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
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
4267d6df-f76d-4a5d-8b92-18fa1751d458 | ia-gcn-interpretable-attention-based-graph | 2103.15587 | null | https://arxiv.org/abs/2103.15587v1 | https://arxiv.org/pdf/2103.15587v1.pdf | IA-GCN: Interpretable Attention based Graph Convolutional Network for Disease prediction | Interpretability in Graph Convolutional Networks (GCNs) has been explored to some extent in computer vision in general, yet, in the medical domain, it requires further examination. Moreover, most of the interpretability approaches for GCNs, especially in the medical domain, focus on interpreting the model in a post hoc... | ['Nassir Navab', 'Soroush Farghadani', 'Anees Kazi'] | 2021-03-29 | null | null | null | null | ['gender-prediction'] | ['computer-vision'] | [ 4.36604768e-01 1.04113901e+00 -2.69206822e-01 -5.49207568e-01
-4.05048043e-01 -1.60105452e-01 4.35531795e-01 7.41058111e-01
-2.05927402e-01 4.75007176e-01 3.79511505e-01 -5.71516991e-01
-5.44749737e-01 -5.28978050e-01 -5.02817333e-01 -7.17952073e-01
-2.45108694e-01 9.95998085e-01 -3.53548676e-01 -1.68715324... | [8.584817886352539, 5.697046279907227] |
e059c5cc-eb69-4cad-9d35-1a7a041c0ab0 | select-good-regions-for-deblurring-based-on | 2008.05065 | null | https://arxiv.org/abs/2008.05065v1 | https://arxiv.org/pdf/2008.05065v1.pdf | Select Good Regions for Deblurring based on Convolutional Neural Networks | The goal of blind image deblurring is to recover sharp image from one input blurred image with an unknown blur kernel. Most of image deblurring approaches focus on developing image priors, however, there is not enough attention to the influence of image details and structures on the blur kernel estimation. What is the ... | ['Xiaotian Wu', 'Xinglong Sun', 'Hang Yang'] | 2020-08-12 | null | null | null | null | ['blind-image-deblurring'] | ['computer-vision'] | [ 1.55542925e-01 -4.85942125e-01 -1.94021482e-02 -1.64628446e-01
-3.17787409e-01 -5.05873144e-01 2.19585836e-01 -6.95677102e-01
-1.04520231e-01 7.44338691e-01 5.70868015e-01 -6.55649826e-02
-1.73194036e-01 -3.63356352e-01 -6.64399743e-01 -9.28223968e-01
2.69413173e-01 -1.95603848e-01 7.46015012e-02 1.53288543... | [11.618948936462402, -2.775852918624878] |
7204fb7a-71ac-41b9-bffb-293a00a916f6 | pas-mef-multi-exposure-image-fusion-based-on | 2105.11809 | null | https://arxiv.org/abs/2105.11809v1 | https://arxiv.org/pdf/2105.11809v1.pdf | PAS-MEF: Multi-exposure image fusion based on principal component analysis, adaptive well-exposedness and saliency map | High dynamic range (HDR) imaging enables to immortalize natural scenes similar to the way that they are perceived by human observers. With regular low dynamic range (LDR) capture/display devices, significant details may not be preserved in images due to the huge dynamic range of natural scenes. To minimize the informat... | ['Mehmet Turkan', 'Oguzhan Ulucan', 'Diclehan Karakaya'] | 2021-05-25 | null | null | null | null | ['multi-exposure-image-fusion'] | ['computer-vision'] | [ 6.95042312e-01 -4.28056329e-01 3.38991702e-01 -3.30857784e-02
-1.28335923e-01 -2.18641862e-01 4.63614136e-01 -8.39212239e-02
-3.04373682e-01 6.56728029e-01 2.76970595e-01 -6.78898990e-02
-1.80057794e-01 -7.24123776e-01 -3.18512142e-01 -6.36166990e-01
2.48718366e-01 -4.18161452e-01 7.48346448e-01 -3.65827292... | [10.871384620666504, -2.4555258750915527] |
438e1797-a105-4710-adae-36feae5784a4 | an-empirical-study-of-remote-sensing | 2204.02825 | null | https://arxiv.org/abs/2204.02825v4 | https://arxiv.org/pdf/2204.02825v4.pdf | An Empirical Study of Remote Sensing Pretraining | Deep learning has largely reshaped remote sensing (RS) research for aerial image understanding and made a great success. Nevertheless, most of the existing deep models are initialized with the ImageNet pretrained weights. Since natural images inevitably present a large domain gap relative to aerial images, probably lim... | ['DaCheng Tao', 'Gui-Song Xia', 'Bo Du', 'Jing Zhang', 'Di Wang'] | 2022-04-06 | null | null | null | null | ['object-detection-in-aerial-images', 'scene-recognition', 'change-detection-for-remote-sensing-images', 'building-change-detection-for-remote-sensing'] | ['computer-vision', 'computer-vision', 'miscellaneous', 'miscellaneous'] | [ 6.15162790e-01 -2.87536472e-01 8.83178264e-02 -5.79688907e-01
-2.45788619e-01 -7.95904517e-01 4.09639239e-01 -3.28497112e-01
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1.03101037e-01 -1.66609824e-01 1.86002031e-01 -6.04106307... | [9.442755699157715, -1.0972907543182373] |
458ef21e-342c-405f-ad1b-af31e890c9e0 | self-trained-one-class-classification-for | 2106.06115 | null | https://arxiv.org/abs/2106.06115v2 | https://arxiv.org/pdf/2106.06115v2.pdf | Self-supervise, Refine, Repeat: Improving Unsupervised Anomaly Detection | Anomaly detection (AD), separating anomalies from normal data, has many applications across domains, from security to healthcare. While most previous works were shown to be effective for cases with fully or partially labeled data, that setting is in practice less common due to labeling being particularly tedious for th... | ['Tomas Pfister', 'Chen-Yu Lee', 'Sercan O. Arik', 'Chun-Liang Li', 'Kihyuk Sohn', 'Jinsung Yoon'] | 2021-06-11 | self-supervise-refine-repeat-improving | https://openreview.net/forum?id=Nct9j3BVswZ | https://openreview.net/pdf?id=Nct9j3BVswZ | null | ['one-class-classifier', 'one-class-classification'] | ['methodology', 'miscellaneous'] | [ 3.94524992e-01 2.48075604e-01 -1.16800837e-01 -6.41612470e-01
-8.46727848e-01 -2.98064172e-01 4.14014786e-01 5.80295205e-01
-3.86169374e-01 6.26552343e-01 -2.05009386e-01 -6.20653629e-02
-8.08834285e-02 -6.38150871e-01 -5.42815030e-01 -8.14181864e-01
-8.71512070e-02 5.92117786e-01 3.04364502e-01 -4.05535474... | [7.647970676422119, 2.3168704509735107] |
8bccdc91-f6e8-4f71-b45e-6d16e2438440 | rethinking-deep-face-restoration | null | null | https://openreview.net/forum?id=-AY7C3f26C_ | https://openreview.net/pdf?id=-AY7C3f26C_ | Rethinking Deep Face Restoration | A model that can authentically restore a low-quality face image to a high-quality one can benefit many applications.
While existing approaches for face restoration make significant progress in generating high-quality faces, they often fail to preserve facial features and cannot authentically reconstruct the faces. Beca... | ['Xuhui Jia', 'Changyou Chen', 'Yukun Zhu', 'Marius Renn', 'Yandong Li', 'Chun-Te Chu', 'Yu-Chuan Su', 'Yang Zhao'] | 2021-09-29 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Zhao_Rethinking_Deep_Face_Restoration_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Zhao_Rethinking_Deep_Face_Restoration_CVPR_2022_paper.pdf | cvpr-2022-1 | ['face-reconstruction'] | ['computer-vision'] | [ 3.04278582e-01 -7.75750801e-02 -4.34407871e-03 -6.00652874e-01
-7.11694300e-01 -2.61842728e-01 4.80703533e-01 -5.33540845e-01
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2.41623923e-01 -1.26115099e-01 -2.65794456e-01 -3.58784825... | [12.813791275024414, -0.03545837104320526] |
44cb9471-37a4-4fbb-bbdb-5931b29c593f | use-of-variational-inference-in-music-emotion | 2106.14323 | null | https://arxiv.org/abs/2106.14323v2 | https://arxiv.org/pdf/2106.14323v2.pdf | Use of Variational Inference in Music Emotion Recognition | This work was developed aiming to employ Statistical techniques to the field of Music Emotion Recognition, a well-recognized area within the Signal Processing world, but hardly explored from the statistical point of view. Here, we opened several possibilities within the field, applying modern Bayesian Statistics techni... | ['Hugo Tremonte de Carvalho', 'Nathalie Deziderio'] | 2021-06-27 | null | null | null | null | ['music-emotion-recognition'] | ['music'] | [ 2.96088487e-01 -1.07810684e-01 2.37495258e-01 -1.66336104e-01
-4.11015630e-01 -3.13206315e-01 3.05801332e-01 1.73774332e-01
-6.35677457e-01 3.98866326e-01 2.38941163e-01 -1.21638149e-01
-7.20528185e-01 -6.83543265e-01 -3.45420778e-01 -8.83173704e-01
-1.07289441e-01 3.21945637e-01 8.19267146e-03 -2.92998105... | [15.799025535583496, 5.330885410308838] |
