.pdf | Learn From All: Erasing Attention Consistency for Noisy Label Facial Expression Recognition | Noisy label Facial Expression Recognition (FER) is more challenging than traditional noisy label classification tasks due to the inter-class similarity and the annotation ambiguity. Recent works mainly tackle this problem by filtering out large-loss samples. In this paper, we explore dealing with noisy labels from a ne... | ['Weihong Deng', 'Xu Ling', 'Chengrui Wang', 'Yuhang Zhang'] | 2022-07-21 | null | null | null | null | ['facial-expression-recognition', 'learning-with-noisy-labels', 'learning-with-noisy-labels'] | ['computer-vision', 'computer-vision', 'natural-language-processing'] | [ 3.22599977e-01 1.54472426e-01 -9.23988745e-02 -9.03527677e-01
-9.98947203e-01 -4.17677350e-02 1.07080065e-01 -2.78017402e-01
-4.26968336e-01 8.45004618e-01 1.70652747e-01 3.33954573e-01
4.36679348e-02 -2.68683910e-01 -7.14021564e-01 -9.54894841e-01
3.06056112e-01 2.08316907e-01 -2.88167030e-01 2.04842836... | [13.596126556396484, 1.6806957721710205] |
60962144-19f2-4d9f-8505-38392c955204 | relation-classification-via-relation | null | null | https://aclanthology.org/2021.semdeep-1.4 | https://aclanthology.org/2021.semdeep-1.4.pdf | Relation Classification via Relation Validation | null | ['Brigitte Grau', 'Antoine Doucet', 'José G. Moreno'] | null | null | null | null | semdeep-2021-1 | ['relation-classification'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.385937213897705, 3.710333824157715] |
7d0f60b6-e1a4-491a-b112-fd68f1adae5e | comparing-deep-learning-strategies-for-paired | 2101.06979 | null | https://arxiv.org/abs/2101.06979v1 | https://arxiv.org/pdf/2101.06979v1.pdf | Comparing Deep Learning strategies for paired but unregistered multimodal segmentation of the liver in T1 and T2-weighted MRI | We address the problem of multimodal liver segmentation in paired but unregistered T1 and T2-weighted MR images. We compare several strategies described in the literature, with or without multi-task training, with or without pre-registration. We also compare different loss functions (cross-entropy, Dice loss, and three... | ['Isabelle Bloch', 'Laurent Milot', 'Pierre-Jean Valette', 'Anna Sesilia Vlachomitrou', 'Guillaume Pizaine', 'Olivier Nempont', 'Mathilde Trintignac', 'Vincent Couteaux'] | 2021-01-18 | null | null | null | null | ['liver-segmentation'] | ['medical'] | [ 1.99103251e-01 4.96226326e-02 1.59114093e-01 -3.21426600e-01
-1.46318257e+00 -7.61902869e-01 4.63437676e-01 3.19901824e-01
-8.60016346e-01 7.49024212e-01 2.06350908e-01 -2.54290521e-01
-2.79554009e-01 -2.01338872e-01 -2.96735644e-01 -1.03442597e+00
-3.50652903e-01 8.21368456e-01 3.21354121e-01 6.25378862... | [14.133427619934082, -2.3156580924987793] |
c9d7a239-d43e-4c34-b3be-b2746245f88b | semantic-embedding-space-for-zero-shot-action | 1502.01540 | null | http://arxiv.org/abs/1502.01540v1 | http://arxiv.org/pdf/1502.01540v1.pdf | Semantic Embedding Space for Zero-Shot Action Recognition | The number of categories for action recognition is growing rapidly. It is
thus becoming increasingly hard to collect sufficient training data to learn
conventional models for each category. This issue may be ameliorated by the
increasingly popular 'zero-shot learning' (ZSL) paradigm. In this framework a
mapping is cons... | ['Timothy Hospedales', 'Xun Xu', 'Shaogang Gong'] | 2015-02-05 | null | null | null | null | ['zero-shot-action-recognition'] | ['computer-vision'] | [ 6.76437736e-01 -8.06236416e-02 -4.36641932e-01 -4.85697240e-01
-5.84290504e-01 -2.92783767e-01 8.19067657e-01 3.89941363e-03
-3.63911062e-01 4.48882848e-01 4.93168771e-01 5.99003769e-02
-5.47546558e-02 -5.04724443e-01 -4.67827111e-01 -6.04243875e-01
6.92424327e-02 5.29867932e-02 3.32170516e-01 -3.30797359... | [8.562767028808594, 0.963975191116333] |
ce56014b-e9d1-49a6-92b6-ce40173d560e | hierarchical-deep-learning-classification-of | 2009.00542 | null | https://arxiv.org/abs/2009.00542v1 | https://arxiv.org/pdf/2009.00542v1.pdf | Hierarchical Deep Learning Classification of Unstructured Pathology Reports to Automate ICD-O Morphology Grading | Timely cancer reporting data are required in order to understand the impact of cancer, inform public health resource planning and implement cancer policy especially in Sub Saharan Africa where the reporting lag is behind world averages. Unstructured pathology reports, which contain tumor specific data, are the main sou... | ['Tapiwa Chiwewe', 'Waheeda Saib', 'Elvira Singh'] | 2020-08-28 | null | null | null | null | ['morphology-classification'] | ['computer-vision'] | [ 2.07190942e-02 3.20624799e-01 -6.78168416e-01 -3.79175395e-01
-1.19376743e+00 -7.26917028e-01 3.23521972e-01 1.17368698e+00
-7.54138589e-01 9.02039766e-01 8.43104303e-01 -9.66332495e-01
-1.14066988e-01 -1.04213035e+00 -3.06375980e-01 -6.61855996e-01
-3.03114410e-02 7.51088083e-01 -3.22081745e-01 1.57271594... | [15.121009826660156, -2.988513469696045] |
f985eb9b-4bd9-4b47-9544-e71f3c4e2453 | repeated-random-sampling-for-minimizing-the | 2305.18424 | null | https://arxiv.org/abs/2305.18424v1 | https://arxiv.org/pdf/2305.18424v1.pdf | Repeated Random Sampling for Minimizing the Time-to-Accuracy of Learning | Methods for carefully selecting or generating a small set of training data to learn from, i.e., data pruning, coreset selection, and data distillation, have been shown to be effective in reducing the ever-increasing cost of training neural networks. Behind this success are rigorously designed strategies for identifying... | ['Theodoros Rekatsinas', 'Nezihe Merve Gürel', 'Dionysis Kalogerias', 'Amin Karbasi', 'Konstantinos E. Nikolakakis', 'Vasilis Mageirakos', 'Roger Waleffe', 'Patrik Okanovic'] | 2023-05-28 | null | null | null | null | ['data-compression'] | ['time-series'] | [ 5.12485325e-01 3.56937237e-02 -4.54581469e-01 -5.76169789e-01
-7.22125709e-01 -2.14123040e-01 5.18986881e-01 1.41521126e-01
-1.06239891e+00 8.52471471e-01 -2.58166701e-01 -4.97846603e-01
-3.48935515e-01 -8.22862744e-01 -8.85509610e-01 -5.51939726e-01
-5.88108636e-02 6.30955458e-01 2.48928681e-01 1.42161235... | [8.718767166137695, 3.315135955810547] |
61c10703-85e4-4e3a-97cb-610a882fcda8 | simmim-a-simple-framework-for-masked-image | 2111.09886 | null | https://arxiv.org/abs/2111.09886v2 | https://arxiv.org/pdf/2111.09886v2.pdf | SimMIM: A Simple Framework for Masked Image Modeling | This paper presents SimMIM, a simple framework for masked image modeling. We simplify recently proposed related approaches without special designs such as block-wise masking and tokenization via discrete VAE or clustering. To study what let the masked image modeling task learn good representations, we systematically st... | ['Han Hu', 'Qi Dai', 'Zhuliang Yao', 'Jianmin Bao', 'Yutong Lin', 'Yue Cao', 'Zheng Zhang', 'Zhenda Xie'] | 2021-11-18 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Xie_SimMIM_A_Simple_Framework_for_Masked_Image_Modeling_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Xie_SimMIM_A_Simple_Framework_for_Masked_Image_Modeling_CVPR_2022_paper.pdf | cvpr-2022-1 | ['self-supervised-image-classification'] | ['computer-vision'] | [ 3.80856663e-01 4.15933013e-01 -3.45038056e-01 -2.62678385e-01
-9.54973340e-01 -2.77348846e-01 4.94179845e-01 -4.40871775e-01
-4.03917432e-01 5.63405871e-01 6.26563728e-02 -6.20608628e-01
4.19418991e-01 -5.36917150e-01 -1.42993307e+00 -8.68131757e-01
-9.57894400e-02 8.46312419e-02 3.66825402e-01 -1.64196238... | [9.589101791381836, 1.0620955228805542] |
57602f43-3bdd-4226-b3f9-71f40b189a71 | fdnerf-few-shot-dynamic-neural-radiance | 2208.05751 | null | https://arxiv.org/abs/2208.05751v2 | https://arxiv.org/pdf/2208.05751v2.pdf | FDNeRF: Few-shot Dynamic Neural Radiance Fields for Face Reconstruction and Expression Editing | We propose a Few-shot Dynamic Neural Radiance Field (FDNeRF), the first NeRF-based method capable of reconstruction and expression editing of 3D faces based on a small number of dynamic images. Unlike existing dynamic NeRFs that require dense images as input and can only be modeled for a single identity, our method ena... | ['Jing Liao', 'Can Wang', 'Ziyu Wan', 'Xiaoyu Li', 'Jingbo Zhang'] | 2022-08-11 | null | null | null | null | ['3d-face-reconstruction', 'face-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 3.02325338e-01 -3.09098139e-02 2.96139896e-01 -6.75779462e-01
-2.76433676e-01 -4.34529692e-01 7.33227849e-01 -8.88902068e-01
-6.64889440e-03 4.27212566e-01 1.99983731e-01 3.11782718e-01
1.18237369e-01 -8.48817825e-01 -7.34951138e-01 -6.14132643e-01
3.50273371e-01 2.40084529e-01 -3.13404232e-01 -4.49039847... | [12.794248580932617, -0.3537129759788513] |
426449ec-f754-4cf0-aded-139ea86f2bc3 | streaming-speech-to-confusion-network-speech | 2306.03778 | null | https://arxiv.org/abs/2306.03778v1 | https://arxiv.org/pdf/2306.03778v1.pdf | Streaming Speech-to-Confusion Network Speech Recognition | In interactive automatic speech recognition (ASR) systems, low-latency requirements limit the amount of search space that can be explored during decoding, particularly in end-to-end neural ASR. In this paper, we present a novel streaming ASR architecture that outputs a confusion network while maintaining limited latenc... | ['Andreas Stolcke', 'Ankur Gandhe', 'Ariya Rastrow', 'Prabhat Pandey', 'Denis Filimonov'] | 2023-06-02 | null | null | null | null | ['automatic-speech-recognition'] | ['speech'] | [ 5.22220373e-01 4.15795952e-01 1.39140561e-01 -4.48722720e-01
-1.47292721e+00 -5.20299792e-01 3.00958484e-01 -4.42668200e-01
-7.46568024e-01 4.58174497e-01 4.79468137e-01 -1.05597544e+00
2.80843198e-01 1.59260616e-01 -4.77841914e-01 -3.94331992e-01
1.15761213e-01 6.83765113e-01 2.68082261e-01 -5.25442481... | [14.412018775939941, 6.819261074066162] |
e52ef8da-f1a8-4617-9b3a-390161844677 | query-reduction-networks-for-question | 1606.04582 | null | http://arxiv.org/abs/1606.04582v6 | http://arxiv.org/pdf/1606.04582v6.pdf | Query-Reduction Networks for Question Answering | In this paper, we study the problem of question answering when reasoning over
multiple facts is required. We propose Query-Reduction Network (QRN), a variant
of Recurrent Neural Network (RNN) that effectively handles both short-term
(local) and long-term (global) sequential dependencies to reason over multiple
facts. Q... | ['Ali Farhadi', 'Sewon Min', 'Minjoon Seo', 'Hannaneh Hajishirzi'] | 2016-06-14 | null | null | null | null | ['goal-oriented-dialog'] | ['natural-language-processing'] | [ 2.92668611e-01 3.58011603e-01 -6.13097399e-02 -7.00165093e-01
-1.31686842e+00 -7.79597223e-01 6.23175740e-01 4.76943329e-02
-5.08565962e-01 9.25900042e-01 6.11838639e-01 -7.80793846e-01
-1.35905936e-01 -7.55552649e-01 -6.63971066e-01 -2.10161120e-01
1.61266197e-02 9.07565057e-01 4.27428186e-01 -9.06708479... | [11.915910720825195, 7.911200046539307] |
fe2b6175-d886-4417-bc3e-0b428eda3201 | towards-robust-object-detection-bayesian | 2108.00784 | null | https://arxiv.org/abs/2108.00784v2 | https://arxiv.org/pdf/2108.00784v2.pdf | Towards Robust Object Detection: Bayesian RetinaNet for Homoscedastic Aleatoric Uncertainty Modeling | According to recent studies, commonly used computer vision datasets contain about 4% of label errors. For example, the COCO dataset is known for its high level of noise in data labels, which limits its use for training robust neural deep architectures in a real-world scenario. To model such a noise, in this paper we ha... | ['Andrey Filchenkov', 'Alexey Lapenok', 'Natalia Khanzhina'] | 2021-08-02 | null | null | null | null | ['robust-object-detection'] | ['computer-vision'] | [ 4.72230986e-02 1.01600252e-01 3.41820955e-01 -5.20276368e-01
-4.83753741e-01 -2.59559870e-01 7.06364930e-01 1.29863471e-01
-8.96547198e-01 7.09835052e-01 -3.40141207e-01 -4.85050231e-02
-4.04088616e-01 -6.29610538e-01 -1.01890159e+00 -6.54448986e-01
1.25808403e-01 5.81274867e-01 4.79161203e-01 2.94202477... | [8.544500350952148, 1.8577994108200073] |
38aa9475-01f5-4b91-93d2-66f4f524253f | spatial-correlation-and-value-prediction-in | 1807.10598 | null | http://arxiv.org/abs/1807.10598v2 | http://arxiv.org/pdf/1807.10598v2.pdf | Spatial Correlation and Value Prediction in Convolutional Neural Networks | Convolutional neural networks (CNNs) are a widely used form of deep neural
networks, introducing state-of-the-art results for different problems such as
image classification, computer vision tasks, and speech recognition. However,
CNNs are compute intensive, requiring billions of multiply-accumulate (MAC)
operations pe... | ['Uri Weiser', 'Gil Shomron'] | 2018-07-21 | null | null | null | null | ['value-prediction'] | ['computer-code'] | [-6.60303682e-02 -2.67652512e-01 -2.54483491e-01 -6.87160730e-01
1.45114260e-02 -1.06738113e-01 1.50022000e-01 1.38126656e-01
-1.10194027e+00 3.73878956e-01 -4.13243771e-01 -7.86271513e-01
4.02354926e-01 -9.95858967e-01 -7.54437685e-01 -4.26413536e-01
-7.14954287e-02 -5.60155034e-01 5.73656797e-01 -8.18588883... | [8.52412223815918, 2.8941259384155273] |
53da635f-2d4b-4a53-aa20-76131c14b99e | what-do-the-us-west-coast-public-libraries | 1808.06021 | null | http://arxiv.org/abs/1808.06021v2 | http://arxiv.org/pdf/1808.06021v2.pdf | What do the US West Coast Public Libraries Post on Twitter? | Twitter has provided a great opportunity for public libraries to disseminate
information for a variety of purposes. Twitter data have been applied in
different domains such as health, politics, and history. There are thousands of
public libraries in the US, but no study has yet investigated the content of