307ce705-6239-4d92-a24d-f05ed3a9a100 | deep-sketch-shape-hashing-with-segmented-3d | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Chen_Deep_Sketch-Shape_Hashing_With_Segmented_3D_Stochastic_Viewing_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Chen_Deep_Sketch-Shape_Hashing_With_Segmented_3D_Stochastic_Viewing_CVPR_2019_paper.pdf | Deep Sketch-Shape Hashing With Segmented 3D Stochastic Viewing | Sketch-based 3D shape retrieval has been extensively studied in recent works, most of which focus on improving the retrieval accuracy, whilst neglecting the efficiency. In this paper, we propose a novel framework for efficient sketch-based 3D shape retrieval, i.e., Deep Sketch-Shape Hashing (DSSH), which tackles the ch... | [' Ling Shao', ' Jin Xie', ' Fumin Shen', ' Fan Zhu', ' Li Liu', ' Jie Qin', 'Jiaxin Chen'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['3d-shape-retrieval', '3d-shape-representation'] | ['computer-vision', 'computer-vision'] | [ 5.55988029e-02 -3.75736415e-01 -2.20148653e-01 -2.42012843e-01
-1.14231193e+00 -6.85085118e-01 6.94228709e-01 7.24804476e-02
-5.88788539e-02 2.37036161e-02 1.07557885e-01 -1.35157658e-02
-3.16003770e-01 -7.88572907e-01 -4.74991918e-01 -8.29328299e-01
1.10707887e-01 7.63626814e-01 1.94384143e-01 1.35293633... | [8.164592742919922, -3.8953781127929688] |
c2adb14e-f565-469b-b67f-160891e1c4b7 | neur2sp-neural-two-stage-stochastic | 2205.12006 | null | https://arxiv.org/abs/2205.12006v2 | https://arxiv.org/pdf/2205.12006v2.pdf | Neur2SP: Neural Two-Stage Stochastic Programming | Stochastic Programming is a powerful modeling framework for decision-making under uncertainty. In this work, we tackle two-stage stochastic programs (2SPs), the most widely used class of stochastic programming models. Solving 2SPs exactly requires optimizing over an expected value function that is computationally intra... | ['Merve Bodur', 'Elias B. Khalil', 'Rahul Patel', 'Justin Dumouchelle'] | 2022-05-20 | null | null | null | null | ['decision-making-under-uncertainty', 'decision-making-under-uncertainty'] | ['medical', 'reasoning'] | [ 2.47091115e-01 2.46280625e-01 -3.91914010e-01 -3.65789622e-01
-1.43546891e+00 -8.18542480e-01 -2.34845579e-01 9.46389958e-02
-2.28173986e-01 1.13862503e+00 -2.52766758e-01 -6.71319306e-01
-5.51585972e-01 -8.48658979e-01 -1.01171076e+00 -8.21169436e-01
-1.41673207e-01 8.57368350e-01 -8.91979188e-02 -5.88608161... | [5.681464672088623, 3.560732841491699] |
4187727f-9587-4471-8410-ef3481497ba6 | deepproblog-neural-probabilistic-logic | 1805.10872 | null | http://arxiv.org/abs/1805.10872v2 | http://arxiv.org/pdf/1805.10872v2.pdf | DeepProbLog: Neural Probabilistic Logic Programming | We introduce DeepProbLog, a probabilistic logic programming language that
incorporates deep learning by means of neural predicates. We show how existing
inference and learning techniques can be adapted for the new language. Our
experiments demonstrate that DeepProbLog supports both symbolic and subsymbolic
representati... | ['Sebastijan Dumančić', 'Luc De Raedt', 'Angelika Kimmig', 'Robin Manhaeve', 'Thomas Demeester'] | 2018-05-28 | deepproblog-neural-probabilistic-logic-1 | http://papers.nips.cc/paper/7632-deepproblog-neural-probabilistic-logic-programming | http://papers.nips.cc/paper/7632-deepproblog-neural-probabilistic-logic-programming.pdf | neurips-2018-12 | ['program-induction'] | ['computer-code'] | [-9.27130580e-02 6.32661343e-01 -7.48989284e-01 -7.30968356e-01
-5.97337544e-01 -4.41008866e-01 9.47668254e-01 2.23263994e-01
-1.62642762e-01 8.86344731e-01 1.39879048e-01 -7.12558508e-01
-3.87728244e-01 -1.47562373e+00 -1.10238194e+00 3.85204442e-02
-3.92338991e-01 1.19459581e+00 5.16218245e-01 -5.64788282... | [8.768558502197266, 7.093825817108154] |
78526357-edfb-4a90-9328-f37c8f1e01fb | loss-optimal-classification-trees-a | 2306.00857 | null | https://arxiv.org/abs/2306.00857v1 | https://arxiv.org/pdf/2306.00857v1.pdf | Loss-Optimal Classification Trees: A Generalized Framework and the Logistic Case | The Classification Tree (CT) is one of the most common models in interpretable machine learning. Although such models are usually built with greedy strategies, in recent years, thanks to remarkable advances in Mixer-Integer Programming (MIP) solvers, several exact formulations of the learning problem have been develope... | ['Matteo Lapucci', 'Tommaso Aldinucci'] | 2023-06-01 | null | null | null | null | ['interpretable-machine-learning'] | ['methodology'] | [ 3.44446778e-01 6.17955685e-01 -4.61602181e-01 -4.89337355e-01
-7.67672718e-01 -4.70248789e-01 4.78502661e-01 4.36809868e-01
-1.82782635e-01 8.16695094e-01 -2.16551676e-01 -3.98828924e-01
-7.00681925e-01 -8.80246222e-01 -7.79942632e-01 -7.51192331e-01
-1.00206330e-01 7.85552919e-01 -2.14207023e-01 -1.22140452... | [8.730076789855957, 4.220551490783691] |
50641a7b-b92a-459d-afd6-27dc849b003c | native-language-identification-with-user | null | null | https://aclanthology.org/D18-1395 | https://aclanthology.org/D18-1395.pdf | Native Language Identification with User Generated Content | We address the task of native language identification in the context of social media content, where authors are highly-fluent, advanced nonnative speakers (of English). Using both linguistically-motivated features and the characteristics of the social media outlet, we obtain high accuracy on this challenging task. We p... | ['Shuly Wintner', 'Gili Goldin', 'Ella Rabinovich'] | 2018-10-01 | null | null | null | emnlp-2018-10 | ['native-language-identification'] | ['natural-language-processing'] | [-4.3919215e-01 -2.4276318e-01 -6.5493196e-01 -2.0089450e-01
-8.4016526e-01 -1.0240122e+00 8.0849999e-01 4.5370755e-01
-8.6580014e-01 3.1732568e-01 6.0806793e-01 -6.8755096e-01
2.1870095e-01 -4.1007778e-01 -9.1615148e-02 -9.7605899e-02
-1.8031636e-02 -5.4844242e-02 -3.3462903e-01 -1.7180908e-01
2.0850372e-01... | [10.396197319030762, 10.394063949584961] |
abed8583-8e8d-4410-8282-92c07fd2035c | multi-target-embodied-question-answering | 1904.04686 | null | http://arxiv.org/abs/1904.04686v1 | http://arxiv.org/pdf/1904.04686v1.pdf | Multi-Target Embodied Question Answering | Embodied Question Answering (EQA) is a relatively new task where an agent is
asked to answer questions about its environment from egocentric perception. EQA
makes the fundamental assumption that every question, e.g., "what color is the
car?", has exactly one target ("car") being inquired about. This assumption
puts a d... | ['Mohit Bansal', 'Tamara L. Berg', 'Licheng Yu', 'Dhruv Batra', 'Xinlei Chen', 'Georgia Gkioxari'] | 2019-04-09 | multi-target-embodied-question-answering-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Yu_Multi-Target_Embodied_Question_Answering_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Yu_Multi-Target_Embodied_Question_Answering_CVPR_2019_paper.pdf | cvpr-2019-6 | ['embodied-question-answering'] | ['computer-vision'] | [-7.95087963e-02 3.13081235e-01 2.84321100e-01 -4.62404281e-01
-7.30734587e-01 -1.03725529e+00 4.32272941e-01 1.52186483e-01
-4.08473074e-01 3.98534775e-01 1.87812373e-01 -7.44511068e-01
-2.05309868e-01 -1.09594452e+00 -8.43261540e-01 -4.54319656e-01
2.14444399e-01 8.27859759e-01 3.52632642e-01 -6.79532766... | [4.370973587036133, 0.6513186693191528] |
f283d877-0e87-4d2f-9ba0-38b294fe4b56 | information-extraction-from-documents | 2304.10994 | null | https://arxiv.org/abs/2304.10994v1 | https://arxiv.org/pdf/2304.10994v1.pdf | Information Extraction from Documents: Question Answering vs Token Classification in real-world setups | Research in Document Intelligence and especially in Document Key Information Extraction (DocKIE) has been mainly solved as Token Classification problem. Recent breakthroughs in both natural language processing (NLP) and computer vision helped building document-focused pre-training methods, leveraging a multimodal under... | ['Fabien Caspani', 'William Vanhuffel', 'Joël Tang', 'Pirashanth Ratnamogan', 'Laurent Lam'] | 2023-04-21 | null | null | null | null | ['key-information-extraction', 'reading-comprehension', 'machine-reading-comprehension'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 5.93248010e-01 1.30585566e-01 1.50629148e-01 -1.51942849e-01
-1.13883853e+00 -6.56640589e-01 9.76379097e-01 8.53219867e-01
-5.29759228e-01 6.66646540e-01 3.96240801e-01 -3.28855842e-01
-3.94494087e-01 -8.83849740e-01 -3.81813198e-01 -5.20929217e-01
1.58685982e-01 5.30496061e-01 3.70382369e-01 -3.35343510... | [11.685791969299316, 2.8630433082580566] |
8adc749b-f97b-4c0e-b984-244b8f511a6c | improving-faithfulness-of-abstractive | 2212.09726 | null | https://arxiv.org/abs/2212.09726v1 | https://arxiv.org/pdf/2212.09726v1.pdf | Improving Faithfulness of Abstractive Summarization by Controlling Confounding Effect of Irrelevant Sentences | Lack of factual correctness is an issue that still plagues state-of-the-art summarization systems despite their impressive progress on generating seemingly fluent summaries. In this paper, we show that factual inconsistency can be caused by irrelevant parts of the input text, which act as confounders. To that end, we l... | ['Asli Celikyilmaz', 'Scott Wen-tau Yih', 'Yashar Mehdad', 'Lili Yu', 'Haoran Li', 'Ankit Arun', 'Arash Einolghozati', 'Asish Ghoshal'] | 2022-12-19 | null | null | null | null | ['abstractive-text-summarization'] | ['natural-language-processing'] | [ 3.49032789e-01 6.10221922e-01 -2.64642626e-01 -2.79950023e-01
-1.44233286e+00 -7.98367560e-01 1.02364218e+00 4.74056393e-01
-4.40885127e-01 1.13063467e+00 1.24612641e+00 -1.68750182e-01
-4.14774800e-03 -5.02464950e-01 -8.96900713e-01 -2.07410783e-01
1.61145285e-01 4.36568111e-01 6.01145066e-02 -4.99048799... | [12.25846004486084, 9.329999923706055] |
006f874f-b699-4d35-b956-dc41220b81df | early-guessing-for-dialect-identification | null | null | https://openreview.net/forum?id=mdAhC06ICCb | https://openreview.net/pdf?id=mdAhC06ICCb | Early Guessing for Dialect Identification | This paper deals with the problem of incremental dialect identification. Our goal is to reliably determine the dialect before the full utterance is given as input. The major part of the previous research on dialect identification has been model-centric with a focus on performance. We address a new question: How much in... | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['dialect-identification'] | ['natural-language-processing'] | [ 1.68318957e-01 -3.36227603e-02 -1.82026699e-02 -7.72368789e-01
-1.05000985e+00 -1.06359780e+00 5.30335128e-01 1.36310548e-01
-4.24506605e-01 3.62479508e-01 2.27509186e-01 -8.02737236e-01
-2.98655108e-02 -4.08733398e-01 -2.21782446e-01 -5.69115639e-01
3.55818629e-01 9.27181184e-01 2.56130487e-01 -7.04680264... | [14.192214012145996, 6.694639205932617] |
60b595ba-e8bf-4dd2-bc67-555791dbb960 | cloud-detection-algorithm-for-remote-sensing | 1810.05782 | null | http://arxiv.org/abs/1810.05782v1 | http://arxiv.org/pdf/1810.05782v1.pdf | Cloud Detection Algorithm for Remote Sensing Images Using Fully Convolutional Neural Networks | This paper presents a deep-learning based framework for addressing the
problem of accurate cloud detection in remote sensing images. This framework
benefits from a Fully Convolutional Neural Network (FCN), which is capable of
pixel-level labeling of cloud regions in a Landsat 8 image. Also, a
gradient-based identificat... | ['Sorour Mohajerani', 'Thomas A. Krammer', 'Parvaneh Saeedi'] | 2018-10-13 | null | null | null | null | ['cloud-detection'] | ['computer-vision'] | [ 2.23603576e-01 -3.90945852e-01 3.43862951e-01 -4.58865196e-01
-7.36671031e-01 -6.98147833e-01 3.02942544e-01 1.75883591e-01
-5.49039423e-01 7.66161919e-01 -5.97208977e-01 -6.22936249e-01
-6.70278817e-02 -1.33951890e+00 -5.99733949e-01 -1.01449013e+00
-1.96934640e-01 2.74391681e-01 1.41406478e-02 -2.29574228... | [9.677370071411133, -1.6471331119537354] |
dd501312-096d-489b-8b0c-674f7cf7cfa8 | comprehensive-fair-meta-learned-recommender | 2206.04789 | null | https://arxiv.org/abs/2206.04789v1 | https://arxiv.org/pdf/2206.04789v1.pdf | Comprehensive Fair Meta-learned Recommender System | In recommender systems, one common challenge is the cold-start problem, where interactions are very limited for fresh users in the systems. To address this challenge, recently, many works introduce the meta-optimization idea into the recommendation scenarios, i.e. learning to learn the user preference by only a few pas... | ['Jingrui He', 'Tianxin Wei'] | 2022-06-09 | null | null | null | null | ['general-knowledge'] | ['miscellaneous'] | [-2.78698355e-01 -2.72142857e-01 -4.56001401e-01 -5.20979464e-01
-5.88650823e-01 -5.41456223e-01 3.94190431e-01 -3.52521598e-01
-4.71222669e-01 9.61847603e-01 2.21373707e-01 -1.36182696e-01
-3.23442787e-01 -7.21165299e-01 -3.93684208e-01 -8.90101969e-01
-1.99575983e-02 3.18139851e-01 -3.52078080e-01 -5.42053998... | [9.707939147949219, 5.582244396209717] |
76f35f18-f6d2-4d96-9e88-aa287802e4a3 | a-new-speech-feature-fusion-method-with-cross | 2211.13377 | null | https://arxiv.org/abs/2211.13377v1 | https://arxiv.org/pdf/2211.13377v1.pdf | A new Speech Feature Fusion method with cross gate parallel CNN for Speaker Recognition | In this paper, a new speech feature fusion method is proposed for speaker recognition on the basis of the cross gate parallel convolutional neural network (CG-PCNN). The Mel filter bank features (MFBFs) of different frequency resolutions can be extracted from each speech frame of a speaker's speech by several Mel filte... | ['Ye Zhang', 'Wenyi Yan', 'Jiacheng Zhang'] | 2022-11-24 | null | null | null | null | ['speaker-recognition'] | ['speech'] | [-3.74202207e-02 -4.61917400e-01 1.61977708e-01 -3.89431566e-01
-5.63452721e-01 -6.54323101e-02 4.23654824e-01 -2.27804080e-01
-3.30745488e-01 2.42237538e-01 4.88514304e-01 -2.04298869e-01
1.67518675e-01 -6.15211248e-01 -2.63468593e-01 -9.81802046e-01
1.54567018e-01 -4.95096266e-01 2.22432166e-01 -3.83811831... | [14.466071128845215, 6.005220413208008] |
b8ac3bbd-52cf-4e0e-ad3e-af4c0c3a8ea7 | data-structures-for-density-estimation | 2306.11312 | null | https://arxiv.org/abs/2306.11312v1 | https://arxiv.org/pdf/2306.11312v1.pdf | Data Structures for Density Estimation | We study statistical/computational tradeoffs for the following density estimation problem: given $k$ distributions $v_1, \ldots, v_k$ over a discrete domain of size $n$, and sampling access to a distribution $p$, identify $v_i$ that is "close" to $p$. Our main result is the first data structure that, given a sublinear ... | ['Sandeep Silwal', 'Shyam Narayanan', 'Piotr Indyk', 'Justin Y. Chen', 'Alexandr Andoni', 'Anders Aamand'] | 2023-06-20 | null | null | null | null | ['density-estimation'] | ['methodology'] | [-6.06724441e-01 -9.33910161e-02 -2.53944755e-01 -3.55681390e-01
-1.44705427e+00 -9.00255620e-01 -1.17961861e-01 3.73491973e-01
-6.77474916e-01 9.87663805e-01 -7.98486054e-01 -6.50370538e-01
-3.68858516e-01 -1.04937494e+00 -1.12201953e+00 -5.72460175e-01
-5.57317257e-01 1.04219103e+00 1.20124593e-01 3.69438142... | [6.69671106338501, 4.591709613800049] |
0d6a4af6-e23c-46dd-89e2-fa14fd414e46 | single-image-hdr-reconstruction-by-multi | 2210.15897 | null | https://arxiv.org/abs/2210.15897v1 | https://arxiv.org/pdf/2210.15897v1.pdf | Single-Image HDR Reconstruction by Multi-Exposure Generation | High dynamic range (HDR) imaging is an indispensable technique in modern photography. Traditional methods focus on HDR reconstruction from multiple images, solving the core problems of image alignment, fusion, and tone mapping, yet having a perfect solution due to ghosting and other visual artifacts in the reconstructi... | ['Binh-Son Hua', 'Rang Nguyen', 'Quynh Le', 'Phuoc-Hieu Le'] | 2022-10-28 | null | null | null | null | ['hdr-reconstruction', 'tone-mapping'] | ['computer-vision', 'computer-vision'] | [ 6.60233855e-01 -8.63396898e-02 1.40883714e-01 -3.63511473e-01
-9.00165856e-01 -2.40368783e-01 4.15356547e-01 -6.01781249e-01
-2.99809724e-01 6.36292696e-01 1.25133500e-01 -1.64244547e-01
2.52896607e-01 -8.32183778e-01 -1.01593494e+00 -8.05817366e-01
4.50249583e-01 -1.34663552e-01 2.95354240e-02 -2.03256890... | [10.851673126220703, -2.2224836349487305] |
12c1a8c3-f445-4419-a385-6a04c90bf43d | a-survey-of-identification-and-mitigation-of | 2210.04491 | null | https://arxiv.org/abs/2210.04491v1 | https://arxiv.org/pdf/2210.04491v1.pdf | A survey of Identification and mitigation of Machine Learning algorithmic biases in Image Analysis | The problem of algorithmic bias in machine learning has gained a lot of attention in recent years due to its concrete and potentially hazardous implications in society. In much the same manner, biases can also alter modern industrial and safety-critical applications where machine learning are based on high dimensional ... | ['Jean-Michel Loubes', 'Lucas Hervier', 'Agustin Picard', 'Laurent Risser'] | 2022-10-10 | null | null | null | null | ['common-sense-reasoning'] | ['reasoning'] | [ 8.28924716e-01 4.53049719e-01 -3.09382200e-01 -3.87161553e-01
-2.93220699e-01 -6.09088898e-01 8.30423057e-01 2.10607126e-01
-6.45720780e-01 1.04466939e+00 3.52657884e-02 -4.65939552e-01
-6.46200657e-01 -9.61709678e-01 -5.77512205e-01 -1.06308877e+00
2.22093031e-01 2.15962902e-01 -3.22616994e-01 -3.24015290... | [8.75290584564209, 5.153059959411621] |
d623fcbf-5244-4f4c-a0f4-fdba24bfd0cf | event-extraction-based-on-open-information | 1907.00692 | null | https://arxiv.org/abs/1907.00692v1 | https://arxiv.org/pdf/1907.00692v1.pdf | Event extraction based on open information extraction and ontology | The work presented in this master thesis consists of extracting a set of events from texts written in natural language. For this purpose, we have based ourselves on the basic notions of the information extraction as well as the open information extraction. First, we applied an open information extraction(OIE) system fo... | ['Sihem Sahnoun'] | 2019-06-24 | null | null | null | null | ['open-information-extraction'] | ['natural-language-processing'] | [ 2.24635825e-01 5.73791981e-01 4.16292787e-01 -2.56668389e-01