their social ... | ['Matthew Collins', 'Amir Karami'] | 2018-08-17 | null | null | null | null | ['public-relations'] | ['miscellaneous'] | [-5.69672108e-01 -9.54945164e-04 -3.45574379e-01 -2.21703127e-01
-7.60484338e-01 -6.63800716e-01 9.89433825e-01 9.69805896e-01
-6.14911318e-01 8.07998657e-01 7.49707878e-01 -5.15732110e-01
1.39706999e-01 -1.08885014e+00 -1.17723711e-01 -5.14354706e-01
2.16741979e-01 3.77837211e-01 4.09670323e-01 -4.56891090... | [10.5614013671875, 7.087311267852783] |
9d366f40-7f22-4d94-a7ef-41586dd01ebf | one-shot-segmentation-of-novel-white-matter | 2303.06852 | null | https://arxiv.org/abs/2303.06852v1 | https://arxiv.org/pdf/2303.06852v1.pdf | One-Shot Segmentation of Novel White Matter Tracts via Extensive Data Augmentation | Deep learning based methods have achieved state-of-the-art performance for automated white matter (WM) tract segmentation. In these methods, the segmentation model needs to be trained with a large number of manually annotated scans, which can be accumulated throughout time. When novel WM tracts, i.e., tracts not includ... | ['Chuyang Ye', 'Yaou Liu', 'Zhizheng Zhuo', 'Qi Lu', 'Wan Liu'] | 2023-03-13 | null | null | null | null | ['one-shot-segmentation'] | ['computer-vision'] | [ 3.63337040e-01 2.13266790e-01 -1.03474297e-01 -3.64055812e-01
-7.64152586e-01 -5.98998904e-01 1.60850704e-01 -1.05577800e-02
-8.49038363e-01 9.82409060e-01 7.40713477e-02 -1.73157558e-01
1.45540863e-01 -8.50699186e-01 -6.64091766e-01 -5.86817861e-01
-3.93275209e-02 6.58396900e-01 7.14733243e-01 2.00192019... | [14.544893264770508, -2.104801654815674] |
3c4633ae-9e9f-4a8a-b4c3-bcfd09339b38 | relation-aware-collaborative-learning-for | null | null | https://aclanthology.org/2020.acl-main.340 | https://aclanthology.org/2020.acl-main.340.pdf | Relation-Aware Collaborative Learning for Unified Aspect-Based Sentiment Analysis | Aspect-based sentiment analysis (ABSA) involves three subtasks, i.e., aspect term extraction, opinion term extraction, and aspect-level sentiment classification. Most existing studies focused on one of these subtasks only. Several recent researches made successful attempts to solve the complete ABSA problem with a unif... | ['Tieyun Qian', 'Zhuang Chen'] | 2020-07-01 | null | null | null | acl-2020-6 | ['aspect-term-extraction-and-sentiment'] | ['natural-language-processing'] | [ 2.19457522e-01 -2.46718749e-02 -3.16192895e-01 -6.66098475e-01
-8.34214032e-01 -6.12168431e-01 4.05027181e-01 4.96606886e-01
-2.03160420e-01 3.55907768e-01 4.06585895e-02 -4.48845297e-01
-2.55688012e-01 -9.49449599e-01 -7.62506425e-01 -6.46659434e-01
-8.68518278e-03 2.08737046e-01 1.39257535e-01 -5.03840327... | [11.467026710510254, 6.6515045166015625] |
fcd2bcbf-44b5-465a-a41e-48aa05e27e19 | combining-contrastive-learning-and-knowledge | 2211.05035 | null | https://arxiv.org/abs/2211.05035v1 | https://arxiv.org/pdf/2211.05035v1.pdf | Combining Contrastive Learning and Knowledge Graph Embeddings to develop medical word embeddings for the Italian language | Word embeddings play a significant role in today's Natural Language Processing tasks and applications. While pre-trained models may be directly employed and integrated into existing pipelines, they are often fine-tuned to better fit with specific languages or domains. In this paper, we attempt to improve available embe... | ['Luigi di Caro', 'Roger Ferrod', 'Denys Amore Bondarenko'] | 2022-11-09 | null | null | null | null | ['knowledge-graph-embedding', 'knowledge-graph-embeddings', 'knowledge-graph-embeddings'] | ['graphs', 'graphs', 'methodology'] | [ 1.39012679e-01 3.49212706e-01 -1.39391392e-01 -1.68577582e-01
-5.51684320e-01 -4.08838511e-01 8.43104780e-01 1.00660360e+00
-1.04096448e+00 5.89336097e-01 3.73795509e-01 -3.18740308e-01
-3.54580671e-01 -7.44324028e-01 -3.00946444e-01 -4.97191876e-01
-1.02865018e-01 8.66851389e-01 4.19399381e-01 -5.46472788... | [8.848169326782227, 8.693984985351562] |
d4e2ca60-5de5-47a6-8268-ab44efcefe9c | rclane-relay-chain-prediction-for-lane | 2207.09399 | null | https://arxiv.org/abs/2207.09399v1 | https://arxiv.org/pdf/2207.09399v1.pdf | RCLane: Relay Chain Prediction for Lane Detection | Lane detection is an important component of many real-world autonomous systems. Despite a wide variety of lane detection approaches have been proposed, reporting steady benchmark improvements over time, lane detection remains a largely unsolved problem. This is because most of the existing lane detection methods either... | ['xiangyang xue', 'Yanwei Fu', 'Hang Xu', 'Li Zhang', 'Bin Zhao', 'Xinyue Cai', 'Shenghua Xu'] | 2022-07-19 | null | null | null | null | ['lane-detection'] | ['computer-vision'] | [ 1.84449583e-01 -5.09426109e-02 -3.92229140e-01 -3.41575474e-01
-3.35451245e-01 -6.44947052e-01 5.93653083e-01 -9.24463719e-02
-7.40784630e-02 6.91235721e-01 -1.49073079e-01 -6.89806819e-01
2.27480784e-01 -8.70861948e-01 -6.21713877e-01 -7.34247029e-01
-2.27870479e-01 4.42449123e-01 1.22973406e+00 -2.37167150... | [8.080198287963867, -1.576707124710083] |
8d1a7d3b-04b1-4866-9988-9441ce0b932a | a-multi-domain-vne-algorithm-based-on-load | 2202.05667 | null | https://arxiv.org/abs/2202.05667v1 | https://arxiv.org/pdf/2202.05667v1.pdf | A Multi-Domain VNE Algorithm based on Load Balancing in the IoT networks | Virtual network embedding is one of the key problems of network virtualization. Since virtual network mapping is an NP-hard problem, a lot of research has focused on the evolutionary algorithm's masterpiece genetic algorithm. However, the parameter setting in the traditional method is too dependent on experience, and i... | ['Joan Serrat-Fernacute', 'Juan-Luis Gorricho', 'Abderrahim Benslimane', 'Chunxiao Jiang', 'Fanglin Liu', 'Peiying Zhang'] | 2022-02-07 | null | null | null | null | ['network-embedding'] | ['methodology'] | [ 4.02891897e-02 -3.59747708e-01 -2.82680631e-01 4.44541276e-02
5.62238693e-01 -1.45162970e-01 -1.31539166e-01 1.63239404e-03
-3.36841404e-01 9.82594252e-01 -5.73037684e-01 -4.27468985e-01
-7.97661662e-01 -1.26639903e+00 4.11709324e-02 -7.94624031e-01
-1.57465652e-01 5.39014876e-01 5.63521683e-01 -4.26113039... | [5.862939834594727, 1.7353507280349731] |
593c4207-eccf-472b-a9c2-1938dc38a30d | few-shot-action-recognition-via-improved | 2001.03905 | null | https://arxiv.org/abs/2001.03905v3 | https://arxiv.org/pdf/2001.03905v3.pdf | Few-shot Action Recognition with Permutation-invariant Attention | Many few-shot learning models focus on recognising images. In contrast, we tackle a challenging task of few-shot action recognition from videos. We build on a C3D encoder for spatio-temporal video blocks to capture short-range action patterns. Such encoded blocks are aggregated by permutation-invariant pooling to make ... | ['Philip H. S. Torr', 'Xiaojuan Qi', 'Li Zhang', 'Hongguang Zhang', 'Piotr Koniusz', 'Hongdong Li'] | 2020-01-12 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/3831_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123500511.pdf | eccv-2020-8 | ['few-shot-action-recognition'] | ['computer-vision'] | [ 5.93970776e-01 -2.54820436e-01 -4.43443805e-01 -3.64460260e-01
-6.94534004e-01 -4.05194789e-01 6.64884448e-01 7.46820420e-02
-5.81192255e-01 4.69007045e-01 7.00698435e-01 4.84468192e-01
-3.09796125e-01 -3.63661259e-01 -9.83253419e-01 -7.93882012e-01
-5.56357563e-01 -2.18311742e-01 7.08112955e-01 1.09424993... | [8.434435844421387, 0.7019134759902954] |
97255f14-968b-4be2-bfce-6f292f9a1491 | hybrid-machine-learning-model-of-extreme | 1910.13574 | null | https://arxiv.org/abs/1910.13574v1 | https://arxiv.org/pdf/1910.13574v1.pdf | Hybrid Machine Learning Model of Extreme Learning Machine Radial basis function for Breast Cancer Detection and Diagnosis; a Multilayer Fuzzy Expert System | Mammography is often used as the most common laboratory method for the detection of breast cancer, yet associated with the high cost and many side effects. Machine learning prediction as an alternative method has shown promising results. This paper presents a method based on a multilayer fuzzy expert system for the det... | ['Laszlo Nadai', 'Narjes Nabipour', 'Javad Hassannataj Joloudari', 'Gergo Pinter', 'Amir Mosavi', 'Sanaz Mojrian', 'Imre Felde'] | 2019-10-29 | null | null | null | null | ['breast-cancer-detection', 'breast-cancer-detection'] | ['knowledge-base', 'medical'] | [-7.52292126e-02 -7.15370625e-02 -1.46938741e-01 -3.66804034e-01
4.64814194e-02 2.22884566e-02 2.67128795e-01 5.69434941e-01
-4.55778718e-01 9.91377890e-01 -6.26011610e-01 -6.22842610e-01
-6.44022048e-01 -7.58495033e-01 -9.24880430e-02 -6.42721236e-01
1.05709657e-01 3.81048352e-01 3.16524893e-01 -2.33633801... | [8.399444580078125, 4.831643581390381] |
f71e5568-7e2c-4166-9bdf-48af818eae0c | deep-transformation-invariant-clustering | 2006.11132 | null | https://arxiv.org/abs/2006.11132v2 | https://arxiv.org/pdf/2006.11132v2.pdf | Deep Transformation-Invariant Clustering | Recent advances in image clustering typically focus on learning better deep representations. In contrast, we present an orthogonal approach that does not rely on abstract features but instead learns to predict image transformations and performs clustering directly in image space. This learning process naturally fits in... | ['Thibault Groueix', 'Mathieu Aubry', 'Tom Monnier'] | 2020-06-19 | null | http://proceedings.neurips.cc/paper/2020/hash/5a5eab21ca2a8fef4af5e35709ecca15-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/5a5eab21ca2a8fef4af5e35709ecca15-Paper.pdf | neurips-2020-12 | ['image-clustering', 'unsupervised-image-classification'] | ['computer-vision', 'computer-vision'] | [ 7.25253746e-02 -1.87559947e-01 2.08594248e-01 -5.52676737e-01
-6.14540815e-01 -7.55640924e-01 1.10211062e+00 2.08450511e-01
-5.34760594e-01 1.18223853e-01 9.64644179e-03 -8.77023339e-02
-3.39934587e-01 -5.67554474e-01 -6.91556513e-01 -1.04815614e+00
-1.31123457e-02 5.84768116e-01 -1.09694906e-01 8.72167572... | [9.103089332580566, 3.0647506713867188] |
c47b3568-0d6f-4e37-9dc5-bc88c365b040 | c-3-compositional-counterfactual-constrastive | 2106.08914 | null | https://arxiv.org/abs/2106.08914v1 | https://arxiv.org/pdf/2106.08914v1.pdf | $C^3$: Compositional Counterfactual Constrastive Learning for Video-grounded Dialogues | Video-grounded dialogue systems aim to integrate video understanding and dialogue understanding to generate responses that are relevant to both the dialogue and video context. Most existing approaches employ deep learning models and have achieved remarkable performance, given the relatively small datasets available. Ho... | ['Steven C. H. Hoi', 'Nancy F. Chen', 'Hung Le'] | 2021-06-16 | null | null | null | null | ['dialogue-understanding'] | ['natural-language-processing'] | [ 4.28122699e-01 3.43923062e-01 -7.67454132e-02 -5.06485701e-01
-1.16079986e+00 -3.30721796e-01 1.21340477e+00 -3.09599847e-01
-2.80703187e-01 9.13119495e-01 9.27656353e-01 8.09631199e-02
2.66261011e-01 -5.32865465e-01 -7.86064267e-01 -4.90572751e-01
-6.65381029e-02 2.47512430e-01 -5.94059154e-02 -3.79397035... | [10.902583122253418, 0.7687861323356628] |
923ebd25-a002-4dec-87c9-bea68337069b | sadm-sequence-aware-diffusion-model-for | 2212.08228 | null | https://arxiv.org/abs/2212.08228v2 | https://arxiv.org/pdf/2212.08228v2.pdf | SADM: Sequence-Aware Diffusion Model for Longitudinal Medical Image Generation | Human organs constantly undergo anatomical changes due to a complex mix of short-term (e.g., heartbeat) and long-term (e.g., aging) factors. Evidently, prior knowledge of these factors will be beneficial when modeling their future state, i.e., via image generation. However, most of the medical image generation tasks on... | ['Xiaoxiao Li', 'Jia Guo', 'Heung-Il Suk', 'Chenghao Zhang', 'Jee Seok Yoon'] | 2022-12-16 | null | null | null | null | ['medical-image-generation'] | ['medical'] | [ 2.59590715e-01 -8.05433020e-02 -1.83416590e-01 -3.84596854e-01
-4.95493501e-01 -3.53579432e-01 7.69899964e-01 -2.63921231e-01
-1.98985219e-01 7.03207910e-01 3.67715895e-01 -1.73619732e-01
9.41617489e-02 -7.27118492e-01 -7.94719517e-01 -9.51073110e-01
-1.07843094e-01 3.38899046e-01 1.67007193e-01 3.29412781... | [13.890761375427246, -2.2595055103302] |
9828df56-d876-490c-b494-5977da631fcc | beta-r-cnn-looking-into-pedestrian-detection-1 | 2210.12758 | null | https://arxiv.org/abs/2210.12758v1 | https://arxiv.org/pdf/2210.12758v1.pdf | Beta R-CNN: Looking into Pedestrian Detection from Another Perspective | Recently significant progress has been made in pedestrian detection, but it remains challenging to achieve high performance in occluded and crowded scenes. It could be attributed mostly to the widely used representation of pedestrians, i.e., 2D axis-aligned bounding box, which just describes the approximate location an... | ['Anhong Dang', 'Ye Yuan', 'Banghuai Li', 'Zixuan Xu'] | 2022-10-23 | beta-r-cnn-looking-into-pedestrian-detection | http://proceedings.neurips.cc/paper/2020/hash/e6b4b2a746ed40e1af829d1fa82daa10-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/e6b4b2a746ed40e1af829d1fa82daa10-Paper.pdf | neurips-2020-12 | ['pedestrian-detection'] | ['computer-vision'] | [-2.53090113e-01 -2.16572523e-01 1.13803931e-01 -3.73753786e-01
-6.35459572e-02 -1.17510550e-01 4.00737911e-01 5.32925986e-02
-4.78758663e-01 6.84407115e-01 2.08470806e-01 8.74475837e-02
7.04916358e-01 -9.65366662e-01 -5.72308958e-01 -8.27871501e-01
8.72758776e-02 4.18732673e-01 8.41534734e-01 -2.05409005... | [8.075636863708496, -0.5647005438804626] |