-1.97175786e-01 -2.45075092e-01 9.76871729e-01 8.31137002e-01
-7.02935219e-01 8.89162898e-01 7.31287673e-02 -1.40094057e-01
-6.30628407e-01 -1.11920679e+00 -2.66089529e-01 -2.28023395e-01
-1.80503666e-01 5.56606650e-01 6.37735367e-01 -2.59309441... | [9.301643371582031, 8.64735221862793] |
9f7a8be5-3bff-4bea-b395-33d117885626 | an-unsupervised-approach-to-user-simulation | null | null | https://aclanthology.org/W12-1606 | https://aclanthology.org/W12-1606.pdf | An Unsupervised Approach to User Simulation: Toward Self-Improving Dialog Systems | null | ['Sungjin Lee', 'Maxine Eskenazi'] | 2012-07-01 | null | null | null | ws-2012-7 | ['user-simulation'] | ['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... | [-7.340228080749512, 3.730822801589966] |
48546e7b-fa90-499c-b818-2a25487bea6a | modeling-cell-size-distribution-with | 2304.06631 | null | https://arxiv.org/abs/2304.06631v2 | https://arxiv.org/pdf/2304.06631v2.pdf | Modeling Cell Size Distribution with Heterogeneous Flux Balance Analysis | For over two decades, Flux Balance Analysis (FBA) has been successfully used for predicting growth rates and intracellular reaction rates in microbiological metabolism. An aspect that is often omitted from this analysis, is segregation or heterogeneity between different cells. In this work, we propose an extended FBA m... | ['Steffen Waldherr', 'Florence H. Vermeire', 'Michiel Busschaert'] | 2023-04-13 | null | null | null | null | ['culture'] | ['speech'] | [ 2.64103502e-01 -2.20212951e-01 1.25558889e-02 6.13370776e-01
6.51626348e-01 -6.15900218e-01 4.74272668e-01 6.79877281e-01
-3.58620942e-01 1.32878733e+00 -3.39511067e-01 -3.48844320e-01
-2.13743821e-01 -8.56267035e-01 -3.55938166e-01 -1.32575881e+00
-1.14640696e-02 4.02398914e-01 -8.72379765e-02 -3.88442948... | [5.754912853240967, 4.280282497406006] |
545350cd-142a-4d4e-bf72-726f815e6e2f | implementation-of-an-internet-of-things | 2203.12787 | null | https://arxiv.org/abs/2203.12787v2 | https://arxiv.org/pdf/2203.12787v2.pdf | Design of an Internet of Things System for Smart Hospitals | With the fast advancement of smart devices and Internet of Things (IoT) technologies, certain established situations are opening up new avenues of exploration. Particularly in the sphere of healthcare, the diverse and big population, the complicated and professional data, and the stringent environmental requirements fo... | ['Zihuai Lin', 'Xucun Yan', 'Jichao Leng'] | 2022-03-24 | null | null | null | null | ['indoor-localization'] | ['computer-vision'] | [ 6.99614640e-04 -3.08390111e-01 -2.79099029e-02 -3.30198228e-01
-7.96993896e-02 -3.08086365e-01 1.17186807e-01 3.59877139e-01
-3.39185685e-01 1.01094341e+00 9.84101593e-02 -4.72516119e-01
-6.32934332e-01 -1.15272033e+00 3.01731288e-01 -8.12160969e-01
-1.24403484e-01 1.64210007e-01 -3.32319736e-03 9.55075771... | [6.545906066894531, 0.916495144367218] |
3edb2550-939f-4b69-a7fc-467736e16335 | fristograms-revealing-and-exploiting-light | 2107.10563 | null | https://arxiv.org/abs/2107.10563v1 | https://arxiv.org/pdf/2107.10563v1.pdf | Fristograms: Revealing and Exploiting Light Field Internals | In recent years, light field (LF) capture and processing has become an integral part of media production. The richness of information available in LFs has enabled novel applications like post-capture depth-of-field editing, 3D reconstruction, segmentation and matting, saliency detection, object detection and recognitio... | ['Robin Kremer', 'Tobias Lange', 'Kelvin Chelli', 'Thorsten Herfet'] | 2021-07-22 | null | null | null | null | ['image-matting'] | ['computer-vision'] | [ 5.62931597e-01 -3.69084597e-01 3.10326159e-01 -1.41925484e-01
-1.29533783e-01 -4.65084255e-01 6.22643828e-01 5.64619243e-01
-4.24234748e-01 5.86729407e-01 -2.45954946e-01 -1.30138472e-01
-1.64949313e-01 -1.21023273e+00 -6.95910633e-01 -6.86945200e-01
1.31379068e-01 3.74882817e-01 5.10546625e-01 -6.74113929... | [9.670615196228027, -2.661588430404663] |
9b5386ec-5157-4616-bcb6-f764178cbf44 | action-machine-rethinking-action-recognition | 1812.05770 | null | http://arxiv.org/abs/1812.05770v2 | http://arxiv.org/pdf/1812.05770v2.pdf | Action Machine: Rethinking Action Recognition in Trimmed Videos | Existing methods in video action recognition mostly do not distinguish human
body from the environment and easily overfit the scenes and objects. In this
work, we present a conceptually simple, general and high-performance framework
for action recognition in trimmed videos, aiming at person-centric modeling.
The method... | ['Jun-Jie Huang', 'Dalong Du', 'Wei Zou', 'Liang Xu', 'Manyu Chang', 'Guan Huang', 'Yiming Hu', 'Zheng Zhu', 'Jiagang Zhu'] | 2018-12-14 | null | null | null | null | ['multimodal-activity-recognition'] | ['computer-vision'] | [ 7.50279948e-02 3.48573946e-03 -2.86227196e-01 -3.83396775e-01
-8.47346544e-01 -1.61771268e-01 5.15905142e-01 -7.56707370e-01
-4.11215305e-01 4.46197540e-01 6.77671492e-01 3.60797405e-01
4.04617071e-01 -1.97620764e-01 -8.33337307e-01 -6.02912664e-01
-8.24441016e-02 5.92821598e-01 3.08543533e-01 1.11195289... | [7.891770362854004, 0.36835142970085144] |
72dc129d-b56c-487d-bd36-a2d1b78e34d8 | context-sensitive-malicious-spelling-error | 1901.07688 | null | http://arxiv.org/abs/1901.07688v1 | http://arxiv.org/pdf/1901.07688v1.pdf | Context-Sensitive Malicious Spelling Error Correction | Misspelled words of the malicious kind work by changing specific keywords and
are intended to thwart existing automated applications for cyber-environment
control such as harassing content detection on the Internet and email spam
detection. In this paper, we focus on malicious spelling correction, which
requires an app... | ['Suma Bhat', 'Pramod Viswanath', 'Yuchen Li', 'Hongyu Gong'] | 2019-01-23 | null | null | null | null | ['spam-detection'] | ['natural-language-processing'] | [ 6.48470521e-01 -4.87539470e-01 2.86449846e-02 -1.16697364e-02
-4.09779310e-01 -8.03862989e-01 8.70452464e-01 5.59127927e-01
-5.43604314e-01 5.64351916e-01 -1.17090754e-01 -7.67579854e-01
2.96959039e-02 -6.16643608e-01 -5.34954727e-01 -3.35856974e-01
3.48414093e-01 1.96788818e-01 5.93165755e-01 -5.50288379... | [7.694363117218018, 9.85285758972168] |
06396515-3774-40ac-995b-a13a055db7c7 | preserving-commonsense-knowledge-from-pre | 2306.10790 | null | https://arxiv.org/abs/2306.10790v1 | https://arxiv.org/pdf/2306.10790v1.pdf | Preserving Commonsense Knowledge from Pre-trained Language Models via Causal Inference | Fine-tuning has been proven to be a simple and effective technique to transfer the learned knowledge of Pre-trained Language Models (PLMs) to downstream tasks. However, vanilla fine-tuning easily overfits the target data and degrades the generalization ability. Most existing studies attribute it to catastrophic forgett... | ['Haibin Chen', 'Xichen Shang', 'Huawen Feng', 'Junlong Liu', 'Peitian Ma', 'Yue Wu', 'Shengjie Qiu', 'Qianli Ma', 'Junhao Zheng'] | 2023-06-19 | null | null | null | null | ['causal-inference', 'causal-inference'] | ['knowledge-base', 'miscellaneous'] | [ 6.39940798e-02 2.02745453e-01 -2.22774237e-01 -2.94390500e-01
-4.75899696e-01 -6.85712039e-01 6.17539048e-01 -1.00982273e-02
-3.52679342e-01 9.54339802e-01 5.76225877e-01 -2.18351245e-01
-1.61918759e-01 -1.08939970e+00 -1.09277534e+00 -6.13438666e-01
3.00733358e-01 5.06258428e-01 2.25706622e-01 -6.42563283... | [10.478271484375, 8.061951637268066] |
8248c89c-7e52-4710-b31b-7149006016e3 | joint-optimization-of-masks-and-deep | 1502.04149 | null | http://arxiv.org/abs/1502.04149v4 | http://arxiv.org/pdf/1502.04149v4.pdf | Joint Optimization of Masks and Deep Recurrent Neural Networks for Monaural Source Separation | Monaural source separation is important for many real world applications. It
is challenging because, with only a single channel of information available,
without any constraints, an infinite number of solutions are possible. In this
paper, we explore joint optimization of masking functions and deep recurrent
neural net... | ['Mark Hasegawa-Johnson', 'Po-Sen Huang', 'Paris Smaragdis', 'Minje Kim'] | 2015-02-13 | null | null | null | null | ['speech-denoising'] | ['speech'] | [ 1.55394018e-01 -3.52608979e-01 3.43574643e-01 -5.47750629e-02
-1.33804667e+00 -5.16739070e-01 1.60295591e-01 -2.46312767e-01
-2.44761840e-01 4.67516512e-01 4.11163270e-01 -3.20349067e-01
2.96116639e-02 -5.01033701e-02 -4.86251503e-01 -9.78513956e-01
3.09229881e-01 -1.58182532e-01 -1.27579048e-01 -2.96922326... | [15.000879287719727, 5.865081787109375] |