c04fd09e-d109-4d9c-904a-54caf1ff9e91 | pwr-align-leveraging-part-whole-relationships | 2306.06717 | null | https://arxiv.org/abs/2306.06717v1 | https://arxiv.org/pdf/2306.06717v1.pdf | PWR-Align: Leveraging Part-Whole Relationships for Part-wise Rigid Point Cloud Registration in Mixed Reality Applications | We present an efficient and robust point cloud registration (PCR) workflow for part-wise rigid point cloud alignment using the Microsoft HoloLens 2. Point Cloud Registration (PCR) is an important problem in Augmented and Mixed Reality use cases, and we present a study for a special class of non-rigid transformations. M... | ['Bhaskar Banerjee', 'Manorama Jha'] | 2023-06-11 | null | null | null | null | ['point-cloud-registration', 'mixed-reality'] | ['computer-vision', 'computer-vision'] | [-8.95100646e-03 -3.09682600e-02 4.14931864e-01 -1.50109425e-01
-1.95522279e-01 -8.46698403e-01 7.47429848e-01 -5.79181351e-02
-2.43148535e-01 2.90113419e-01 -4.37501132e-01 1.74571164e-02
-4.48007166e-01 -5.24052441e-01 -9.87559021e-01 -4.20874745e-01
-1.07141025e-02 1.51285374e+00 3.57094109e-01 -7.55218387... | [7.728026866912842, -2.8783464431762695] |
3a549efc-7991-4f7a-b3e7-c9b56f8128c5 | quantized-dialog-language-model-for-goal | 1812.10356 | null | http://arxiv.org/abs/1812.10356v1 | http://arxiv.org/pdf/1812.10356v1.pdf | Quantized-Dialog Language Model for Goal-Oriented Conversational Systems | We propose a novel methodology to address dialog learning in the context of
goal-oriented conversational systems. The key idea is to quantize the dialog
space into clusters and create a language model across the clusters, thus
allowing for an accurate choice of the next utterance in the conversation. The
language model... | ['Jatin Ganhotra', 'R. Chulaka Gunasekara', 'Kshitij P. Fadnis', 'Lazaros C. Polymenakos', 'David Nahamoo'] | 2018-12-26 | null | null | null | null | ['goal-oriented-dialog', 'dialog-learning'] | ['natural-language-processing', 'natural-language-processing'] | [-2.90591061e-01 1.48006633e-01 6.74533173e-02 -7.97769308e-01
-8.54708254e-01 -5.37385225e-01 7.15701461e-01 4.12544340e-01
-4.23914641e-01 4.46708679e-01 6.07025683e-01 -2.80940235e-01
3.08821034e-02 -4.72501367e-01 2.12283537e-01 -4.32045549e-01
-9.89447385e-02 1.15002930e+00 1.88630372e-01 -8.25892150... | [12.7738618850708, 7.837454795837402] |
46862929-c901-4a11-b69e-704831f0983f | lightweight-and-scalable-particle-tracking | 1908.03775 | null | https://arxiv.org/abs/1908.03775v3 | https://arxiv.org/pdf/1908.03775v3.pdf | Lightweight and Scalable Particle Tracking and Motion Clustering of 3D Cell Trajectories | Tracking cell particles in 3D microscopy videos is a challenging task but is of great significance for modeling the motion of cells. Proper characterization of the cell's shape, evolution, and their movement over time is crucial to understanding and modeling the mechanobiology of cell migration in many diseases. One in... | ['Shannon Quinn', 'Mojtaba S. Fazli', 'BahaaEddin Alaila', 'Gary E. Ward', 'Stephen A. Vella', 'Silvia N. J. Moreno', 'Rachel V. Stadler'] | 2019-08-10 | null | null | null | null | ['cell-detection'] | ['computer-vision'] | [ 1.45006999e-01 -8.14202726e-01 4.95857857e-02 3.27619046e-01
7.79800117e-02 -7.86405444e-01 4.74879175e-01 6.11901283e-01
-5.19593358e-01 4.58259404e-01 -1.70894131e-01 -2.98411846e-01
-3.95746939e-02 -5.03311455e-01 -4.23937976e-01 -1.33568895e+00
-5.16462922e-01 9.88223076e-01 4.33887005e-01 3.31221282... | [14.261411666870117, -3.161133050918579] |
623e0caa-a61b-4d50-9347-a5a2321b78cb | ranking-based-autoencoder-for-extreme-multi | 1904.05937 | null | http://arxiv.org/abs/1904.05937v1 | http://arxiv.org/pdf/1904.05937v1.pdf | Ranking-Based Autoencoder for Extreme Multi-label Classification | Extreme Multi-label classification (XML) is an important yet challenging
machine learning task, that assigns to each instance its most relevant
candidate labels from an extremely large label collection, where the numbers of
labels, features and instances could be thousands or millions. XML is more and
more on demand in... | ['Kefeng Li', 'Wei Sun', 'Li Chen', 'Hui Zhou', 'Bingyu Wang', 'Kechen Qin'] | 2019-04-11 | ranking-based-autoencoder-for-extreme-multi-1 | https://aclanthology.org/N19-1289 | https://aclanthology.org/N19-1289.pdf | naacl-2019-6 | ['extreme-multi-label-classification'] | ['methodology'] | [ 1.29044592e-01 -2.50166327e-01 -6.07413836e-02 -6.58335567e-01
-9.77631450e-01 -2.80875474e-01 3.39728773e-01 4.07386124e-01
-5.96617997e-01 5.05596638e-01 2.73784876e-01 2.33313590e-01
-5.04506171e-01 -6.69736922e-01 -3.70898783e-01 -8.77112985e-01
2.92197466e-01 6.10605478e-01 2.84339488e-02 1.05766878... | [9.629064559936523, 4.403374671936035] |
f18d2a73-c6b5-4be4-a43a-15294b2df976 | a-case-based-reasoning-approach-for-answer | 1503.02917 | null | http://arxiv.org/abs/1503.02917v1 | http://arxiv.org/pdf/1503.02917v1.pdf | A Case Based Reasoning Approach for Answer Reranking in Question Answering | In this document I present an approach to answer validation and reranking for
question answering (QA) systems. A cased-based reasoning (CBR) system judges
answer candidates for questions from annotated answer candidates for earlier
questions. The promise of this approach is that user feedback will result in
improved an... | ['Karl-Heinz Weis'] | 2015-03-10 | null | null | null | null | ['graph-similarity'] | ['graphs'] | [-4.13693860e-02 8.29889417e-01 1.19897470e-01 -6.36135399e-01
-8.34477186e-01 -5.96994638e-01 5.37173271e-01 6.83890104e-01
-2.80894428e-01 7.40344107e-01 3.60736132e-01 -5.78024387e-01
-9.07597363e-01 -1.15397859e+00 -8.21820870e-02 1.65197641e-01
1.50142983e-01 1.11866498e+00 1.10501313e+00 -1.03023136... | [11.529135704040527, 8.059293746948242] |
bb8e211b-3917-4650-b7c1-687a0f5db6cd | deep-learning-on-home-drone-searching-for-the | 2209.11064 | null | https://arxiv.org/abs/2209.11064v1 | https://arxiv.org/pdf/2209.11064v1.pdf | Deep Learning on Home Drone: Searching for the Optimal Architecture | We suggest the first system that runs real-time semantic segmentation via deep learning on a weak micro-computer such as the Raspberry Pi Zero v2 (whose price was \$15) attached to a toy-drone. In particular, since the Raspberry Pi weighs less than $16$ grams, and its size is half of a credit card, we could easily atta... | ['Dan Feldman', 'Daniela Rus', 'Oren Gal', 'Barak Diker', 'Yotam Gurfinkel', 'Alaa Maalouf'] | 2022-09-21 | null | null | null | null | ['real-time-semantic-segmentation'] | ['computer-vision'] | [ 2.16981247e-02 1.07004359e-01 -1.16164833e-01 -5.60967736e-02
-3.49789679e-01 -4.69160289e-01 -2.91579306e-01 -2.45445758e-01
-7.04198718e-01 5.82056105e-01 -1.07804871e+00 -6.29982591e-01
-2.39570990e-01 -1.06523955e+00 -7.74532855e-01 -4.29320395e-01
-3.97279203e-01 7.63064742e-01 3.15789580e-01 -5.01553006... | [8.55170726776123, -0.9847840666770935] |
7c755b05-698b-485f-aa44-cf672b667583 | sc-transformer-structured-context-transformer | 2206.12634 | null | https://arxiv.org/abs/2206.12634v1 | https://arxiv.org/pdf/2206.12634v1.pdf | SC-Transformer++: Structured Context Transformer for Generic Event Boundary Detection | This report presents the algorithm used in the submission of Generic Event Boundary Detection (GEBD) Challenge at CVPR 2022. In this work, we improve the existing Structured Context Transformer (SC-Transformer) method for GEBD. Specifically, a transformer decoder module is added after transformer encoders to extract hi... | ['Longyin Wen', 'YuFei Wang', 'CongCong Li', 'Xinyao Wang', 'Xiaoqi Ma', 'Dexiang Hong'] | 2022-06-25 | null | null | null | null | ['boundary-detection'] | ['computer-vision'] | [ 2.56827265e-01 2.84207053e-02 -2.27010772e-01 -1.82003126e-01
-8.85015666e-01 -2.01768324e-01 5.97823322e-01 -4.56083529e-02
-3.72391611e-01 8.17222595e-01 3.82087648e-01 -4.77541834e-02
3.00824612e-01 -5.64255297e-01 -5.03586352e-01 -6.54070079e-01
2.16331705e-01 1.09132193e-01 7.14116871e-01 8.14246852... | [8.85196304321289, 0.2280692458152771] |
70314449-ec28-4a32-9495-0974b8e32b8c | nemo-3d-neural-motion-fields-from-multiple | 2212.13660 | null | https://arxiv.org/abs/2212.13660v1 | https://arxiv.org/pdf/2212.13660v1.pdf | NeMo: 3D Neural Motion Fields from Multiple Video Instances of the Same Action | The task of reconstructing 3D human motion has wideranging applications. The gold standard Motion capture (MoCap) systems are accurate but inaccessible to the general public due to their cost, hardware and space constraints. In contrast, monocular human mesh recovery (HMR) methods are much more accessible than MoCap as... | ['Serena Yeung', 'C. Karen Liu', 'Jeffrey Gu', 'Joao Pedro Araujo', 'Maria Xenochristou', 'Zhenzhen Weng', 'Kuan-Chieh Wang'] | 2022-12-28 | null | null | null | null | ['keypoint-detection', 'human-mesh-recovery'] | ['computer-vision', 'computer-vision'] | [-2.00663418e-01 -4.09084827e-01 -5.53668261e-01 2.62859076e-01
-8.55102658e-01 -6.51104927e-01 3.97164673e-01 -6.66427970e-01
-4.42353606e-01 4.07894701e-01 6.86637104e-01 1.69095203e-01
3.53183001e-01 -5.05767107e-01 -1.06405139e+00 -4.92790341e-01
-3.40512465e-03 3.32978815e-01 5.25717556e-01 -2.76058197... | [7.286233425140381, -0.6455467343330383] |
929fd8b1-8200-4d3f-9e82-307244664cb4 | hyperparameter-optimization-through-neural | 2304.14766 | null | https://arxiv.org/abs/2304.14766v1 | https://arxiv.org/pdf/2304.14766v1.pdf | Hyperparameter Optimization through Neural Network Partitioning | Well-tuned hyperparameters are crucial for obtaining good generalization behavior in neural networks. They can enforce appropriate inductive biases, regularize the model and improve performance -- especially in the presence of limited data. In this work, we propose a simple and efficient way for optimizing hyperparamet... | ['Christos Louizos', 'Matthias Reisser', 'Bruno Mlodozeniec'] | 2023-04-28 | null | null | null | null | ['hyperparameter-optimization'] | ['methodology'] | [ 5.74583001e-02 2.34678209e-01 -2.20796853e-01 -7.35994577e-01
-6.39042318e-01 -5.09885132e-01 1.80820212e-01 1.58967435e-01
-9.30339217e-01 9.33132350e-01 -3.69688869e-01 -2.53307521e-01
-3.43391031e-01 -9.77337182e-01 -1.18740463e+00 -9.35990334e-01
-1.02237158e-01 5.27963579e-01 4.48618986e-04 1.40546948... | [8.82492446899414, 3.5486268997192383] |
13bb233f-e96c-449c-9b7b-156a1ebfe371 | beyond-parallel-data-joint-word-alignment-and | null | null | https://aclanthology.org/D14-1061 | https://aclanthology.org/D14-1061.pdf | Beyond Parallel Data: Joint Word Alignment and Decipherment Improves Machine Translation | null | ['Qing Dou', 'Ashish Vaswani', 'Kevin Knight'] | 2014-10-01 | null | null | null | emnlp-2014-10 | ['decipherment'] | ['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... | [-7.274813652038574, 3.763646125793457] |
1d09b0fc-5ffc-4414-b6cd-e7dbc3360060 | multilingual-sequence-labeling-approach-to | null | null | https://aclanthology.org/2021.wnut-1.51 | https://aclanthology.org/2021.wnut-1.51.pdf | Multilingual Sequence Labeling Approach to solve Lexical Normalization | The task of converting a nonstandard text to a standard and readable text is known as lexical normalization. Almost all the Natural Language Processing (NLP) applications require the text data in normalized form to build quality task-specific models. Hence, lexical normalization has been proven to improve the performan... | ['Apurva Nagvenkar', 'Divesh Kubal'] | null | null | null | null | wnut-acl-2021-11 | ['lexical-normalization'] | ['natural-language-processing'] | [ 3.26221883e-01 2.16393024e-02 -5.99384420e-02 -5.18163800e-01
-8.12308550e-01 -4.18691665e-01 6.27483606e-01 5.02328873e-01
-1.06222034e+00 9.48289514e-01 3.94826919e-01 -2.35941902e-01
3.84790659e-01 -6.02778435e-01 -3.39446187e-01 -3.81612808e-01
7.70879149e-01 5.45666158e-01 4.79983054e-02 -6.34067059... | [10.211786270141602, 10.031354904174805] |
fae737bc-eb8a-41d7-a77b-18b5790b4256 | visual-concept-reasoning-networks | 2008.11783 | null | https://arxiv.org/abs/2008.11783v1 | https://arxiv.org/pdf/2008.11783v1.pdf | Visual Concept Reasoning Networks | A split-transform-merge strategy has been broadly used as an architectural constraint in convolutional neural networks for visual recognition tasks. It approximates sparsely connected networks by explicitly defining multiple branches to simultaneously learn representations with different visual concepts or properties. ... | ['Sungwoong Kim', 'Yoshua Bengio', 'Taesup Kim'] | 2020-08-26 | null | null | null | null | ['scene-recognition'] | ['computer-vision'] | [ 4.00127798e-01 1.45142451e-01 -3.48847300e-01 -7.47254550e-01
4.57683317e-02 -5.69139183e-01 8.51939142e-01 3.90939921e-01
-5.98146081e-01 2.21023306e-01 -2.17695490e-01 -1.66982129e-01
-2.49585509e-01 -7.89313376e-01 -7.13915050e-01 -5.65503478e-01
-5.68365306e-02 3.87725234e-01 7.37453401e-01 -4.04224917... | [9.655308723449707, 1.1407487392425537] |
38bcad6f-f672-449a-9a09-9a41be6a0f59 | heart-rate-variability-code-does-it-exist-and | 2001.08264 | null | https://arxiv.org/abs/2001.08264v4 | https://arxiv.org/pdf/2001.08264v4.pdf | Heart rate variability code: Does it exist and can we hack it? | Heart rate variability (HRV) has been studied for over 50 years, yet an integrative concept is missing on what HRV's mathematical properties represent physiologically. Here I introduce the notion of HRV code as an attempt to address this challenge systematically. I review the existing evidence from physiological studie... | ['Martin G. Frasch'] | 2020-01-22 | null | null | null | null | ['heart-rate-variability'] | ['medical'] | [ 1.35154545e-01 -2.97238350e-01 -3.93203437e-01 -4.80631620e-01
4.85578537e-01 -4.03122514e-01 7.01416796e-03 3.86314481e-01
-2.67661273e-01 9.11937714e-01 6.10028803e-02 -3.72697979e-01
-2.24234626e-01 -4.44522411e-01 2.46507540e-01 -5.62204778e-01
-5.84534824e-01 -1.94714189e-01 -3.92675310e-01 -3.41060042... | [13.981917381286621, 3.069995403289795] |
8dba3b03-4eaf-47b9-a99d-4922e1a3b8d8 | cross-modal-3d-shape-generation-and | 2207.11795 | null | https://arxiv.org/abs/2207.11795v1 | https://arxiv.org/pdf/2207.11795v1.pdf | Cross-Modal 3D Shape Generation and Manipulation | Creating and editing the shape and color of 3D objects require tremendous human effort and expertise. Compared to direct manipulation in 3D interfaces, 2D interactions such as sketches and scribbles are usually much more natural and intuitive for the users. In this paper, we propose a generic multi-modal generative mod... | ['Sergey Tulyakov', 'Subhransu Maji', 'Zeng Huang', 'Kyle Olszewski', 'Hsin-Ying Lee', 'Jian Ren', 'Menglei Chai', 'Zezhou Cheng'] | 2022-07-24 | null | null | null | null | ['3d-shape-generation'] | ['computer-vision'] | [ 4.92695987e-01 -4.26016450e-02 2.13563651e-01 -3.03548902e-01
-3.71438205e-01 -1.02768004e+00 9.93320644e-01 -4.08601165e-01
2.52389133e-01 4.42305267e-01 5.54881021e-02 -1.64988771e-01
-5.12447581e-02 -8.86599898e-01 -6.71135604e-01 -3.86322409e-01
4.17786270e-01 5.64293265e-01 3.58530506e-02 -1.88006401... | [9.078319549560547, -3.5066263675689697] |
b7ee04fe-9be4-4dc4-b140-e953c0b9bcd1 | tabular-data-deep-learning-is-not-all-you | 2106.03253 | null | https://arxiv.org/abs/2106.03253v2 | https://arxiv.org/pdf/2106.03253v2.pdf | Tabular Data: Deep Learning is Not All You Need | A key element in solving real-life data science problems is selecting the types of models to use. Tree ensemble models (such as XGBoost) are usually recommended for classification and regression problems with tabular data. However, several deep learning models for tabular data have recently been proposed, claiming to o... | ['Amitai Armon', 'Ravid Shwartz-Ziv'] | 2021-06-06 | null | https://openreview.net/forum?id=vdgtepS1pV | https://openreview.net/pdf?id=vdgtepS1pV | icml-workshop-automl-2021-7 | ['classification'] | ['methodology'] | [-7.15398610e-01 -6.56470433e-02 -7.06374049e-01 -6.77360117e-01
-4.30648327e-01 -3.51817310e-01 6.06970251e-01 4.20728475e-01
-1.19320258e-01 9.14939702e-01 3.11255842e-01 -9.14499998e-01
-8.88104200e-01 -1.29848409e+00 -6.68638825e-01 -6.81922734e-01
-2.12928981e-01 1.12119031e+00 -4.59015876e-01 -2.35990092... | [8.598123550415039, 4.126636028289795] |
faa5de2f-9d6e-45ee-9540-3dbbb68935c7 | rpvnet-a-deep-and-efficient-range-point-voxel | 2103.12978 | null | https://arxiv.org/abs/2103.12978v1 | https://arxiv.org/pdf/2103.12978v1.pdf | RPVNet: A Deep and Efficient Range-Point-Voxel Fusion Network for LiDAR Point Cloud Segmentation | Point clouds can be represented in many forms (views), typically, point-based sets, voxel-based cells or range-based images(i.e., panoramic view). The point-based view is geometrically accurate, but it is disordered, which makes it difficult to find local neighbors efficiently. The voxel-based view is regular, but spar... | ['ShiLiang Pu', 'Jie Sun', 'Yushi Zhu', 'Jian Dou', 'Ruixiang Zhang', 'Jianyun Xu'] | 2021-03-24 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Xu_RPVNet_A_Deep_and_Efficient_Range-Point-Voxel_Fusion_Network_for_LiDAR_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Xu_RPVNet_A_Deep_and_Efficient_Range-Point-Voxel_Fusion_Network_for_LiDAR_ICCV_2021_paper.pdf | iccv-2021-1 | ['robust-3d-semantic-segmentation'] | ['computer-vision'] | [-2.07826551e-02 -3.56171608e-01 -3.62983160e-02 -3.18058312e-01
-7.30212152e-01 -4.42642152e-01 3.04370284e-01 -2.42853582e-01
-3.09805069e-02 4.04672891e-01 -3.40978019e-02 -2.79248646e-03
-2.58681357e-01 -1.26309788e+00 -8.00746560e-01 -8.74354362e-01
3.54558706e-01 6.91984117e-01 6.97396994e-01 -5.88091426... | [8.59457015991211, -2.9691848754882812] |
03f88d94-86e2-42fb-b07d-bb2b831afd3a | revisiting-implicit-neural-representations-in | 2304.10250 | null | https://arxiv.org/abs/2304.10250v1 | https://arxiv.org/pdf/2304.10250v1.pdf | Revisiting Implicit Neural Representations in Low-Level Vision | Implicit Neural Representation (INR) has been emerging in computer vision in recent years. It has been shown to be effective in parameterising continuous signals such as dense 3D models from discrete image data, e.g. the neural radius field (NeRF). However, INR is under-explored in 2D image processing tasks. Considerin... | ['Jianbo Jiao', 'Wentian Xu'] | 2023-04-20 | null | null | null | null | ['deblurring'] | ['computer-vision'] | [ 5.97739637e-01 -4.23524380e-02 1.84990928e-01 -2.15803847e-01
-6.60400033e-01 -3.32513452e-02 5.48616588e-01 -1.57919526e-01
-4.47950214e-01 4.57021862e-01 4.87380743e-01 1.37252212e-02
-5.87226544e-03 -3.93744975e-01 -6.74066901e-01 -7.86452353e-01
-1.05748534e-01 -2.22206473e-01 3.70921753e-02 -1.69123396... | [11.233626365661621, -2.18058443069458] |
4cd4ee88-0032-4fd0-88f0-33bb84fc5cb0 | self-supervised-video-object-segmentation | 2006.12480 | null | https://arxiv.org/abs/2006.12480v1 | https://arxiv.org/pdf/2006.12480v1.pdf | Self-supervised Video Object Segmentation | The objective of this paper is self-supervised representation learning, with the goal of solving semi-supervised video object segmentation (a.k.a. dense tracking). We make the following contributions: (i) we propose to improve the existing self-supervised approach, with a simple, yet more effective memory mechanism for... | ['Yanwei Fu', 'Li Zhang', 'Fangrui Zhu', 'Weidi Xie', 'Guodong Guo'] | 2020-06-22 | null | null | null | null | ['one-shot-visual-object-segmentation'] | ['computer-vision'] | [-1.24938570e-01 -1.45265386e-01 -5.20355284e-01 -1.71845302e-01
-7.78290212e-01 -5.90320528e-01 3.27088565e-01 -1.87155873e-01
-4.24442858e-01 5.87379098e-01 2.54737854e-01 8.80274475e-02
1.37475684e-01 -2.74354607e-01 -1.12711823e+00 -4.28332508e-01
-2.00307518e-01 4.30729568e-01 6.97959244e-01 1.90882623... | [6.418793678283691, -2.030290126800537] |
4ce1cf47-c4bf-4cd6-bc85-d5568c939c6f | medical-phrase-grounding-with-region-phrase | 2303.07618 | null | https://arxiv.org/abs/2303.07618v1 | https://arxiv.org/pdf/2303.07618v1.pdf | Medical Phrase Grounding with Region-Phrase Context Contrastive Alignment | Medical phrase grounding (MPG) aims to locate the most relevant region in a medical image, given a phrase query describing certain medical findings, which is an important task for medical image analysis and radiological diagnosis. However, existing visual grounding methods rely on general visual features for identifyin... | ['Huazhu Fu', 'Yong liu', 'Xinxing Xu', 'Choon Hua Thng', 'Lionel Cheng', 'Gideon Ooi', 'Liang Wan', 'Junting Zhao', 'Anh Tran', 'Yang Zhou', 'Zhihao Chen'] | 2023-03-14 | null | null | null | null | ['visual-grounding', 'phrase-grounding'] | ['computer-vision', 'natural-language-processing'] | [ 2.41921276e-01 1.86928838e-01 -4.03582454e-01 -1.62533909e-01
-1.01803052e+00 -1.93363190e-01 4.05745149e-01 6.29398048e-01
-2.02239439e-01 3.85646671e-01 4.84499276e-01 -4.33567375e-01
-2.42935017e-01 -5.50533295e-01 -6.89256608e-01 -7.47527182e-01
-1.34286210e-01 3.14630032e-01 4.13556397e-01 -1.08789727... | [15.015018463134766, -1.5559483766555786] |
3b6141f5-921d-401b-8cce-899dde70eacd | prototype-memory-and-attention-mechanisms-for | null | null | https://openreview.net/forum?id=lY0-7bj0Vfz | https://openreview.net/pdf?id=lY0-7bj0Vfz | Prototype memory and attention mechanisms for few shot image generation | Recent discoveries indicate that the neural codes in the primary visual cortex (V1) of macaque monkeys are complex, diverse and sparse. This leads us to ponder the computational advantages and functional role of these “grandmother cells." Here, we propose that such cells can serve as prototype memory priors that bias a... | ['Tai Sing Lee', 'Amir Barati Farimani', 'Harold Rockwell', 'Andrew Luo', 'Zijie Li', 'Tianqin Li'] | 2021-09-29 | null | null | null | iclr-2022-4 | ['online-clustering'] | ['computer-vision'] | [ 2.05902264e-01 4.47077990e-01 2.50875652e-01 -3.56006593e-01
3.24216001e-02 -3.41358453e-01 1.00105107e+00 -2.36745365e-02
-4.22046751e-01 1.62158161e-01 5.55395305e-01 -8.09048191e-02
-2.24963613e-02 -6.58178687e-01 -8.88746202e-01 -8.84022593e-01
1.46677747e-01 3.11493397e-01 3.06314621e-02 -1.23912260... | [10.354081153869629, 2.5092415809631348] |
c94e9e95-2fea-4bd3-8510-cbc27f7f2f7e | multi-level-semantic-feature-augmentation-for | 1804.05298 | null | http://arxiv.org/abs/1804.05298v4 | http://arxiv.org/pdf/1804.05298v4.pdf | Multi-level Semantic Feature Augmentation for One-shot Learning | The ability to quickly recognize and learn new visual concepts from limited
samples enables humans to swiftly adapt to new environments. This ability is
enabled by semantic associations of novel concepts with those that have already
been learned and stored in memory. Computers can start to ascertain similar
abilities b... | ['xiangyang xue', 'Yu-Gang Jiang', 'yinda zhang', 'Yanwei Fu', 'Zitian Chen', 'Leonid Sigal'] | 2018-04-15 | null | null | null | null | ['novel-concepts'] | ['reasoning'] | [ 5.03690124e-01 -7.83535764e-02 8.91560838e-02 -5.79876244e-01
-2.93718755e-01 -5.27497709e-01 8.71061206e-01 3.45613718e-01
-5.46807289e-01 6.54963911e-01 1.47807226e-01 1.99458003e-01
-1.41217962e-01 -1.04659379e+00 -8.77996981e-01 -5.08208334e-01
7.10055381e-02 3.16486418e-01 3.00362110e-01 -2.34025404... | [10.137286186218262, 2.377394437789917] |
d9f2babe-0d9f-426e-abc7-7a60cab7851d | metaset-exploring-shape-and-property-spaces | 2006.02142 | null | https://arxiv.org/abs/2006.02142v3 | https://arxiv.org/pdf/2006.02142v3.pdf | METASET: Exploring Shape and Property Spaces for Data-Driven Metamaterials Design | Data-driven design of mechanical metamaterials is an increasingly popular method to combat costly physical simulations and immense, often intractable, geometrical design spaces. Using a precomputed dataset of unit cells, a multiscale structure can be quickly filled via combinatorial search algorithms, and machine learn... | ['Li-Wei Wang', 'Yu-Chin Chan', 'Wei Chen', 'Faez Ahmed'] | 2020-06-01 | null | null | null | null | ['physical-simulations'] | ['miscellaneous'] | [ 1.81585729e-01 -1.86246693e-01 2.05448992e-03 1.80309057e-01
-8.30672145e-01 -7.35728145e-01 4.05758679e-01 4.20758501e-02
-8.57090577e-02 8.08468759e-01 1.75540775e-01 -1.10307023e-01
-6.14859045e-01 -9.48485672e-01 -4.53801423e-01 -1.01206863e+00
-3.04221758e-03 7.28423536e-01 8.47145244e-02 -2.64002919... | [5.285543441772461, 5.188082218170166] |
7948eb72-dc3e-49ae-908d-9f8a011ed592 | skeleton2mesh-kinematics-prior-injected | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Yu_Skeleton2Mesh_Kinematics_Prior_Injected_Unsupervised_Human_Mesh_Recovery_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Yu_Skeleton2Mesh_Kinematics_Prior_Injected_Unsupervised_Human_Mesh_Recovery_ICCV_2021_paper.pdf | Skeleton2Mesh: Kinematics Prior Injected Unsupervised Human Mesh Recovery | In this paper, we decouple unsupervised human mesh recovery into the well-studied problems of unsupervised 3D pose estimation, and human mesh recovery from estimated 3D skeletons, focusing on the latter task. The challenges of the latter task are two folds: (1) pose failure (i.e., pose mismatching -- different skel... | ['Wenjun Zhang', 'Minsi Wang', 'Chenglong Zhao', 'Bingbing Ni', 'Jingwei Xu', 'Junjie Wang', 'Zhenbo Yu'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['3d-pose-estimation', 'human-mesh-recovery'] | ['computer-vision', 'computer-vision'] | [ 1.73612863e-01 2.44027138e-01 -3.08098625e-02 -5.18324487e-02
-9.20981944e-01 -3.40722561e-01 2.94606566e-01 -4.10385996e-01
-2.00720966e-01 3.95568520e-01 2.18152508e-01 2.43231460e-01
-1.64695345e-02 -5.62072933e-01 -8.72745514e-01 -4.46338415e-01
1.16184108e-01 9.50213850e-01 2.12542444e-01 -1.49425536... | [7.012986660003662, -1.1448668241500854] |
740c2243-59eb-4ade-a429-819c2b03c426 | deepdpm-deep-clustering-with-an-unknown | 2203.14309 | null | https://arxiv.org/abs/2203.14309v1 | https://arxiv.org/pdf/2203.14309v1.pdf | DeepDPM: Deep Clustering With an Unknown Number of Clusters | Deep Learning (DL) has shown great promise in the unsupervised task of clustering. That said, while in classical (i.e., non-deep) clustering the benefits of the nonparametric approach are well known, most deep-clustering methods are parametric: namely, they require a predefined and fixed number of clusters, denoted by ... | ['Oren Freifeld', 'Shahaf E. Finder', 'Meitar Ronen'] | 2022-03-27 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Ronen_DeepDPM_Deep_Clustering_With_an_Unknown_Number_of_Clusters_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Ronen_DeepDPM_Deep_Clustering_With_an_Unknown_Number_of_Clusters_CVPR_2022_paper.pdf | cvpr-2022-1 | ['unsupervised-image-classification'] | ['computer-vision'] | [-3.66064608e-01 -1.00405045e-01 -1.00051194e-01 -4.77699816e-01
-7.31459558e-01 -6.01070762e-01 5.20203173e-01 2.63518900e-01
-7.18866348e-01 6.48646533e-01 -1.29774198e-01 -1.14859574e-01
-3.56781721e-01 -6.97945952e-01 -9.27530706e-01 -1.12793100e+00
-1.54614210e-01 1.09683251e+00 3.04831982e-01 1.26503110... | [9.074505805969238, 3.3205645084381104] |