46b189c1-9d4a-4b75-8ce8-e10fc1ef35bb | tgdm-target-guided-dynamic-mixup-for-cross | 2210.05392 | null | https://arxiv.org/abs/2210.05392v2 | https://arxiv.org/pdf/2210.05392v2.pdf | TGDM: Target Guided Dynamic Mixup for Cross-Domain Few-Shot Learning | Given sufficient training data on the source domain, cross-domain few-shot learning (CD-FSL) aims at recognizing new classes with a small number of labeled examples on the target domain. The key to addressing CD-FSL is to narrow the domain gap and transferring knowledge of a network trained on the source domain to the ... | ['Yu-Gang Jiang', 'Yixin Cao', 'Jingjing Chen', 'Yuqian Fu', 'Linhai Zhuo'] | 2022-10-11 | null | null | null | null | ['cross-domain-few-shot', 'cross-domain-few-shot-learning'] | ['computer-vision', 'computer-vision'] | [ 5.18494010e-01 1.48463309e-01 -4.76177245e-01 -2.71497101e-01
-8.83165061e-01 -2.92675674e-01 5.27571142e-01 -4.06049579e-01
-3.81927639e-02 6.11348271e-01 -6.13814443e-02 2.61457134e-02
1.35257438e-01 -1.08832598e+00 -6.21041000e-01 -6.68144643e-01
5.03376484e-01 5.56601048e-01 6.41340554e-01 -2.37889305... | [10.263811111450195, 2.9945411682128906] |
ee491200-d50b-481f-9fc5-1cc7642f6f15 | fc2rn-a-fully-convolutional-corner-refinement | 2007.05113 | null | https://arxiv.org/abs/2007.05113v1 | https://arxiv.org/pdf/2007.05113v1.pdf | FC2RN: A Fully Convolutional Corner Refinement Network for Accurate Multi-Oriented Scene Text Detection | Recent scene text detection works mainly focus on curve text detection. However, in real applications, the curve texts are more scarce than the multi-oriented ones. Accurate detection of multi-oriented text with large variations of scales, orientations, and aspect ratios is of great significance. Among the multi-orient... | ['Yinliang Yue', 'Xugong Qin', 'Yu Zhou', 'Dayan Wu', 'Weiping Wang'] | 2020-07-10 | null | null | null | null | ['multi-oriented-scene-text-detection', 'scene-text-detection'] | ['computer-vision', 'computer-vision'] | [-1.55119091e-01 -5.22194922e-01 1.92929551e-01 -5.29605616e-03
-8.77147257e-01 -4.05623525e-01 5.09344399e-01 3.72027010e-01
-3.95687819e-01 -5.80839477e-02 2.29964301e-01 -8.55166689e-02
3.54398370e-01 -6.85416877e-01 -5.20045638e-01 -6.81991339e-01
5.20869792e-01 6.26480877e-01 8.18209529e-01 -1.43853575... | [12.08687686920166, 2.291288375854492] |
920aa896-3e12-4f2a-a770-bf2bcf7d9479 | is-artificial-data-useful-for-biomedical | 1907.01055 | null | https://arxiv.org/abs/1907.01055v2 | https://arxiv.org/pdf/1907.01055v2.pdf | Is artificial data useful for biomedical Natural Language Processing algorithms? | A major obstacle to the development of Natural Language Processing (NLP) methods in the biomedical domain is data accessibility. This problem can be addressed by generating medical data artificially. Most previous studies have focused on the generation of short clinical text, and evaluation of the data utility has been... | ['Lucia Specia', 'Julia Ive', 'Zixu Wang', 'Sumithra Velupillai'] | 2019-07-01 | is-artificial-data-useful-for-biomedical-1 | https://aclanthology.org/W19-5026 | https://aclanthology.org/W19-5026.pdf | ws-2019-8 | ['temporal-relation-extraction'] | ['natural-language-processing'] | [ 7.50730276e-01 7.91948974e-01 -1.57058388e-01 -4.95978236e-01
-8.90634120e-01 -2.94872165e-01 5.25361598e-01 8.94607008e-01
-7.81940222e-01 1.23683178e+00 3.96526128e-01 -6.07472539e-01
-2.27241874e-01 -6.92853987e-01 -3.98002356e-01 -4.82444048e-01
-1.58828378e-01 8.64729106e-01 -1.87498495e-01 -1.35539681... | [8.508461952209473, 8.709741592407227] |
cdd5c42e-a538-4e2e-b65e-7c3dae3636a3 | zero-shot-aspect-based-scientific-document-1 | null | null | https://aclanthology.org/2022.bionlp-1.5 | https://aclanthology.org/2022.bionlp-1.5.pdf | Zero-Shot Aspect-Based Scientific Document Summarization using Self-Supervised Pre-training | We study the zero-shot setting for the aspect-based scientific document summarization task. Summarizing scientific documents with respect to an aspect can remarkably improve document assistance systems and readers experience. However, existing large-scale datasets contain a limited variety of aspects, causing summariza... | ['Salah Ait Mokhtar', 'Benoit Favre', 'Vassilina Nikoulina', 'Amir Soleimani'] | null | null | null | null | bionlp-acl-2022-5 | ['scientific-article-summarization'] | ['natural-language-processing'] | [ 5.41358650e-01 5.31193078e-01 -7.02492893e-01 -3.49916816e-01
-1.48283529e+00 -4.23757672e-01 6.08061671e-01 7.48599112e-01
-3.31686497e-01 1.04064727e+00 9.53733623e-01 1.37723060e-02
-1.24912836e-01 -4.56855863e-01 -7.60057986e-01 -4.33914840e-01
2.94192195e-01 8.08297575e-01 2.31868565e-01 -4.23605777... | [12.353708267211914, 9.488912582397461] |
b052dc3a-41a2-4a5d-951b-14b7c1be358c | manifold-contrastive-learning-with | 2306.13544 | null | https://arxiv.org/abs/2306.13544v1 | https://arxiv.org/pdf/2306.13544v1.pdf | Manifold Contrastive Learning with Variational Lie Group Operators | Self-supervised learning of deep neural networks has become a prevalent paradigm for learning representations that transfer to a variety of downstream tasks. Similar to proposed models of the ventral stream of biological vision, it is observed that these networks lead to a separation of category manifolds in the repres... | ['Christopher J. Rozell', 'Kyle A. Johnsen', 'Alec Helbling', 'Kion Fallah'] | 2023-06-23 | null | null | null | null | ['contrastive-learning', 'self-supervised-learning', 'contrastive-learning'] | ['computer-vision', 'computer-vision', 'methodology'] | [ 3.49590242e-01 3.06822240e-01 -4.07642543e-01 -4.09846663e-01
-3.96997094e-01 -6.38848186e-01 1.10175383e+00 2.05106921e-02
-1.88331187e-01 4.21651423e-01 2.70592928e-01 -3.30702849e-02
-1.85240239e-01 -7.05061197e-01 -9.93339181e-01 -8.14972818e-01
-6.83416575e-02 3.26465786e-01 -3.00279349e-01 -2.48300165... | [9.02013111114502, 2.8636019229888916] |
9586c75c-f899-4510-8918-075f9542fb5b | mask-detection-and-classification-in-thermal | 2304.02931 | null | https://arxiv.org/abs/2304.02931v1 | https://arxiv.org/pdf/2304.02931v1.pdf | Mask Detection and Classification in Thermal Face Images | Face masks are recommended to reduce the transmission of many viruses, especially SARS-CoV-2. Therefore, the automatic detection of whether there is a mask on the face, what type of mask is worn, and how it is worn is an important research topic. In this work, the use of thermal imaging was considered to analyze the po... | ['Jacek Rumiński', 'Natalia Kowalczyk'] | 2023-04-06 | null | null | null | null | ['type'] | ['speech'] | [ 2.25258067e-01 1.07194647e-01 4.06336397e-01 -2.33675048e-01
-6.97040334e-02 -4.50722098e-01 5.25999308e-01 -1.52527571e-01
-3.76462787e-01 3.52603614e-01 -4.33664352e-01 -7.88794830e-02
2.67363131e-01 -5.87890744e-01 -6.72648847e-01 -1.06422496e+00
5.50321117e-03 5.80971658e-01 9.22465138e-03 -6.09695241... | [13.20423412322998, 0.8564512133598328] |
ddddcfab-67aa-464c-8186-635f2429b6f9 | decontamination-of-the-scientific-literature | 2210.15912 | null | https://arxiv.org/abs/2210.15912v1 | https://arxiv.org/pdf/2210.15912v1.pdf | Decontamination of the scientific literature | Research misconduct and frauds pollute the scientific literature. Honest errors and malevolent data fabrication, image manipulation, journal hijacking, and plagiarism passed peer review unnoticed. Problematic papers deceive readers, authors citing them, and AI-powered literature-based discovery. Flagship publishers acc... | ['Guillaume Cabanac'] | 2022-10-28 | null | null | null | null | ['image-manipulation'] | ['computer-vision'] | [-7.24442899e-02 1.80908442e-01 -2.21548840e-01 3.09210628e-01
-3.21690559e-01 -1.04786813e+00 7.07068503e-01 1.31979048e-01
-5.64645112e-01 1.27760303e+00 -5.18463366e-02 -1.16520166e+00
1.48191765e-01 -4.13057476e-01 -1.25232375e+00 -2.11324796e-01
5.50761819e-01 -1.16506122e-01 -4.91907805e-01 5.33079743... | [8.965596199035645, 6.542915344238281] |
16e8f1f8-3078-4bcf-92a6-b4fcb76bba5f | seamless-multimodal-biometrics-for-continuous | 2301.03045 | null | https://arxiv.org/abs/2301.03045v2 | https://arxiv.org/pdf/2301.03045v2.pdf | Seamless Multimodal Biometrics for Continuous Personalised Wellbeing Monitoring | Artificially intelligent perception is increasingly present in the lives of every one of us. Vehicles are no exception, (...) In the near future, pattern recognition will have an even stronger role in vehicles, as self-driving cars will require automated ways to understand what is happening around (and within) them and... | ['João Ribeiro Pinto'] | 2023-01-08 | null | null | null | null | ['multimodal-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'speech'] | [ 2.80756444e-01 1.50686130e-02 1.61903396e-01 -7.39091158e-01