a5c7a7d6-8fb6-4429-aa60-3ecf5d6fe430 | towards-table-to-text-generation-with-1 | 2301.02071 | null | https://arxiv.org/abs/2301.02071v1 | https://arxiv.org/pdf/2301.02071v1.pdf | Towards Table-to-Text Generation with Pretrained Language Model: A Table Structure Understanding and Text Deliberating Approach | Although remarkable progress on the neural table-to-text methods has been made, the generalization issues hinder the applicability of these models due to the limited source tables. Large-scale pretrained language models sound like a promising solution to tackle such issues. However, how to effectively bridge the gap be... | ['Hui Xiong', 'Dejing Dou', 'Jingbo Zhou', 'Yanyan Li', 'Tong Xu', 'Xinjiang Lu', 'Miao Chen'] | 2023-01-05 | null | null | null | null | ['table-to-text-generation'] | ['natural-language-processing'] | [ 2.60580182e-01 4.38415974e-01 -1.00662604e-01 -4.79606450e-01
-1.09300530e+00 -4.73517269e-01 7.73198128e-01 -3.03068701e-02
-1.12103298e-01 8.00507784e-01 7.54100919e-01 -3.60382646e-01
2.27096826e-01 -1.22754824e+00 -9.00545418e-01 -2.30111599e-01
7.40389943e-01 7.93010294e-01 2.88520604e-02 -7.10358918... | [11.66535472869873, 8.816625595092773] |
5874d463-6651-4d4d-95fe-d5113faef893 | automotive-parts-assessment-applying-real | 2202.00884 | null | https://arxiv.org/abs/2202.00884v1 | https://arxiv.org/pdf/2202.00884v1.pdf | Automotive Parts Assessment: Applying Real-time Instance-Segmentation Models to Identify Vehicle Parts | The problem of automated car damage assessment presents a major challenge in the auto repair and damage assessment industry. The domain has several application areas ranging from car assessment companies such as car rentals and body shops to accidental damage assessment for car insurance companies. In vehicle assessmen... | ['Riad Souissi', 'Abdulmalik Ali Aldawsari', 'Syed Adnan Yusuf'] | 2022-02-02 | null | null | null | null | ['real-time-instance-segmentation'] | ['computer-vision'] | [-2.05335040e-02 4.33109477e-02 8.34439397e-02 1.65031236e-02
-1.13568079e+00 -6.01341128e-01 5.05555689e-01 4.11981076e-01
-1.18011191e-01 5.68204880e-01 -4.15528566e-01 -2.09515631e-01
-2.28074566e-01 -8.51522446e-01 -6.76506281e-01 -6.82671785e-01
3.31067383e-01 7.02166021e-01 6.79619312e-01 -2.74532855... | [7.454608917236328, 1.4915467500686646] |
34773616-9572-4f81-94ee-3a9efea40245 | deep-automatic-natural-image-matting | 2107.07235 | null | https://arxiv.org/abs/2107.07235v1 | https://arxiv.org/pdf/2107.07235v1.pdf | Deep Automatic Natural Image Matting | Automatic image matting (AIM) refers to estimating the soft foreground from an arbitrary natural image without any auxiliary input like trimap, which is useful for image editing. Prior methods try to learn semantic features to aid the matting process while being limited to images with salient opaque foregrounds such as... | ['DaCheng Tao', 'Jing Zhang', 'Jizhizi Li'] | 2021-07-15 | null | null | null | null | ['image-matting'] | ['computer-vision'] | [ 5.87653995e-01 2.46487126e-01 -1.13710195e-01 -3.97691220e-01
-3.62497419e-01 -2.50748873e-01 4.87232834e-01 -4.61093098e-01
-1.39917046e-01 6.03306949e-01 1.02100335e-02 -1.07054766e-02
3.10197026e-01 -6.91043317e-01 -1.05010235e+00 -6.52628660e-01
3.64452899e-01 5.47874033e-01 2.54074067e-01 -1.53613929... | [10.646900177001953, -0.8697010278701782] |
99153dfc-b257-4120-9565-80aa8d3b5a07 | navigating-to-objects-specified-by-images | 2304.01192 | null | https://arxiv.org/abs/2304.01192v1 | https://arxiv.org/pdf/2304.01192v1.pdf | Navigating to Objects Specified by Images | Images are a convenient way to specify which particular object instance an embodied agent should navigate to. Solving this task requires semantic visual reasoning and exploration of unknown environments. We present a system that can perform this task in both simulation and the real world. Our modular method solves sub-... | ['Devendra Singh Chaplot', 'Stefan Lee', 'Jitendra Malik', 'Dhruv Batra', 'Roozbeh Mottaghi', 'Chris Paxton', 'Austin Wang', 'Karmesh Yadav', 'Theophile Gervet', 'Jacob Krantz'] | 2023-04-03 | null | null | null | null | ['visual-reasoning', 'visual-reasoning'] | ['computer-vision', 'reasoning'] | [-2.24261567e-01 3.21865916e-01 1.29945830e-01 -3.38848561e-01
-8.47700596e-01 -7.92077959e-01 7.21904457e-01 -2.69994467e-01
-7.91712344e-01 6.54301763e-01 1.82506293e-01 -3.43368411e-01
1.22703999e-01 -2.98975408e-01 -8.05768728e-01 -3.77286673e-01
-3.25857818e-01 9.62775588e-01 1.36909142e-01 -3.62810820... | [4.564723014831543, 0.593198299407959] |
b55d3da1-d070-4102-a0b6-f2c16f884991 | dwformer-dynamic-window-transformer-for | 2303.01694 | null | https://arxiv.org/abs/2303.01694v1 | https://arxiv.org/pdf/2303.01694v1.pdf | DWFormer: Dynamic Window transFormer for Speech Emotion Recognition | Speech emotion recognition is crucial to human-computer interaction. The temporal regions that represent different emotions scatter in different parts of the speech locally. Moreover, the temporal scales of important information may vary over a large range within and across speech segments. Although transformer-based m... | ['Xiangmin Xu', 'Weidong Chen', 'Weibin Zhang', 'Xiaofen Xing', 'Shuaiqi Chen'] | 2023-03-03 | null | null | null | null | ['speech-emotion-recognition'] | ['speech'] | [-1.63580865e-01 -2.48454049e-01 -2.36840636e-01 -5.86329639e-01
-1.07473683e+00 -3.55659008e-01 4.51240420e-01 2.87485600e-01
-3.80311877e-01 4.85243559e-01 8.01676571e-01 2.35008642e-01
-2.58964449e-02 -1.56429142e-01 -1.10714450e-01 -5.76352417e-01
-2.86870778e-01 -2.05637282e-03 5.97172439e-01 -2.63777077... | [13.498022079467773, 5.750939846038818] |
7e6c3813-86fc-40b4-a70e-70cbfb3b8dba | knowgraph-pm-a-knowledge-graph-based-pricing | 2205.07627 | null | https://arxiv.org/abs/2205.07627v1 | https://arxiv.org/pdf/2205.07627v1.pdf | KnowGraph-PM: a Knowledge Graph based Pricing Model for Semiconductors Supply Chains | Semiconductor supply chains are described by significant demand fluctuation that increases as one moves up the supply chain, the so-called bullwhip effect. To counteract, semiconductor manufacturers aim to optimize capacity utilization, to deliver with shorter lead times and exploit this to generate revenue. Additional... | ['Hans Ehm', 'Javad Chamanara', 'Soren Auer', 'Nour Ramzy'] | 2022-05-13 | null | null | null | null | ['data-integration'] | ['knowledge-base'] | [-5.39157450e-01 2.57773101e-01 -3.85001868e-01 -4.07582194e-01
-3.98089111e-01 -8.30063343e-01 1.71101719e-01 6.60379112e-01
-1.84405074e-01 6.72218561e-01 -5.59020303e-02 -1.46062955e-01
-9.36057270e-01 -1.38512933e+00 -4.67758268e-01 -1.87406868e-01
7.18923733e-02 1.25337160e+00 3.04050427e-02 -4.00145262... | [9.091737747192383, 7.544629096984863] |
6882b1c6-152f-4dfd-84e9-dd24358e9161 | learning-underrepresented-classes-from | 2206.15353 | null | https://arxiv.org/abs/2206.15353v1 | https://arxiv.org/pdf/2206.15353v1.pdf | Learning Underrepresented Classes from Decentralized Partially Labeled Medical Images | Using decentralized data for federated training is one promising emerging research direction for alleviating data scarcity in the medical domain. However, in contrast to large-scale fully labeled data commonly seen in general object recognition tasks, the local medical datasets are more likely to only have images annot... | ['Irina Voiculescu', 'Michael Kampffmeyer', 'Nanqing Dong'] | 2022-06-30 | null | null | null | null | ['thoracic-disease-classification'] | ['computer-vision'] | [ 3.73366386e-01 3.56121212e-01 -8.49693418e-01 -5.04047096e-01
-1.15055442e+00 -3.66483063e-01 1.63651809e-01 3.64602506e-01
-2.99526781e-01 5.96578777e-01 2.88416117e-01 -3.73276100e-02
-3.85937065e-01 -7.08758116e-01 -5.06314158e-01 -1.05735373e+00
8.73811617e-02 7.45320618e-01 -1.83529228e-01 2.39974901... | [6.035907745361328, 6.446976661682129] |
c022ffca-3931-4f86-a664-b58a5580e692 | 190412634 | 1904.12634 | null | http://arxiv.org/abs/1904.12634v1 | http://arxiv.org/pdf/1904.12634v1.pdf | DADA-2000: Can Driving Accident be Predicted by Driver Attention? Analyzed by A Benchmark | Driver attention prediction is currently becoming the focus in safe driving
research community, such as the DR(eye)VE project and newly emerged Berkeley
DeepDrive Attention (BDD-A) database in critical situations. In safe driving,
an essential task is to predict the incoming accidents as early as possible.
BDD-A was aw... | ['Dingxin Yan', 'Sen Li', 'Jianru Xue', 'Jiahuan Qiao', 'He Wang', 'Jianwu Fang'] | 2019-04-23 | null | null | null | null | ['driver-attention-monitoring'] | ['computer-vision'] | [-3.27877223e-01 -3.77174288e-01 -5.95651194e-02 -2.77221262e-01
-2.22884312e-01 -2.31573179e-01 3.34197700e-01 -2.89532840e-01
-5.61813712e-01 5.08441389e-01 5.09022593e-01 -4.24212039e-01
-3.96013297e-02 -4.77130353e-01 -4.40824270e-01 -5.08355916e-01
2.56767392e-01 -3.91457453e-02 5.20492435e-01 -5.27165949... | [7.587363243103027, -0.10580616444349289] |
36ee38a9-f446-4c5d-91ee-859d75582243 | contrastive-instruction-trajectory-learning | 2112.04138 | null | https://arxiv.org/abs/2112.04138v2 | https://arxiv.org/pdf/2112.04138v2.pdf | Contrastive Instruction-Trajectory Learning for Vision-Language Navigation | The vision-language navigation (VLN) task requires an agent to reach a target with the guidance of natural language instruction. Previous works learn to navigate step-by-step following an instruction. However, these works may fail to discriminate the similarities and discrepancies across instruction-trajectory pairs an... | ['Xiaodan Liang', 'Bing Wang', 'Bingqian Lin', 'Yi Zhu', 'Fengda Zhu', 'Xiwen Liang'] | 2021-12-08 | null | null | null | null | ['vision-language-navigation'] | ['computer-vision'] | [ 5.88542931e-02 -4.38301235e-01 -7.99562410e-02 -4.52821493e-01
-7.42571712e-01 -5.30863583e-01 1.01179612e+00 -3.77778932e-02
-8.44158411e-01 2.86411643e-01 2.82803327e-01 -6.32875860e-01
-2.55511433e-01 -4.32272404e-01 -8.22330236e-01 -8.94213021e-01
-6.32315800e-02 1.22317173e-01 2.89321661e-01 -5.01384139... | [4.418062210083008, 0.5160495638847351] |
4ed1131e-4ff0-4461-a4d1-59d754d5ab61 | 3d-brainformer-3d-fusion-transformer-for | 2304.14508 | null | https://arxiv.org/abs/2304.14508v1 | https://arxiv.org/pdf/2304.14508v1.pdf | 3D Brainformer: 3D Fusion Transformer for Brain Tumor Segmentation | Magnetic resonance imaging (MRI) is critically important for brain mapping in both scientific research and clinical studies. Precise segmentation of brain tumors facilitates clinical diagnosis, evaluations, and surgical planning. Deep learning has recently emerged to improve brain tumor segmentation and achieved impres... | ['Simon K. Warfield', 'Ali Gholipour', 'Jianhui Li', 'Mingzhang Zhao', 'Qiuying Li', 'Yuqi Qian', 'Yao Sui', 'Guoyao Zhang', 'Rui Nian'] | 2023-04-28 | null | null | null | null | ['tumor-segmentation', 'brain-tumor-segmentation'] | ['computer-vision', 'medical'] | [ 1.53810710e-01 2.63682067e-01 8.49247500e-02 -4.67670202e-01
-6.47690415e-01 -1.31765693e-01 3.90990108e-01 -8.75052661e-02
-5.57766497e-01 4.21165526e-01 9.83149633e-02 -3.75772953e-01
-1.52841926e-01 -8.19723964e-01 -6.00516796e-01 -8.89259875e-01
-2.32197702e-01 6.73224807e-01 6.35469317e-01 -1.72689542... | [14.651260375976562, -2.317664861679077] |
299d466c-5944-48ec-a655-e674c8075ec4 | learning-chess-blindfolded | null | null | https://openreview.net/forum?id=DGIXvEAJVd | https://openreview.net/pdf?id=DGIXvEAJVd | Learning Chess Blindfolded | Transformer language models have made tremendous strides in natural language
understanding. However, the complexity of natural language makes it challenging
to ascertain how accurately these models are tracking the world state underlying
the text. Motivated by this issue, we consider the task of language modeling for
t... | ['Kevin Gimpel', 'Karen Livescu', 'Sam Wiseman', 'Shubham Toshniwal'] | 2021-01-01 | null | null | null | null | ['game-of-chess'] | ['playing-games'] | [ 8.60633031e-02 1.96451202e-01 -6.18047357e-01 -9.17766318e-02
-7.35243380e-01 -1.08568633e+00 8.03221226e-01 4.10949558e-01
-3.71781260e-01 6.31500423e-01 3.06858391e-01 -1.12105000e+00
2.28356972e-01 -1.04203117e+00 -6.82403862e-01 9.28501487e-02
-4.17558700e-02 6.59065366e-01 5.22986889e-01 -5.89463472... | [9.087780952453613, 7.30671501159668] |
abdd03c7-3294-459f-8a76-8f03dd6151c6 | towards-fair-and-explainable-ai-using-a-human | 2306.07427 | null | https://arxiv.org/abs/2306.07427v1 | https://arxiv.org/pdf/2306.07427v1.pdf | Towards Fair and Explainable AI using a Human-Centered AI Approach | The rise of machine learning (ML) is accompanied by several high-profile cases that have stressed the need for fairness, accountability, explainability and trust in ML systems. The existing literature has largely focused on fully automated ML approaches that try to optimize for some performance metric. However, human-c... | ['Bhavya Ghai'] | 2023-06-12 | null | null | null | null | ['word-embeddings'] | ['methodology'] | [-5.02896070e-01 5.59614897e-01 -3.51525098e-01 -6.78892255e-01
1.55302882e-01 -3.70239139e-01 7.25172341e-01 9.27915156e-01
-3.75976920e-01 4.21636164e-01 8.60568643e-01 -5.39404273e-01
-9.91093069e-02 -3.83748144e-01 -1.71059951e-01 -1.27704933e-01
4.85770226e-01 2.98168868e-01 -4.92666394e-01 -4.08923358... | [9.043755531311035, 5.469122886657715] |
d530045d-9218-4854-b7da-3add90ababaf | a-systematic-study-and-comprehensive | 2305.18486 | null | https://arxiv.org/abs/2305.18486v4 | https://arxiv.org/pdf/2305.18486v4.pdf | A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasets | The development of large language models (LLMs) such as ChatGPT has brought a lot of attention recently. However, their evaluation in the benchmark academic datasets remains under-explored due to the difficulty of evaluating the generative outputs produced by this model against the ground truth. In this paper, we aim t... | ['Jimmy Xiangji Huang', 'Shafiq Joty', 'Md Amran Hossen Bhuiyan', 'Mizanur Rahman', 'M Saiful Bari', 'Md Tahmid Rahman Laskar'] | 2023-05-29 | null | null | null | null | ['code-generation', 'bias-detection', 'text-summarization'] | ['computer-code', 'natural-language-processing', 'natural-language-processing'] | [ 2.84571573e-02 2.61742681e-01 3.98318470e-02 -2.86632746e-01
-1.52082872e+00 -7.40119815e-01 6.72856808e-01 2.18108475e-01
-3.15323062e-02 9.04342353e-01 3.94158810e-01 -5.73322475e-01
4.72556390e-02 -5.04916072e-01 -7.82790005e-01 -3.14055800e-01
1.06767677e-01 7.77370334e-01 -5.93660027e-02 -2.82804251... | [11.504196166992188, 8.50103759765625] |
1275d75b-3890-453a-aa82-27234c0f3173 | soft-sampling-for-robust-object-detection | 1806.06986 | null | https://arxiv.org/abs/1806.06986v2 | https://arxiv.org/pdf/1806.06986v2.pdf | Soft Sampling for Robust Object Detection | We study the robustness of object detection under the presence of missing annotations. In this setting, the unlabeled object instances will be treated as background, which will generate an incorrect training signal for the detector. Interestingly, we observe that after dropping 30% of the annotations (and labeling them... | ['Navaneeth Bodla', 'Zhe Wu', 'Rama Chellappa', 'Mahyar Najibi', 'Larry S. Davis', 'Bharat Singh'] | 2018-06-18 | null | null | null | null | ['robust-object-detection'] | ['computer-vision'] | [ 3.86780322e-01 3.48546624e-01 -2.20964327e-02 -3.00499856e-01
-7.88427889e-01 -6.98502362e-01 3.77715468e-01 -3.27840224e-02
-7.64993787e-01 5.54423809e-01 -3.15871507e-01 -3.45541090e-02
7.46625125e-01 -4.41725850e-01 -1.05036390e+00 -7.86974907e-01
1.70924380e-01 5.96418791e-02 1.12868559e+00 2.54772633... | [9.152514457702637, 1.0078966617584229] |
02237499-adac-4851-946f-13a82291a207 | islam-imperative-slam | 2306.07894 | null | https://arxiv.org/abs/2306.07894v2 | https://arxiv.org/pdf/2306.07894v2.pdf | iSLAM: Imperative SLAM | Simultaneous localization and mapping (SLAM) stands as one of the critical challenges in robot navigation. Recent advancements suggest that methods based on supervised learning deliver impressive performance in front-end odometry, while traditional optimization-based methods still play a vital role in the back-end for ... | ['Chen Wang', 'Shaoshu Su', 'Taimeng Fu'] | 2023-06-13 | null | null | null | null | ['simultaneous-localization-and-mapping', 'robot-navigation'] | ['computer-vision', 'robots'] | [-9.30179656e-02 2.59752065e-01 -4.71847616e-02 -4.27159965e-01
-6.63414955e-01 -3.40282351e-01 4.14886117e-01 2.51988202e-01
-6.58210278e-01 7.79243410e-01 -4.78503346e-01 -2.89452672e-01
-3.50933582e-01 -8.05291057e-01 -9.45521355e-01 -6.28070772e-01
-2.01817617e-01 6.28776550e-01 2.32239246e-01 -4.31417942... | [7.5750532150268555, -2.1104543209075928] |
6b575691-6b3c-4402-9b85-8e89764eced7 | teaching-deep-convolutional-neural-networks | 1412.3409 | null | http://arxiv.org/abs/1412.3409v2 | http://arxiv.org/pdf/1412.3409v2.pdf | Teaching Deep Convolutional Neural Networks to Play Go | Mastering the game of Go has remained a long standing challenge to the field
of AI. Modern computer Go systems rely on processing millions of possible
future positions to play well, but intuitively a stronger and more 'humanlike'
way to play the game would be to rely on pattern recognition abilities rather
then brute f... | ['Christopher Clark', 'Amos Storkey'] | 2014-12-10 | null | null | null | null | ['game-of-go'] | ['playing-games'] | [ 1.50464952e-01 2.69421071e-01 -1.67404786e-01 -7.11863264e-02
-5.30908227e-01 -6.90131783e-01 3.46338809e-01 -3.86430472e-01
-4.54351395e-01 6.49850070e-01 -2.95391113e-01 -8.33674014e-01
-3.66633922e-01 -1.32814503e+00 -1.27255106e+00 -4.28438395e-01
-3.01568091e-01 6.41475677e-01 5.12318134e-01 -1.06253541... | [3.467175245285034, 1.4484323263168335] |
13bca4bb-527c-4f77-bbbe-f81b9ced0aaf | copy-the-old-or-paint-anew-an-adversarial | 1811.09236 | null | http://arxiv.org/abs/1811.09236v1 | http://arxiv.org/pdf/1811.09236v1.pdf | Copy the Old or Paint Anew? An Adversarial Framework for (non-) Parametric Image Stylization | Parametric generative deep models are state-of-the-art for photo and
non-photo realistic image stylization. However, learning complicated image
representations requires compute-intense models parametrized by a huge number
of weights, which in turn requires large datasets to make learning successful.
Non-parametric exem... | ['Urs Bergmann', 'Nikolay Jetchev', 'Gokhan Yildirim'] | 2018-11-22 | null | null | null | null | ['image-stylization'] | ['computer-vision'] | [ 2.67506272e-01 7.45725706e-02 1.33859128e-01 2.80544329e-02
-6.15952432e-01 -7.35335112e-01 8.59153748e-01 -7.14802921e-01
-6.61983192e-02 7.57436395e-01 -2.26897672e-02 -7.35084191e-02
-1.41092949e-02 -8.26551378e-01 -9.61765349e-01 -6.48226261e-01
3.18204999e-01 8.48814249e-01 -5.67891896e-02 -3.42542797... | [11.701123237609863, -0.40009766817092896] |
692e4c47-edc1-47be-adae-c05c1a45b947 | keyword-extraction-from-short-texts-with-a | 2209.14008 | null | https://arxiv.org/abs/2209.14008v2 | https://arxiv.org/pdf/2209.14008v2.pdf | Keyword Extraction from Short Texts with a Text-To-Text Transfer Transformer | The paper explores the relevance of the Text-To-Text Transfer Transformer language model (T5) for Polish (plT5) to the task of intrinsic and extrinsic keyword extraction from short text passages. The evaluation is carried out on the new Polish Open Science Metadata Corpus (POSMAC), which is released with this paper: a ... | ['Maciej Ogrodniczuk', 'Bartłomiej Nitoń', 'Adam Wawrzyński', 'Agnieszka Mikołajczyk-Bareła', 'Piotr Pęzik'] | 2022-09-28 | null | null | null | null | ['keyword-extraction'] | ['natural-language-processing'] | [ 1.02498733e-01 5.93909204e-01 -1.32973492e-01 -5.48211522e-02
-1.71924007e+00 -7.38066316e-01 1.10039914e+00 2.53529966e-01
-7.11174965e-01 1.13056707e+00 5.63287199e-01 -2.72969931e-01
-1.96130738e-01 -1.26704752e-01 -6.04586065e-01 -4.98536229e-01
5.57207227e-01 1.05236888e+00 1.79898724e-01 2.10350920... | [11.076208114624023, 9.622673988342285] |
ca9087bf-4a12-4288-a765-38dcddb10563 | return-of-the-rnn-residual-recurrent-networks | 2303.13570 | null | https://arxiv.org/abs/2303.13570v2 | https://arxiv.org/pdf/2303.13570v2.pdf | Return of the RNN: Residual Recurrent Networks for Invertible Sentence Embeddings | This study presents a novel model for invertible sentence embeddings using a residual recurrent network trained on an unsupervised encoding task. Rather than the probabilistic outputs common to neural machine translation models, our approach employs a regression-based output layer to reconstruct the input sequence's wo... | ['Jeremy Wilkerson'] | 2023-03-23 | null | null | null | null | ['sentence-embeddings', 'sentence-embeddings'] | ['methodology', 'natural-language-processing'] | [ 4.82981712e-01 3.71728569e-01 -4.18258250e-01 -5.14091372e-01
-7.98564911e-01 -3.47698212e-01 5.38377881e-01 1.57082766e-01
-9.34932649e-01 6.90126717e-01 4.37578291e-01 -8.20530117e-01
5.26608527e-01 -7.55108654e-01 -9.30900335e-01 -3.31123024e-01
1.00318320e-01 3.26853633e-01 -2.81746835e-01 -2.98068404... | [10.66348934173584, 8.197282791137695] |
e04b9783-0744-4522-b4f8-bd2c0e76618b | regret-bounds-for-information-directed | 2206.04640 | null | https://arxiv.org/abs/2206.04640v2 | https://arxiv.org/pdf/2206.04640v2.pdf | Regret Bounds for Information-Directed Reinforcement Learning | Information-directed sampling (IDS) has revealed its potential as a data-efficient algorithm for reinforcement learning (RL). However, theoretical understanding of IDS for Markov Decision Processes (MDPs) is still limited. We develop novel information-theoretic tools to bound the information ratio and cumulative inform... | ['Tor Lattimore', 'Botao Hao'] | 2022-06-09 | null | null | null | null | ['thompson-sampling'] | ['methodology'] | [ 2.70310640e-01 5.58611155e-01 -6.03290915e-01 -2.53118128e-01
-1.28899610e+00 -6.27683938e-01 2.41604522e-01 2.36175150e-01
-4.43084598e-01 9.64044988e-01 1.96064979e-01 -4.87213641e-01
-6.60300851e-01 -7.80233026e-01 -9.65551019e-01 -9.76792634e-01
-2.19101623e-01 6.34215772e-01 -2.08161205e-01 2.99192190... | [4.407537460327148, 3.01192045211792] |
23b70d21-3331-4ce6-a419-14d929c3f96d | yaclc-a-chinese-learner-corpus-with | 2112.15043 | null | https://arxiv.org/abs/2112.15043v1 | https://arxiv.org/pdf/2112.15043v1.pdf | YACLC: A Chinese Learner Corpus with Multidimensional Annotation | Learner corpus collects language data produced by L2 learners, that is second or foreign-language learners. This resource is of great relevance for second language acquisition research, foreign-language teaching, and automatic grammatical error correction. However, there is little focus on learner corpus for Chinese as... | ['Maosong Sun', 'Erhong Yang', 'Yun Chen', 'Zhenghao Liu', 'Shan He', 'Renfen Hu', 'Xiaorong Lu', 'Yijun Wang', 'Liner Yang', 'Cunliang Kong', 'Yingying Wang'] | 2021-12-30 | null | null | null | null | ['language-acquisition'] | ['natural-language-processing'] | [-4.41728204e-01 -4.90060002e-02 4.36955225e-03 -2.96711534e-01
-1.07464278e+00 -6.02153599e-01 1.69813588e-01 4.95041728e-01
-6.94494307e-01 9.00387049e-01 4.93564039e-01 -5.80893934e-01
4.12639797e-01 -7.61739314e-01 -7.44633377e-01 -1.49470627e-01
6.43124402e-01 3.86674643e-01 2.89713204e-01 -5.77475548... | [10.995808601379395, 10.702879905700684] |
4f1b35b9-a7eb-4463-8a25-cf7d2f3eea64 | robust-and-fine-grained-prosody-control-of | 1811.02122 | null | http://arxiv.org/abs/1811.02122v2 | http://arxiv.org/pdf/1811.02122v2.pdf | Robust and fine-grained prosody control of end-to-end speech synthesis | We propose prosody embeddings for emotional and expressive speech synthesis
networks. The proposed methods introduce temporal structures in the embedding
networks, thus enabling fine-grained control of the speaking style of the
synthesized speech. The temporal structures can be designed either on the
speech side or the... | ['Young-Gun Lee', 'Taesu Kim'] | 2018-11-06 | null | null | null | null | ['expressive-speech-synthesis'] | ['speech'] | [-1.19568005e-01 4.44179237e-01 -2.85736978e-01 -4.89845425e-01
-4.44374770e-01 -6.93926871e-01 3.78376663e-01 -4.27337468e-01
-2.72941083e-01 5.15563726e-01 7.72916496e-01 9.26592276e-02
3.41597974e-01 -8.49366248e-01 -4.46251869e-01 -8.92988920e-01
1.34443566e-01 -1.42878875e-01 1.45887479e-01 -4.65150923... | [14.910160064697266, 6.5301971435546875] |
d836833f-32ac-4052-8d22-712af945939f | grm-generative-relevance-modeling-using | 2306.09938 | null | https://arxiv.org/abs/2306.09938v1 | https://arxiv.org/pdf/2306.09938v1.pdf | GRM: Generative Relevance Modeling Using Relevance-Aware Sample Estimation for Document Retrieval | Recent studies show that Generative Relevance Feedback (GRF), using text generated by Large Language Models (LLMs), can enhance the effectiveness of query expansion. However, LLMs can generate irrelevant information that harms retrieval effectiveness. To address this, we propose Generative Relevance Modeling (GRM) that... | ['Fabio Crestani', 'Jeffrey Dalton', 'Shubham Chatterjee', 'Ivan Sekulic', 'Iain Mackie'] | 2023-06-16 | null | null | null | null | ['document-ranking'] | ['natural-language-processing'] | [ 2.97816813e-01 9.58880857e-02 -2.32461005e-01 -1.47360906e-01
-1.39441419e+00 -4.55130279e-01 9.84865487e-01 2.60380149e-01
-3.31850082e-01 7.02593803e-01 7.29034245e-01 -2.75404006e-01
-2.26351768e-01 -7.50290811e-01 -5.86328864e-01 -1.27501965e-01
2.85789371e-02 8.13992083e-01 3.46396655e-01 -7.55967200... | [11.500020027160645, 7.613100528717041] |
c7d05f35-1486-462a-bcba-e2573506a4b0 | weakly-supervised-temporal-action-1 | 2001.07793 | null | https://arxiv.org/abs/2001.07793v1 | https://arxiv.org/pdf/2001.07793v1.pdf | Weakly Supervised Temporal Action Localization Using Deep Metric Learning | Temporal action localization is an important step towards video understanding. Most current action localization methods depend on untrimmed videos with full temporal annotations of action instances. However, it is expensive and time-consuming to annotate both action labels and temporal boundaries of videos. To this end... | ['Ashraful Islam', 'Richard J. Radke'] | 2020-01-21 | null | null | null | null | ['weakly-supervised-temporal-action'] | ['computer-vision'] | [ 4.25151020e-01 -1.50703147e-01 -6.67863607e-01 -4.39546734e-01
-9.39138949e-01 -4.91605788e-01 4.47109520e-01 -2.03557536e-01
-6.70616448e-01 5.26636720e-01 2.58160442e-01 1.28140092e-01
2.58050531e-01 -2.38167211e-01 -9.14625466e-01 -6.28579021e-01
-5.26298881e-01 -1.96161848e-02 6.12051487e-01 4.03310806... | [8.419750213623047, 0.5439583659172058] |
3c5119af-7adf-409c-8cfb-60f512f9c364 | kinematic-aware-hierarchical-attention | 2211.15868 | null | https://arxiv.org/abs/2211.15868v1 | https://arxiv.org/pdf/2211.15868v1.pdf | Kinematic-aware Hierarchical Attention Network for Human Pose Estimation in Videos | Previous video-based human pose estimation methods have shown promising results by leveraging aggregated features of consecutive frames. However, most approaches compromise accuracy to mitigate jitter or do not sufficiently comprehend the temporal aspects of human motion. Furthermore, occlusion increases uncertainty be... | ['Seong-Whan Lee', 'Tae-Kyung Kang', 'Gun-Hee Lee', 'Byoung-Sung Lim', 'Kyung-Min Jin'] | 2022-11-29 | null | null | null | null | ['3d-pose-estimation'] | ['computer-vision'] | [-1.85355291e-01 -6.43030852e-02 -3.00893605e-01 -3.08031976e-01
-8.68416488e-01 -2.00132877e-01 2.12747842e-01 7.81338010e-03