-6.27977431e-01 -4.50683296e-01 1.90084323e-01 -1.84147552e-01
-3.87979418e-01 5.77813923e-01 -1.43445924e-01 -3.26206237e-01
-1.63857266e-01 -5.55117965e-01 -4.20041293e-01 -7.85803080e-01
9.41229388e-02 -1.63444832e-01 -6.86285615e-01 -2.21944004... | [13.277771949768066, 1.1472382545471191] |
60298304-e728-4e3a-af6c-0832039ba3cb | the-winnability-of-klondike-and-many-other | 1906.12314 | null | https://arxiv.org/abs/1906.12314v4 | https://arxiv.org/pdf/1906.12314v4.pdf | The Winnability of Klondike Solitaire and Many Other Patience Games | Our ignorance of the winnability percentage of the game in the Windows Solitaire program, more properly called 'Klondike', has been described as "one of the embarrassments of applied mathematics". Klondike is just one of many single-player card games, generically called 'patience' or 'solitaire' games, for which player... | ['Ian P. Gent', 'Charlie Blake'] | 2019-06-28 | null | null | null | null | ['klondike', 'solitaire', 'card-games'] | ['playing-games', 'playing-games', 'playing-games'] | [-4.53218728e-01 1.38198689e-01 4.85238396e-02 4.82142493e-02
-6.16272449e-01 -8.60978305e-01 1.38082623e-01 4.65025008e-02
-7.53353179e-01 1.10032034e+00 -4.63773429e-01 -9.99266207e-01
-6.50811791e-01 -1.07808888e+00 -7.31322706e-01 -5.50114810e-01
-7.75154978e-02 7.37986088e-01 4.48305815e-01 -4.37313318... | [3.4973032474517822, 1.5352567434310913] |
641beedd-d9d8-410e-bb89-1a6acb6867c7 | specificity-preserving-rgb-d-saliency | 2108.08162 | null | https://arxiv.org/abs/2108.08162v2 | https://arxiv.org/pdf/2108.08162v2.pdf | Specificity-preserving RGB-D Saliency Detection | Salient object detection (SOD) on RGB and depth images has attracted more and more research interests, due to its effectiveness and the fact that depth cues can now be conveniently captured. Existing RGB-D SOD models usually adopt different fusion strategies to learn a shared representation from the two modalities (\ie... | ['Deng-Ping Fan', 'Yi Zhou', 'Geng Chen', 'Huazhu Fu', 'Tao Zhou'] | 2021-08-18 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Zhou_Specificity-Preserving_RGB-D_Saliency_Detection_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Zhou_Specificity-Preserving_RGB-D_Saliency_Detection_ICCV_2021_paper.pdf | iccv-2021-1 | ['thermal-image-segmentation'] | ['computer-vision'] | [ 3.32252860e-01 -1.88496336e-01 -4.17201698e-01 -4.49395180e-01
-8.19102764e-01 -1.44279182e-01 3.46647173e-01 -2.72356778e-01
-2.13769406e-01 5.73608220e-01 3.48751485e-01 1.56772912e-01
3.06047890e-02 -6.60961926e-01 -7.40911841e-01 -7.67930031e-01
4.55295682e-01 -5.00505328e-01 6.71423316e-01 -2.78894544... | [9.65461540222168, -0.8142433166503906] |
0ad67646-e791-4a41-93a1-049fffcc65d7 | adversarial-deep-structured-nets-for-mass | 1710.09288 | null | http://arxiv.org/abs/1710.09288v2 | http://arxiv.org/pdf/1710.09288v2.pdf | Adversarial Deep Structured Nets for Mass Segmentation from Mammograms | Mass segmentation provides effective morphological features which are
important for mass diagnosis. In this work, we propose a novel end-to-end
network for mammographic mass segmentation which employs a fully convolutional
network (FCN) to model a potential function, followed by a CRF to perform
structured learning. Be... | ['Trac. D. Tran', 'Xiang Xiang', 'Wentao Zhu', 'Xiaohui Xie', 'Gregory D. Hager'] | 2017-10-24 | null | null | null | null | ['mass-segmentation-from-mammograms'] | ['medical'] | [ 1.95526436e-01 5.76847017e-01 -3.00590754e-01 -7.25187659e-01
-8.97567272e-01 3.78179811e-02 1.90974519e-01 2.37577432e-03
-4.53803927e-01 5.73799491e-01 9.28556100e-02 -5.91162205e-01
3.72856796e-01 -1.02089405e+00 -8.90034616e-01 -7.14092016e-01
-1.49147943e-01 5.40320992e-01 4.85575765e-01 -4.43959236... | [14.845076560974121, -2.4297332763671875] |
4d5a2001-1554-452f-b6d9-db57cdb95153 | network-giant-fully-distributed-newton-type | 2305.07898 | null | https://arxiv.org/abs/2305.07898v1 | https://arxiv.org/pdf/2305.07898v1.pdf | Network-GIANT: Fully distributed Newton-type optimization via harmonic Hessian consensus | This paper considers the problem of distributed multi-agent learning, where the global aim is to minimize a sum of local objective (empirical loss) functions through local optimization and information exchange between neighbouring nodes. We introduce a Newton-type fully distributed optimization algorithm, Network-GIANT... | ['Subhrakanti Dey', 'Luca Schenato', 'Ganesh Sharma', 'Alessio Maritan'] | 2023-05-13 | null | null | null | null | ['distributed-optimization', 'type'] | ['methodology', 'speech'] | [-6.49768054e-01 7.45093375e-02 4.01629768e-02 -3.13558549e-01
-1.26629484e+00 -4.35752153e-01 2.52305299e-01 3.42152089e-01
-7.26850927e-01 1.10879922e+00 -5.11660948e-02 9.93471071e-02
-6.44903660e-01 -6.56140387e-01 -8.46599817e-01 -1.14645123e+00
-7.56354332e-01 1.03226793e+00 -7.30784163e-02 -1.04408152... | [6.197488784790039, 5.059884548187256] |
f52cfd10-d71a-4ca4-9806-c23b85e678a6 | accuracy-of-segment-anything-model-sam-in | 2304.09324 | null | https://arxiv.org/abs/2304.09324v3 | https://arxiv.org/pdf/2304.09324v3.pdf | Computer-Vision Benchmark Segment-Anything Model (SAM) in Medical Images: Accuracy in 12 Datasets | Background: The segment-anything model (SAM), introduced in April 2023, shows promise as a benchmark model and a universal solution to segment various natural images. It comes without previously-required re-training or fine-tuning specific to each new dataset. Purpose: To test SAM's accuracy in various medical image se... | ['Yangming Ou', 'Atle Bjornerud', 'Jeffrey Stout', 'P. Ellen Grant', 'Jingpeng Li', 'Rina Bao', 'Sheng He'] | 2023-04-18 | null | null | null | null | ['zero-shot-segmentation'] | ['computer-vision'] | [ 4.02083665e-01 1.80073544e-01 -3.21221977e-01 -4.96701062e-01
-9.43898022e-01 -7.01488614e-01 2.09884942e-01 2.76116759e-01
-7.98348963e-01 3.59824687e-01 -1.64066404e-02 -3.55605841e-01
-1.83752745e-01 -5.21453202e-01 -3.17326546e-01 -5.76983154e-01
-2.32289173e-02 5.56823075e-01 4.30941075e-01 2.12745532... | [14.535049438476562, -2.401785373687744] |
6554f4b7-52fb-4f02-b731-b035708005e0 | specular-to-diffuse-translation-for-multi | 1807.05439 | null | http://arxiv.org/abs/1807.05439v3 | http://arxiv.org/pdf/1807.05439v3.pdf | Specular-to-Diffuse Translation for Multi-View Reconstruction | Most multi-view 3D reconstruction algorithms, especially when
shape-from-shading cues are used, assume that object appearance is
predominantly diffuse. To alleviate this restriction, we introduce S2Dnet, a
generative adversarial network for transferring multiple views of objects with
specular reflection into diffuse on... | ['Danny Cohen-Or', 'Shihao Wu', 'Hui Huang', 'Matthias Zwicker', 'Matan Sela', 'Tiziano Portenier', 'Ron Kimmel'] | 2018-07-14 | specular-to-diffuse-translation-for-multi-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Shihao_Wu_Specular-to-Diffuse_Translation_for_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Shihao_Wu_Specular-to-Diffuse_Translation_for_ECCV_2018_paper.pdf | eccv-2018-9 | ['lighting-estimation'] | ['computer-vision'] | [ 3.51193547e-01 -1.96885273e-01 3.17184448e-01 -5.28206110e-01
-7.25874126e-01 -7.95145035e-01 5.65639615e-01 -9.20549810e-01
1.14945605e-01 4.83470201e-01 1.24019325e-01 3.48623618e-02
4.16081697e-01 -9.95517135e-01 -9.91055548e-01 -8.97656798e-01
6.80575252e-01 2.40507200e-01 2.00976923e-01 -2.06908360... | [9.323426246643066, -3.057081699371338] |
1f239abe-2a8d-4155-be54-31c5096b09f9 | self-attention-presents-low-dimensional | 2112.10644 | null | https://arxiv.org/abs/2112.10644v3 | https://arxiv.org/pdf/2112.10644v3.pdf | Self-attention Presents Low-dimensional Knowledge Graph Embeddings for Link Prediction | A few models have tried to tackle the link prediction problem, also known as knowledge graph completion, by embedding knowledge graphs in comparably lower dimensions. However, the state-of-the-art results are attained at the cost of considerably increasing the dimensionality of embeddings which causes scalability issue... | ['Hadi Moradi', 'Reshad Hosseini', 'Peyman Baghershahi'] | 2021-12-20 | null | null | null | null | ['knowledge-graph-embeddings', 'knowledge-graph-embeddings'] | ['graphs', 'methodology'] | [-3.01489770e-01 5.30638158e-01 -7.11026609e-01 -3.18507105e-02
-4.78446960e-01 -3.33594650e-01 6.37933969e-01 1.87656686e-01
-3.89792889e-01 6.64719701e-01 4.96184438e-01 -4.20347214e-01
-5.49592376e-01 -1.03602612e+00 -7.61099160e-01 -2.05059722e-01