-3.14754635e-01 5.73423922e-01 5.17879069e-01 6.16866529e-01
1.32377401e-01 -4.00562167e-01 -7.49958932e-01 -2.74111807e-01
-1.84558138e-01 4.27333444e-01 3.66695195e-01 -1.83465198... | [7.159297943115234, -0.7827191352844238] |
03cb23b5-6957-44e9-b2eb-6e253c5566fb | a-multi-view-dynamic-fusion-framework-how-to | 2012.11211 | null | https://arxiv.org/abs/2012.11211v1 | https://arxiv.org/pdf/2012.11211v1.pdf | A Multi-View Dynamic Fusion Framework: How to Improve the Multimodal Brain Tumor Segmentation from Multi-Views? | When diagnosing the brain tumor, doctors usually make a diagnosis by observing multimodal brain images from the axial view, the coronal view and the sagittal view, respectively. And then they make a comprehensive decision to confirm the brain tumor based on the information obtained from multi-views. Inspired by this di... | ['Zhiguang Qin', 'Mingsheng Cao', 'Ji Geng', 'Guozheng Wu', 'Wei Zheng', 'Yi Ding'] | 2020-12-21 | null | null | null | null | ['multi-view-learning'] | ['computer-vision'] | [-1.01711191e-01 -9.51625332e-02 1.13083385e-01 -2.20055148e-01
-6.06904209e-01 -4.65002321e-02 2.76897371e-01 -1.65955007e-01
-6.04041100e-01 3.90453428e-01 8.81001949e-02 4.79731075e-02
-2.33532712e-01 -7.43611038e-01 -3.32017154e-01 -1.10920024e+00
5.80182076e-01 3.08878928e-01 3.05453658e-01 5.06329425... | [14.549976348876953, -2.3737783432006836] |
6a458585-5d62-46d2-894f-dbde771fb30e | adversarial-language-games-for-advanced | 1911.01622 | null | https://arxiv.org/abs/1911.01622v4 | https://arxiv.org/pdf/1911.01622v4.pdf | Adversarial Language Games for Advanced Natural Language Intelligence | We study the problem of adversarial language games, in which multiple agents with conflicting goals compete with each other via natural language interactions. While adversarial language games are ubiquitous in human activities, little attention has been devoted to this field in natural language processing. In this work... | ['Yuan Yao', 'Zhengyan Zhang', 'Haoxi Zhong', 'Xiaozhi Wang', 'Maosong Sun', 'Guoyang Zeng', 'Zhiyuan Liu', 'Xu Han', 'Chaojun Xiao'] | 2019-11-05 | null | null | null | null | ['board-games'] | ['playing-games'] | [ 2.29531795e-01 5.63374460e-01 3.80657107e-01 7.03027323e-02
-6.03873253e-01 -1.38415396e+00 9.16923463e-01 -3.08469385e-02
-6.81284726e-01 4.75336224e-01 8.50269645e-02 -5.88411093e-01
2.87991524e-01 -8.96002650e-01 -1.36823207e-01 -6.22502387e-01
-3.05732995e-01 6.12484157e-01 2.54065961e-01 -8.58566165... | [6.079279899597168, 8.036239624023438] |
33547fa1-cae4-421c-9ef6-018a834c9964 | adaptive-learning-path-navigation-based-on | 2305.04475 | null | https://arxiv.org/abs/2305.04475v2 | https://arxiv.org/pdf/2305.04475v2.pdf | Adaptive Learning Path Navigation Based on Knowledge Tracing and Reinforcement Learning | This paper introduces the Adaptive Learning Path Navigation (ALPN) system, a novel approach for enhancing E-learning platforms by providing highly adaptive learning paths for students. The ALPN system integrates the Attentive Knowledge Tracing (AKT) model, which assesses students' knowledge states, with the proposed En... | ['I-Wei Lai', 'Saeed Saeedvand', 'Jyun-Yi Chen'] | 2023-05-08 | null | null | null | null | ['knowledge-tracing'] | ['miscellaneous'] | [-3.53899151e-01 2.46395439e-01 -6.49197996e-01 1.04632959e-01
-4.41328466e-01 -7.53253222e-01 3.19416851e-01 2.87827939e-01
-4.27764624e-01 8.97314668e-01 3.85616869e-01 -8.19577277e-01
-9.52520132e-01 -1.12944996e+00 -3.86833876e-01 -5.22989154e-01
1.36305839e-01 -2.43589492e-03 4.12442178e-01 -6.19725943... | [10.147920608520508, 7.139024257659912] |
d058d536-421a-4f6f-972f-a90bf5c18e9b | learning-a-latent-space-of-style-aware | 2001.05494 | null | https://arxiv.org/abs/2001.05494v2 | https://arxiv.org/pdf/2001.05494v2.pdf | Learning Style-Aware Symbolic Music Representations by Adversarial Autoencoders | We address the challenging open problem of learning an effective latent space for symbolic music data in generative music modeling. We focus on leveraging adversarial regularization as a flexible and natural mean to imbue variational autoencoders with context information concerning music genre and style. Through the pa... | ['Davide Bacciu', 'Antonio Carta', 'Andrea Valenti'] | 2020-01-15 | null | null | null | null | ['music-modeling'] | ['music'] | [-2.04359423e-02 2.97248214e-01 2.18164206e-01 1.78551316e-01
-6.20854497e-01 -8.82212162e-01 9.03088391e-01 -5.33192277e-01
-2.25670248e-01 5.55568337e-01 4.32405680e-01 3.66180688e-01
-2.12754130e-01 -8.55425417e-01 -1.23488975e+00 -9.37631011e-01
2.30161026e-01 8.35156739e-01 -7.13253394e-02 -3.30845803... | [15.785387992858887, 5.706925868988037] |
b29f6522-d0a2-4045-9992-bd8550562808 | towards-generalizable-surgical-activity | 2001.03728 | null | https://arxiv.org/abs/2001.03728v4 | https://arxiv.org/pdf/2001.03728v4.pdf | Towards Generalizable Surgical Activity Recognition Using Spatial Temporal Graph Convolutional Networks | Modeling and recognition of surgical activities poses an interesting research problem. Although a number of recent works studied automatic recognition of surgical activities, generalizability of these works across different tasks and different datasets remains a challenge. We introduce a modality that is robust to scen... | ['Pierre Jannin', 'Duygu Sarikaya'] | 2020-01-11 | null | null | null | null | ['surgical-gesture-recognition'] | ['medical'] | [ 3.22412878e-01 1.85279220e-01 -6.77003384e-01 6.72531575e-02
-6.34544611e-01 -5.98671317e-01 5.11639714e-01 -5.65203577e-02
-3.88785571e-01 1.13651693e-01 5.65197349e-01 -3.83714378e-01
-5.49932003e-01 -3.54230404e-01 -6.62895322e-01 -9.01772201e-01
-5.04827857e-01 2.80490249e-01 1.63408682e-01 4.37685363... | [14.06633472442627, -3.3518784046173096] |
6a6e0667-345c-4ff4-9fe3-6ed78f3672ab | a-regularized-implicit-policy-for-offline | 2202.09673 | null | https://arxiv.org/abs/2202.09673v2 | https://arxiv.org/pdf/2202.09673v2.pdf | A Behavior Regularized Implicit Policy for Offline Reinforcement Learning | Offline reinforcement learning enables learning from a fixed dataset, without further interactions with the environment. The lack of environmental interactions makes the policy training vulnerable to state-action pairs far from the training dataset and prone to missing rewarding actions. For training more effective age... | ['Mingyuan Zhou', 'Yihao Feng', 'Huangjie Zheng', 'Zhendong Wang', 'Shentao Yang'] | 2022-02-19 | null | null | null | null | ['d4rl'] | ['robots'] | [ 1.85373157e-01 2.61417091e-01 -6.23843968e-01 -2.43672177e-01
-7.51619875e-01 -8.78062665e-01 8.28767478e-01 -1.59126610e-01
-6.17508411e-01 1.08059514e+00 9.92598906e-02 -5.31906188e-01
-4.50023502e-01 -6.42633021e-01 -1.02265739e+00 -8.16634536e-01
-5.34676075e-01 2.30796739e-01 2.15958878e-02 -1.44010305... | [4.089123249053955, 2.0882740020751953] |
7093d30c-ef59-47d2-bd35-efe4dbc19088 | omnidet-surround-view-cameras-based-multi | 2102.07448 | null | https://arxiv.org/abs/2102.07448v3 | https://arxiv.org/pdf/2102.07448v3.pdf | OmniDet: Surround View Cameras based Multi-task Visual Perception Network for Autonomous Driving | Surround View fisheye cameras are commonly deployed in automated driving for 360\deg{} near-field sensing around the vehicle. This work presents a multi-task visual perception network on unrectified fisheye images to enable the vehicle to sense its surrounding environment. It consists of six primary tasks necessary for... | ['Ganesh Sistu', 'Patrick Mäder', 'Stefan Milz', 'Isabelle Leang', 'Christian Witt', 'Hazem Rashed', 'Senthil Yogamani', 'Varun Ravi Kumar'] | 2021-02-15 | null | null | null | null | ['motion-segmentation'] | ['computer-vision'] | [-6.79862574e-02 1.26601011e-01 -1.99629106e-02 -7.07922816e-01
-6.69004798e-01 -7.59974360e-01 5.74026167e-01 -6.28631711e-01
-5.82723975e-01 1.82503700e-01 -2.25388840e-01 -3.43100816e-01
3.77880335e-01 -4.16554987e-01 -1.22618568e+00 -6.15374148e-01
2.25403428e-01 2.69546270e-01 7.36053050e-01 -2.63362199... | [8.03696346282959, -1.8815594911575317] |
928b7003-abdf-4043-95a2-6d0301f72597 | spatial-pyramid-context-aware-moving-object | 1711.01656 | null | http://arxiv.org/abs/1711.01656v1 | http://arxiv.org/pdf/1711.01656v1.pdf | Spatial Pyramid Context-Aware Moving Object Detection and Tracking for Full Motion Video and Wide Aerial Motion Imagery | A robust and fast automatic moving object detection and tracking system is
essential to characterize target object and extract spatial and temporal
information for different functionalities including video surveillance systems,
urban traffic monitoring and navigation, robotic. In this dissertation, I
present a collabor... | ['Mahdieh Poostchi'] | 2017-11-05 | null | null | null | null | ['moving-object-detection'] | ['computer-vision'] | [ 1.67918339e-01 -9.74955916e-01 -1.13046981e-01 -1.53393283e-01
-5.34647703e-01 -9.09704030e-01 3.17695081e-01 -1.13745421e-01
-4.22841281e-01 3.20380896e-01 -1.80650100e-01 -1.48013860e-01
-2.79899120e-01 -7.08112478e-01 -5.29251158e-01 -9.84197617e-01
-1.15177624e-01 -1.35578543e-01 8.85355115e-01 -5.33857457... | [6.822399616241455, -1.8646867275238037] |
c93b08a1-e6f4-4627-ba6a-928436c004ad | seeing-the-forest-and-the-trees-detection-and-1 | null | null | https://aclanthology.org/2020.aespen-1.7 | https://aclanthology.org/2020.aespen-1.7.pdf | Seeing the Forest and the Trees: Detection and Cross-Document Coreference Resolution of Militarized Interstate Disputes | Previous efforts to automate the detection of social and political events in text have primarily focused on identifying events described within single sentences or documents. Within a corpus of documents, these automated systems are unable to link event references{---}recognize singular events across multiple sentences... | ['Benjamin Radford'] | 2020-05-01 | null | null | null | lrec-2020-5 | ['cross-document-coreference-resolution'] | ['natural-language-processing'] | [ 2.68055767e-01 1.08061209e-01 -3.15592974e-01 -6.30063951e-01
-1.55413032e+00 -1.01967585e+00 1.47815585e+00 8.86401713e-01
-8.71954858e-01 9.16765153e-01 1.04984117e+00 -3.22825760e-01
-6.08492613e-01 -6.29573762e-01 -4.57348973e-01 -1.64017856e-01
-1.87872630e-02 1.01825249e+00 2.39233319e-02 -4.04457003... | [9.143219947814941, 9.596538543701172] |
f9aed005-0a3d-4163-880a-a99e184a24a6 | augmentation-scheme-for-dealing-with | 1901.00204 | null | http://arxiv.org/abs/1901.00204v1 | http://arxiv.org/pdf/1901.00204v1.pdf | Augmentation Scheme for Dealing with Imbalanced Network Traffic Classification Using Deep Learning | One of the most important tasks in network management is identifying
different types of traffic flows. As a result, a type of management service,
called Network Traffic Classifier (NTC), has been introduced. One type of NTCs
that has gained huge attention in recent years applies deep learning on packets
in order to cla... | ['Ramin Hasibi', 'Mehdi Dehghan', 'Matin Shokri'] | 2019-01-01 | null | null | null | null | ['traffic-classification'] | ['miscellaneous'] | [-9.80426073e-02 -3.82940859e-01 -2.45466620e-01 -4.31672603e-01
-1.88833103e-01 -1.75276995e-01 7.07642436e-02 5.84024303e-02
-2.67447382e-01 7.88939595e-01 -3.60567510e-01 -7.35668600e-01
-2.69292861e-01 -1.19524717e+00 -6.08728170e-01 -5.68622530e-01
-8.41677785e-02 6.22763097e-01 2.18185976e-01 -5.69836050... | [5.065185070037842, 7.239945888519287] |
61bc86cf-9378-40d9-9fb6-c6b0cd2200c5 | gophormer-ego-graph-transformer-for-node | 2110.13094 | null | https://arxiv.org/abs/2110.13094v1 | https://arxiv.org/pdf/2110.13094v1.pdf | Gophormer: Ego-Graph Transformer for Node Classification | Transformers have achieved remarkable performance in a myriad of fields including natural language processing and computer vision. However, when it comes to the graph mining area, where graph neural network (GNN) has been the dominant paradigm, transformers haven't achieved competitive performance, especially on the no... | ['Yanfang Ye', 'Xing Xie', 'Hao Sun', 'Yuming Liu', 'Yiqi Wang', 'Qianlong Wen', 'Chaozhuo Li', 'Jianan Zhao'] | 2021-10-25 | null | null | null | null | ['graph-sampling'] | ['graphs'] | [ 4.58498113e-02 3.00336719e-01 -2.54985571e-01 -2.79977381e-01
-4.02110815e-01 -1.72985122e-01 5.33651233e-01 1.35348931e-01
-1.52206749e-01 5.51120460e-01 1.34128273e-01 -4.08558786e-01
-2.08879456e-01 -1.24583769e+00 -8.31766665e-01 -6.89061522e-01
1.35537043e-01 4.15543288e-01 2.87280083e-01 -1.67003036... | [7.23362398147583, 6.229283332824707] |
19ff7b32-22de-4ee1-ae49-6b312267bf79 | learn-dynamic-aware-state-embedding-for | 2101.02230 | null | https://arxiv.org/abs/2101.02230v1 | https://arxiv.org/pdf/2101.02230v1.pdf | Learn Dynamic-Aware State Embedding for Transfer Learning | Transfer reinforcement learning aims to improve the sample efficiency of solving unseen new tasks by leveraging experiences obtained from previous tasks. We consider the setting where all tasks (MDPs) share the same environment dynamic except reward function. In this setting, the MDP dynamic is a good knowledge to tran... | ['Kaige Yang'] | 2021-01-06 | null | null | null | null | ['transfer-reinforcement-learning'] | ['methodology'] | [-3.82279865e-02 1.44006193e-01 -4.20481384e-01 9.05077159e-03
-6.36191905e-01 -8.59864473e-01 5.44755042e-01 -6.33367524e-02
-7.32537985e-01 1.21618009e+00 -3.11573930e-02 -3.72532398e-01
-2.26936668e-01 -7.06569076e-01 -1.08263469e+00 -9.82539713e-01
-1.86564878e-01 4.48279411e-01 1.99818134e-01 -1.20795168... | [4.076898574829102, 1.9808650016784668] |
86734acb-5d5a-4216-863a-ae78bb4992c3 | a-novel-hybrid-deep-learning-approach-for-non | 2104.07809 | null | https://arxiv.org/abs/2104.07809v1 | https://arxiv.org/pdf/2104.07809v1.pdf | A Novel Hybrid Deep Learning Approach for Non-Intrusive Load Monitoring of Residential Appliance Based on Long Short Term Memory and Convolutional Neural Networks | Energy disaggregation or nonintrusive load monitoring (NILM), is a single-input blind source discrimination problem, aims to interpret the mains user electricity consumption into appliance level measurement. This article presents a new approach for power disaggregation by using a deep recurrent long short term memory (... | ['Sobhan Naderian'] | 2021-04-15 | null | null | null | null | ['non-intrusive-load-monitoring', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring'] | ['knowledge-base', 'miscellaneous', 'time-series'] | [ 1.05549090e-01 -2.13038951e-01 1.25025228e-01 -3.75053227e-01
-7.18753338e-01 -4.34607059e-01 5.62803209e-01 -3.57279368e-02
-1.20504126e-01 1.01334822e+00 3.09821904e-01 -1.95184484e-01
-2.43218511e-01 -1.02264428e+00 -3.45304072e-01 -1.03654015e+00
-7.51699880e-02 3.16834092e-01 -6.03897512e-01 -4.11645547... | [16.04917335510254, 7.566929817199707] |
7345832f-f64a-4b76-bde8-b37cd64db4f5 | adversarial-extreme-multi-label | 1803.01570 | null | http://arxiv.org/abs/1803.01570v1 | http://arxiv.org/pdf/1803.01570v1.pdf | Adversarial Extreme Multi-label Classification | The goal in extreme multi-label classification is to learn a classifier which
can assign a small subset of relevant labels to an instance from an extremely
large set of target labels. Datasets in extreme classification exhibit a long
tail of labels which have small number of positive training instances. In this
work, w... | ['Bernhard Schölkopf', 'Rohit Babbar'] | 2018-03-05 | null | null | null | null | ['extreme-multi-label-classification'] | ['methodology'] | [ 6.45165980e-01 3.18506956e-01 -3.67705852e-01 -4.30887222e-01
-1.46012676e+00 -8.73631895e-01 2.84208834e-01 6.94267035e-01
-2.80228198e-01 8.66560638e-01 -1.09092012e-01 -2.57159412e-01
-6.51870728e-01 -5.23742855e-01 -7.53075361e-01 -1.24879253e+00
-2.41537869e-01 5.69856703e-01 -2.50940353e-01 7.34199136... | [9.231246948242188, 4.27152156829834] |
8209e138-5fc5-4d3e-b13a-1acfa6661b4a | sleep-syndromes-onset-detection-based-on | 2107.03387 | null | https://arxiv.org/abs/2107.03387v1 | https://arxiv.org/pdf/2107.03387v1.pdf | Sleep syndromes onset detection based on automatic sleep staging algorithm | In this paper, we propose a novel method and a practical approach to predicting early onsets of sleep syndromes, including restless leg syndrome, insomnia, based on an algorithm that is comprised of two modules. A Fast Fourier Transform is applied to 30 seconds long epochs of EEG recordings to provide localized time-fr... | ['Tinkara Robek', 'Tim Cvetko'] | 2021-07-07 | null | null | null | null | ['sleep-staging'] | ['medical'] | [ 3.50148268e-02 -2.17057645e-01 6.39538243e-02 -5.52315414e-01
-3.39991271e-01 7.74788931e-02 7.72448331e-02 1.79179590e-02
-7.78250277e-01 1.06218624e+00 -1.39931574e-01 -5.61471544e-02
-3.65156204e-01 -4.72293168e-01 -1.60114422e-01 -7.55876720e-01
-5.62035799e-01 -3.02101439e-03 -2.76597682e-03 -3.82351205... | [13.518587112426758, 3.494802474975586] |
64bcad0e-d2e8-4b5a-8c1b-0d81b42722e1 | tclr-temporal-contrastive-learning-for-video | 2101.07974 | null | https://arxiv.org/abs/2101.07974v4 | https://arxiv.org/pdf/2101.07974v4.pdf | TCLR: Temporal Contrastive Learning for Video Representation | Contrastive learning has nearly closed the gap between supervised and self-supervised learning of image representations, and has also been explored for videos. However, prior work on contrastive learning for video data has not explored the effect of explicitly encouraging the features to be distinct across the temporal... | ['Mubarak Shah', 'Mamshad Nayeem Rizve', 'Rohit Gupta', 'Ishan Dave'] | 2021-01-20 | null | null | null | null | ['self-supervised-action-recognition'] | ['computer-vision'] | [ 4.45547312e-01 -3.87974948e-01 -6.94083035e-01 -3.97294641e-01
-1.05829263e+00 -4.04897571e-01 6.64912820e-01 -1.18426867e-01
-5.65692365e-01 6.48787439e-01 5.43874502e-01 2.15966851e-01
-1.46169752e-01 -3.98110330e-01 -9.29680526e-01 -8.55808794e-01
-4.18039769e-01 1.06220551e-01 3.62282127e-01 2.21945718... | [8.70442008972168, 0.7690211534500122] |
5e575b93-6391-4df2-9157-abebb4bc9759 | retroformer-pushing-the-limits-of | 2201.12475 | null | https://arxiv.org/abs/2201.12475v1 | https://arxiv.org/pdf/2201.12475v1.pdf | Retroformer: Pushing the Limits of Interpretable End-to-end Retrosynthesis Transformer | Retrosynthesis prediction is one of the fundamental challenges in organic synthesis. The task is to predict the reactants given a core product. With the advancement of machine learning, computer-aided synthesis planning has gained increasing interest. Numerous methods were proposed to solve this problem with different ... | ['Shengyu Zhang', 'Chang-Yu Hsieh', 'Benben Liao', 'Yue Wan'] | 2022-01-29 | null | null | null | null | ['retrosynthesis'] | ['medical'] | [ 4.59724903e-01 2.53960669e-01 -5.30337334e-01 -5.72414398e-02
-5.11658669e-01 -1.01044142e+00 9.62715387e-01 4.00092393e-01
4.64796536e-02 9.33141708e-01 3.67195427e-01 -4.97313678e-01
1.85780227e-01 -8.37507784e-01 -8.96610856e-01 -8.56375039e-01
3.58744293e-01 5.31938136e-01 -6.02662526e-02 -3.52892727... | [4.521842002868652, 6.092784404754639] |
0e91b190-82ba-4459-aada-7e932f5e813a | kga-a-general-machine-unlearning-framework | 2305.06535 | null | https://arxiv.org/abs/2305.06535v1 | https://arxiv.org/pdf/2305.06535v1.pdf | KGA: A General Machine Unlearning Framework Based on Knowledge Gap Alignment | Recent legislation of the "right to be forgotten" has led to the interest in machine unlearning, where the learned models are endowed with the function to forget information about specific training instances as if they have never existed in the training set. Previous work mainly focuses on computer vision scenarios and... | ['Hongzhi Yin', 'Kam-Fai Wong', 'Xingshan Zeng', 'Wei Yuan', 'Tong Chen', 'Lingzhi Wang'] | 2023-05-11 | null | null | null | null | ['response-generation'] | ['natural-language-processing'] | [ 3.55538607e-01 2.60672271e-01 -4.15927798e-01 -3.66515577e-01
-5.63883901e-01 -5.99265456e-01 6.44774556e-01 3.38425636e-02
-4.33929116e-01 1.17046630e+00 2.82773107e-01 -4.39823091e-01
-1.63426250e-01 -7.41945565e-01 -9.70257759e-01 -8.57153535e-01
6.22427642e-01 4.32437837e-01 -6.81070462e-02 -2.64511146... | [9.8482666015625, 3.446613311767578] |
4907ae29-d1f2-4bdc-aabe-ae064009b71f | deep-semantic-parsing-of-freehand-sketches | 1910.06023 | null | https://arxiv.org/abs/1910.06023v2 | https://arxiv.org/pdf/1910.06023v2.pdf | Deep Semantic Parsing of Freehand Sketches with Homogeneous Transformation, Soft-Weighted Loss, and Staged Learning | In this paper, we propose a novel deep framework for part-level semantic parsing of freehand sketches, which makes three main contributions that are experimentally shown to have substantial practical merit. First, we propose a homogeneous transformation method to address the problem of domain adaptation. For the task o... | ['Xiaoshuai Sun', 'Ying Zheng', 'Hongxun Yao'] | 2019-10-14 | null | null | null | null | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 3.40322167e-01 -1.92352995e-01 -4.42576885e-01 -7.38026321e-01
-1.06701612e+00 -5.30457020e-01 3.94781798e-01 -3.80345374e-01
-1.58475563e-01 4.41438645e-01 -4.07304280e-02 3.72214913e-02
9.74616259e-02 -9.72279191e-01 -8.13700140e-01 -6.09393358e-01
5.76683640e-01 5.58181107e-01 2.29896367e-01 -4.41838838... | [11.644886016845703, 0.633401095867157] |
071836e6-572b-4a03-a866-d15ef46f6f14 | liga-stereo-learning-lidar-geometry-aware | 2108.08258 | null | https://arxiv.org/abs/2108.08258v1 | https://arxiv.org/pdf/2108.08258v1.pdf | LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D Detector | Stereo-based 3D detection aims at detecting 3D object bounding boxes from stereo images using intermediate depth maps or implicit 3D geometry representations, which provides a low-cost solution for 3D perception. However, its performance is still inferior compared with LiDAR-based detection algorithms. To detect and lo... | ['Hongsheng Li', 'Xiaogang Wang', 'Shaoshuai Shi', 'Xiaoyang Guo'] | 2021-08-18 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Guo_LIGA-Stereo_Learning_LiDAR_Geometry_Aware_Representations_for_Stereo-Based_3D_Detector_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Guo_LIGA-Stereo_Learning_LiDAR_Geometry_Aware_Representations_for_Stereo-Based_3D_Detector_ICCV_2021_paper.pdf | iccv-2021-1 | ['3d-object-detection-from-stereo-images'] | ['computer-vision'] | [-7.55851790e-02 -4.88408394e-02 8.29264298e-02 -4.93150294e-01
-7.11152971e-01 -5.05453467e-01 4.78601158e-01 2.10463122e-01
-3.85122836e-01 1.80392280e-01 -2.71125615e-01 -2.65948772e-01
4.55513597e-01 -1.06393993e+00 -8.70244205e-01 -2.40934983e-01
1.15366012e-01 8.84922385e-01 1.31643867e+00 -2.52260894... | [7.7591705322265625, -2.6200647354125977] |
a6ea4825-9cf0-492e-bda4-2e59ab080e8f | improved-zero-shot-audio-tagging | 2208.11402 | null | https://arxiv.org/abs/2208.11402v1 | https://arxiv.org/pdf/2208.11402v1.pdf | Improved Zero-Shot Audio Tagging & Classification with Patchout Spectrogram Transformers | Standard machine learning models for tagging and classifying acoustic signals cannot handle classes that were not seen during training. Zero-Shot (ZS) learning overcomes this restriction by predicting classes based on adaptable class descriptions. This study sets out to investigate the effectiveness of self-attention-b... | ['Gerhard Widmer', 'Paul Primus'] | 2022-08-24 | null | null | null | null | ['audio-tagging', 'environmental-sound-classification', 'sound-classification'] | ['audio', 'audio', 'audio'] | [ 2.74892062e-01 1.99358642e-01 8.39436874e-02 -4.72046673e-01
-9.53433156e-01 -6.42141223e-01 4.18349057e-01 1.88117325e-01
-5.08834064e-01 3.97891015e-01 3.15821975e-01 -6.97092339e-02
-7.37045631e-02 -5.86419046e-01 -5.02358913e-01 -5.28923512e-01
-3.85471106e-01 2.31148571e-01 5.65478086e-01 -1.26426771... | [15.262622833251953, 5.203197002410889] |
5564d573-3375-4914-b7e0-19eec8ac510b | knowledge-graph-self-supervised | 2307.02759 | null | https://arxiv.org/abs/2307.02759v1 | https://arxiv.org/pdf/2307.02759v1.pdf | Knowledge Graph Self-Supervised Rationalization for Recommendation | In this paper, we introduce a new self-supervised rationalization method, called KGRec, for knowledge-aware recommender systems. To effectively identify informative knowledge connections, we propose an attentive knowledge rationalization mechanism that generates rational scores for knowledge triplets. With these scores... | ['Chunzhen Huang', 'Lianghao Xia', 'Chao Huang', 'Yuhao Yang'] | 2023-07-06 | null | null | null | null | ['contrastive-learning', 'graph-learning', 'contrastive-learning'] | ['computer-vision', 'graphs', 'methodology'] | [ 4.74826247e-02 5.55367887e-01 -4.97818738e-01 -2.26900890e-01
-4.45718259e-01 -5.54242074e-01 2.67410100e-01 -1.72464084e-02
2.41340831e-01 6.60492361e-01 5.92207968e-01 -1.59896575e-02
-5.78241050e-01 -9.94789362e-01 -7.95402050e-01 -4.05281991e-01
2.32906695e-02 1.52057499e-01 -1.02357075e-01 2.06584972... | [10.244647979736328, 5.616556644439697] |
d6d2497b-7186-40d7-b20f-c9cf1fb97847 | mednc-multi-ensemble-deep-neural-network-for | 2304.13135 | null | https://arxiv.org/abs/2304.13135v1 | https://arxiv.org/pdf/2304.13135v1.pdf | MEDNC: Multi-ensemble deep neural network for COVID-19 diagnosis | Coronavirus disease 2019 (COVID-19) has spread all over the world for three years, but medical facilities in many areas still aren't adequate. There is a need for rapid COVID-19 diagnosis to identify high-risk patients and maximize the use of limited medical resources. Motivated by this fact, we proposed the deep learn... | ['Yudong Zhang', 'Shuihua Wang', 'Lin Yang'] | 2023-04-25 | null | null | null | null | ['covid-19-detection', 'computed-tomography-ct'] | ['medical', 'methodology'] | [-1.42695522e-02 -3.39416236e-01 7.12097958e-02 -1.70462519e-01
-5.01919568e-01 -2.84931809e-01 2.46350795e-01 2.20956534e-01
-6.51984692e-01 7.36292005e-01 -9.28789750e-02 -3.99755806e-01
4.09538038e-02 -6.64605021e-01 -1.74774721e-01 -7.45761871e-01
-1.25184298e-01 9.73124087e-01 -7.53387064e-02 1.55933753... | [15.56867790222168, -1.7008709907531738] |
6dd25c45-7953-43f3-aa8a-dafe32ded9aa | dynadog-t-a-parametric-animal-model-for | 2107.07330 | null | https://arxiv.org/abs/2107.07330v2 | https://arxiv.org/pdf/2107.07330v2.pdf | DynaDog+T: A Parametric Animal Model for Synthetic Canine Image Generation | Synthetic data is becoming increasingly common for training computer vision models for a variety of tasks. Notably, such data has been applied in tasks related to humans such as 3D pose estimation where data is either difficult to create or obtain in realistic settings. Comparatively, there has been less work into synt... | ['Darren Cosker', 'Kwang In Kim', 'Sinead Kearney', 'Jake Deane'] | 2021-07-15 | null | null | null | null | ['3d-pose-estimation'] | ['computer-vision'] | [ 2.05594912e-01 3.80787283e-01 -1.12266473e-01 -4.48345572e-01
-1.98186651e-01 -2.23215684e-01 6.52231097e-01 7.23908842e-02
-5.61356902e-01 7.04512775e-01 -1.43124700e-01 -2.45380178e-01
5.08870542e-01 -4.12927389e-01 -7.11231053e-01 -3.54337603e-01
4.07922059e-01 6.99078321e-01 2.61633366e-01 -2.19367847... | [7.5688395500183105, -1.0943937301635742] |
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