-3.79684389e-01 8.85934353e-01 2.15257540e-01 -3.54439646... | [8.769235610961914, 7.883674144744873] |
88522dcb-d1a9-4acd-af6d-0d44c9b77dc4 | faceqgen-semi-supervised-deep-learning-for | 2201.00770 | null | https://arxiv.org/abs/2201.00770v1 | https://arxiv.org/pdf/2201.00770v1.pdf | FaceQgen: Semi-Supervised Deep Learning for Face Image Quality Assessment | In this paper we develop FaceQgen, a No-Reference Quality Assessment approach for face images based on a Generative Adversarial Network that generates a scalar quality measure related with the face recognition accuracy. FaceQgen does not require labelled quality measures for training. It is trained from scratch using t... | ['Aythami Morales', 'Ignacio Serna', 'Julian Fierrez', 'Javier Hernandez-Ortega'] | 2022-01-03 | null | null | null | null | ['face-image-quality', 'face-image-quality-assessment'] | ['computer-vision', 'computer-vision'] | [ 2.38767475e-01 1.69490799e-01 1.00730553e-01 -5.37360787e-01
-9.06226695e-01 -3.49889308e-01 6.23157442e-01 -4.17628169e-01
-6.11243304e-04 7.07223177e-01 1.93941407e-02 1.43607616e-01
-3.60745639e-01 -9.10378456e-01 -6.22950971e-01 -9.84183073e-01
-4.28344160e-02 4.93728071e-01 -3.09054494e-01 -1.70593739... | [13.008182525634766, 0.7440258264541626] |
84f91aae-a478-4215-9f76-df1287f13929 | trailers12k-evaluating-transfer-learning-for | 2210.07983 | null | https://arxiv.org/abs/2210.07983v4 | https://arxiv.org/pdf/2210.07983v4.pdf | Improving Transfer Learning with a Dual Image and Video Transformer for Multi-label Movie Trailer Genre Classification | In this paper, we study the transferability of ImageNet spatial and Kinetics spatio-temporal representations to multi-label Movie Trailer Genre Classification (MTGC). In particular, we present an extensive evaluation of the transferability of ConvNet and Transformer models pretrained on ImageNet and Kinetics to Trailer... | ['Gibran Fuentes-Pineda', 'Berenice Montalvo-Lezama', 'Ricardo Montalvo-Lezama'] | 2022-10-14 | null | null | null | null | ['genre-classification'] | ['computer-vision'] | [ 6.48892671e-02 -4.67199266e-01 -1.93432391e-01 -1.58966273e-01
-6.13852680e-01 -8.78432751e-01 4.75735754e-01 -1.56789497e-02
-7.65135229e-01 3.92623484e-01 1.00463055e-01 -4.28279154e-02
-2.50954945e-02 -5.61911523e-01 -9.75590765e-01 -6.32591903e-01
-2.51844496e-01 7.12160394e-02 4.86657351e-01 -2.55387396... | [9.299049377441406, 0.784055769443512] |
4a740db7-dbe7-4b6f-8620-46df0d6fbb9b | video-object-segmentation-using-supervoxel | 1704.05165 | null | http://arxiv.org/abs/1704.05165v1 | http://arxiv.org/pdf/1704.05165v1.pdf | Video Object Segmentation using Supervoxel-Based Gerrymandering | Pixels operate locally. Superpixels have some potential to collect
information across many pixels; supervoxels have more potential by implicitly
operating across time. In this paper, we explore this well established notion
thoroughly analyzing how supervoxels can be used in place of and in conjunction
with other means ... | ['Jason J. Corso', 'Brent A. Griffin'] | 2017-04-18 | null | null | null | null | ['unsupervised-video-object-segmentation'] | ['computer-vision'] | [ 5.57291508e-01 -7.29857385e-02 -2.83805102e-01 -4.13366616e-01
-5.52722991e-01 -9.70958471e-01 8.99530053e-01 2.63072371e-01
-4.93206203e-01 3.63993287e-01 2.17799678e-01 -3.57773483e-01
-3.60638618e-01 -4.66080725e-01 -5.16088843e-01 -9.29693580e-01
-2.57417355e-02 2.10944206e-01 8.13889623e-01 -3.21293138... | [9.080535888671875, -0.33820340037345886] |
1e709c36-68e9-449f-8597-82e675a4d5e7 | deep-multi-branch-cnn-architecture-for-early | 2210.12331 | null | https://arxiv.org/abs/2210.12331v3 | https://arxiv.org/pdf/2210.12331v3.pdf | Deep Multi-Branch CNN Architecture for Early Alzheimer's Detection from Brain MRIs | Alzheimer's disease (AD) is a neuro-degenerative disease that can cause dementia and result severe reduction in brain function inhibiting simple tasks especially if no preventative care is taken. Over 1 in 9 Americans suffer from AD induced dementia and unpaid care for people with AD related dementia is valued at $271.... | ['Rakesh Mahto', 'Paul K. Mandal'] | 2022-10-22 | null | null | null | null | ['alzheimer-s-disease-detection'] | ['medical'] | [-6.40599579e-02 4.71615121e-02 1.13227956e-01 -6.71157837e-01
-3.62659991e-01 -1.76509336e-01 2.83283025e-01 8.08912795e-03
-9.05505896e-01 1.23977208e+00 3.41085672e-01 -4.96822655e-01
1.13860153e-01 -8.16018283e-01 -3.48815918e-01 -2.78478891e-01
-5.06953835e-01 6.35334194e-01 4.70020533e-01 1.22263260... | [14.164098739624023, -1.7420815229415894] |
f7b81179-843d-493a-b0ce-945422323d01 | playing-atari-games-with-deep-reinforcement | 1607.05077 | null | http://arxiv.org/abs/1607.05077v1 | http://arxiv.org/pdf/1607.05077v1.pdf | Playing Atari Games with Deep Reinforcement Learning and Human Checkpoint Replay | This paper introduces a novel method for learning how to play the most
difficult Atari 2600 games from the Arcade Learning Environment using deep
reinforcement learning. The proposed method, human checkpoint replay, consists
in using checkpoints sampled from human gameplay as starting points for the
learning process. T... | ['Ionel-Alexandru Hosu', 'Traian Rebedea'] | 2016-07-18 | null | null | null | null | ['montezumas-revenge'] | ['playing-games'] | [-3.47729176e-01 1.97766498e-01 -2.90552080e-02 2.07262591e-01
-7.79217184e-01 -5.62832773e-01 7.59357035e-01 -2.17055887e-01
-9.68101740e-01 9.95907605e-01 -1.53056279e-01 -3.67897660e-01
9.40841213e-02 -8.38755369e-01 -7.97934175e-01 -7.84823239e-01
-4.00718361e-01 6.55598342e-01 3.29513550e-01 -5.92684031... | [3.737663984298706, 1.533073902130127] |
1a55376f-3a7a-4d13-85bc-f9b3fc04aebc | augmented-dual-contrastive-aggregation | null | null | https://dl.acm.org/doi/abs/10.1145/3503161.3548198 | https://dl.acm.org/doi/abs/10.1145/3503161.3548198 | Augmented Dual-Contrastive Aggregation Learning for Unsupervised Visible-Infrared Person Re-Identification | Visible infrared person re-identification (VI-ReID) aims at searching out the corresponding infrared (visible) images from a gallery set captured by other spectrum cameras. Recent works mainly focus on supervised VI-ReID methods that require plenty of cross-modality (visible-infrared) identity labels which are more exp... | ['Zesen Wu', 'Jun Chen', 'Mang Ye', 'Bin Yang'] | 2022-10-14 | null | null | null | acm-mm-2022-10 | ['person-re-identification'] | ['computer-vision'] | [ 3.72130990e-01 -5.98258376e-01 -2.45982826e-01 -3.47369254e-01
-9.80098009e-01 -6.30517125e-01 6.85912967e-01 -9.44703445e-03
-5.36742210e-01 4.46804106e-01 1.82583988e-01 1.92725420e-01
-3.29835534e-01 -5.31964540e-01 -4.65665698e-01 -9.60037231e-01
1.80484384e-01 4.07467186e-01 -5.00361681e-01 9.83461961... | [14.727742195129395, 0.9515729546546936] |
207f9989-0ce3-41fd-b5bd-dfb0c1c62e7d | fast-passage-re-ranking-with-contextualized | 2108.08513 | null | https://arxiv.org/abs/2108.08513v2 | https://arxiv.org/pdf/2108.08513v2.pdf | Fast Passage Re-ranking with Contextualized Exact Term Matching and Efficient Passage Expansion | BERT-based information retrieval models are expensive, in both time (query latency) and computational resources (energy, hardware cost), making many of these models impractical especially under resource constraints. The reliance on a query encoder that only performs tokenization and on the pre-processing of passage rep... | ['Guido Zuccon', 'Shengyao Zhuang'] | 2021-08-19 | null | null | null | null | ['passage-re-ranking'] | ['natural-language-processing'] | [ 2.36783903e-02 -4.03998971e-01 -3.63102406e-01 1.93947032e-01
-1.22969997e+00 -4.96582747e-01 6.26976848e-01 8.49479079e-01
-1.01010633e+00 4.40886080e-01 1.21500745e-01 -3.15477401e-01
-4.30648774e-01 -1.13175678e+00 -4.36609834e-01 -1.03378467e-01
-1.94404215e-01 9.09096479e-01 9.79329050e-01 -4.43063587... | [11.442876815795898, 7.565459728240967] |
6f5c2264-2323-48ed-bf11-15f8a17774af | unsupervised-few-shot-learning-via-deep | 2210.03595 | null | https://arxiv.org/abs/2210.03595v1 | https://arxiv.org/pdf/2210.03595v1.pdf | Unsupervised Few-shot Learning via Deep Laplacian Eigenmaps | Learning a new task from a handful of examples remains an open challenge in machine learning. Despite the recent progress in few-shot learning, most methods rely on supervised pretraining or meta-learning on labeled meta-training data and cannot be applied to the case where the pretraining data is unlabeled. In this st... | ['Chi-Guhn Lee', 'Kuilin Chen'] | 2022-10-07 | null | null | null | null | ['unsupervised-few-shot-learning', 'unsupervised-few-shot-image-classification'] | ['computer-vision', 'computer-vision'] | [ 3.90896618e-01 3.88995647e-01 -4.87502754e-01 -6.16139591e-01
-8.38130891e-01 -1.52584314e-01 7.03617513e-01 2.78847992e-01
-4.44661885e-01 6.47486210e-01 2.73173273e-01 1.74357250e-01
-1.27480403e-01 -9.15368080e-01 -4.80907738e-01 -5.88626802e-01
-1.15141543e-02 8.24002087e-01 3.82159173e-01 -1.31327271... | [9.960832595825195, 3.0390193462371826] |
7574ec54-f3a3-412b-9b32-06832db8109f | state-representation-learning-using-an | 2305.10267 | null | https://arxiv.org/abs/2305.10267v1 | https://arxiv.org/pdf/2305.10267v1.pdf | State Representation Learning Using an Unbalanced Atlas | The manifold hypothesis posits that high-dimensional data often lies on a lower-dimensional manifold and that utilizing this manifold as the target space yields more efficient representations. While numerous traditional manifold-based techniques exist for dimensionality reduction, their application in self-supervised l... | ['Paal Engelstad', 'Anis Yazidi', 'Morten Goodwin', 'Li Meng'] | 2023-05-17 | null | null | null | null | ['dimensionality-reduction'] | ['methodology'] | [-4.94420109e-03 8.29975381e-02 -1.87919959e-01 -1.49042696e-01
-7.77123213e-01 -4.36349660e-01 7.28648007e-01 -1.26330450e-01
-3.22896808e-01 4.52890009e-01 2.06494898e-01 -2.05717534e-01
-4.09069479e-01 -6.34583175e-01 -6.83401346e-01 -6.64312065e-01
-5.34512877e-01 5.14438093e-01 -3.06686223e-01 -2.69729674... | [9.18376636505127, 3.0624663829803467] |
9af32409-b39c-4a78-88ce-3d204e4b850f | experiencing-the-communication-advantage-of | null | null | https://ieeexplore.ieee.org/document/9593171 | https://spawc2021.myquadra.it/wp-content/paper/1570721633-1.pdf | Experiencing the communication advantage of the Indefinite Causal Orders | Many recent studies deal with the Superposition of Causal Orders, a quantum operation with promising advantages in both communication or computing. To experience the advantages, there are several way of implementing it. In literature, most of the set-ups are photonic-based. Instead, our interest is witnessing the Super... | ['Daniele Cuomo; Marcello Caleffi; Angela Sara Cacciapuoti'] | 2021-11-19 | null | null | null | ieee-spawc-2021-11 | ['noise-estimation'] | ['medical'] | [ 4.01327103e-01 -2.33438984e-02 -1.44686148e-01 -1.13860182e-01
3.98069769e-01 -4.39359874e-01 6.22871935e-01 -6.61343694e-01
6.42608404e-02 9.91513908e-01 3.01508307e-02 -2.60636091e-01
-3.13887149e-01 -1.19052446e+00 -4.22858119e-01 -9.67692852e-01
-3.14137995e-01 -9.69337150e-02 6.27014399e-01 -5.05627751... | [5.605099678039551, 4.901496410369873] |
50e465a8-fc4e-406b-867c-c78dc9745b97 | a-gaussian-scale-space-approach-for-exudates | 1505.00737 | null | http://arxiv.org/abs/1505.00737v1 | http://arxiv.org/pdf/1505.00737v1.pdf | A Gaussian Scale Space Approach For Exudates Detection, Classification And Severity Prediction | In the context of Computer Aided Diagnosis system for diabetic retinopathy,
we present a novel method for detection of exudates and their classification
for disease severity prediction. The method is based on Gaussian scale space
based interest map and mathematical morphology. It makes use of support vector
machine for... | ['Samarendra Dandapat', 'Rohit Sinha', 'Mrinal Haloi'] | 2015-05-04 | null | null | null | null | ['severity-prediction'] | ['computer-vision'] | [-2.25742534e-01 -1.81466877e-01 3.44042063e-01 -1.71937346e-01
-2.83065915e-01 -2.79609889e-01 2.59984005e-02 3.60553145e-01
-5.28231442e-01 7.38630056e-01 3.20150346e-01 -6.02011919e-01
-4.34780568e-01 -6.55366063e-01 1.54167876e-01 -7.61118233e-01
-1.56282812e-01 2.64626712e-01 6.82472944e-01 2.03368932... | [15.83108139038086, -4.0093865394592285] |
7808b8f5-7aee-46cd-a35c-b35eb33f771a | physics-based-shadow-image-decomposition-for | 2012.13018 | null | https://arxiv.org/abs/2012.13018v2 | https://arxiv.org/pdf/2012.13018v2.pdf | Physics-based Shadow Image Decomposition for Shadow Removal | We propose a novel deep learning method for shadow removal. Inspired by physical models of shadow formation, we use a linear illumination transformation to model the shadow effects in the image that allows the shadow image to be expressed as a combination of the shadow-free image, the shadow parameters, and a matte lay... | ['Dimitris Samaras', 'Hieu Le'] | 2020-12-23 | null | null | null | null | ['shadow-removal'] | ['computer-vision'] | [ 6.70461893e-01 5.86396568e-02 5.32564104e-01 -2.57281840e-01
-4.00042534e-01 -1.51493773e-01 3.49234700e-01 -7.73621678e-01
-1.47130221e-01 8.06379914e-01 6.55700415e-02 -2.66607732e-01
5.65190554e-01 -5.46875238e-01 -9.74692822e-01 -1.17039204e+00
4.38146949e-01 2.86107123e-01 5.62003732e-01 -3.07702214... | [10.84497356414795, -4.104945182800293] |
3b2334c0-c917-4db1-8376-9acc3299a553 | decomposed-temporal-dynamic-cnn-efficient | 2203.15277 | null | https://arxiv.org/abs/2203.15277v2 | https://arxiv.org/pdf/2203.15277v2.pdf | Decomposed Temporal Dynamic CNN: Efficient Time-Adaptive Network for Text-Independent Speaker Verification Explained with Speaker Activation Map | To extract accurate speaker information for text-independent speaker verification, temporal dynamic CNNs (TDY-CNNs) adapting kernels to each time bin was proposed. However, model size of TDY-CNN is too large and the adaptive kernel's degree of freedom is limited. To address these limitations, we propose decomposed temp... | ['Yong-Hwa Park', 'Hyeonuk Nam', 'Seong-Hu Kim'] | 2022-03-29 | null | null | null | null | ['text-independent-speaker-verification'] | ['speech'] | [-3.29318881e-01 -2.66801149e-01 1.75246537e-01 -3.71311665e-01
-8.85887921e-01 -4.20444191e-01 1.43143684e-01 -3.64499301e-01
-6.36319399e-01 3.18953037e-01 3.86524409e-01 -3.84038955e-01
3.73436630e-01 -3.07138592e-01 -3.62642258e-01 -8.35709751e-01
-3.16364348e-01 -2.96896607e-01 3.47910859e-02 -2.43685037... | [14.372838973999023, 6.087403774261475] |
c503381b-a88a-4a44-a728-003f65eaedf5 | a-novel-transferability-attention-neural | 2009.09585 | null | https://arxiv.org/abs/2009.09585v1 | https://arxiv.org/pdf/2009.09585v1.pdf | A Novel Transferability Attention Neural Network Model for EEG Emotion Recognition | The existed methods for electroencephalograph (EEG) emotion recognition always train the models based on all the EEG samples indistinguishably. However, some of the source (training) samples may lead to a negative influence because they are significant dissimilar with the target (test) samples. So it is necessary to gi... | ['Guangming Shi', 'Boxun Fu', 'Yang Li', 'Wenming Zheng', 'Fu Li'] | 2020-09-21 | null | null | null | null | ['eeg-emotion-recognition'] | ['miscellaneous'] | [-5.00102807e-03 -1.79257691e-01 3.72864097e-01 -8.30105841e-01
-3.50495964e-01 -1.30274385e-01 -3.10878642e-02 -1.38959676e-01
-2.51298338e-01 9.41153169e-01 -2.72516645e-02 3.61082554e-01
-9.69081447e-02 -6.26851499e-01 -6.73142731e-01 -9.81299996e-01
-2.33451873e-01 -2.29881257e-02 -2.45139807e-01 -1.26081169... | [13.123860359191895, 3.493354082107544] |
c47b763f-d45d-4e6f-9e09-2ae24c39b288 | visualizing-global-explanations-of-point | 2203.09505 | null | https://arxiv.org/abs/2203.09505v2 | https://arxiv.org/pdf/2203.09505v2.pdf | Visualizing Global Explanations of Point Cloud DNNs | In the field of autonomous driving and robotics, point clouds are showing their excellent real-time performance as raw data from most of the mainstream 3D sensors. Therefore, point cloud neural networks have become a popular research direction in recent years. So far, however, there has been little discussion about the... | ['Hanxiao Tan'] | 2022-03-17 | null | null | null | null | ['point-cloud-classification'] | ['computer-vision'] | [-3.10843259e-01 2.75142640e-01 -3.89467806e-01 -7.28678405e-01
-3.02683890e-01 -2.14447394e-01 7.92651832e-01 1.45471364e-01
3.52669120e-01 5.40093958e-01 -3.85535620e-02 -6.26670361e-01
-4.79142457e-01 -7.32985198e-01 -1.00688374e+00 -4.12640601e-01
2.05586344e-01 6.84486568e-01 -3.05897649e-03 -2.74842829... | [8.115801811218262, -3.4908127784729004] |
a55e8795-6258-4361-9d39-63871e0b52e0 | naomi-non-autoregressive-multiresolution | 1901.10946 | null | https://arxiv.org/abs/1901.10946v3 | https://arxiv.org/pdf/1901.10946v3.pdf | NAOMI: Non-Autoregressive Multiresolution Sequence Imputation | Missing value imputation is a fundamental problem in spatiotemporal modeling, from motion tracking to the dynamics of physical systems. Deep autoregressive models suffer from error propagation which becomes catastrophic for imputing long-range sequences. In this paper, we take a non-autoregressive approach and propose ... | ['Yukai Liu', 'Yisong Yue', 'Rose Yu', 'Stephan Zheng', 'Eric Zhan'] | 2019-01-30 | naomi-non-autoregressive-multiresolution-1 | http://papers.nips.cc/paper/9302-naomi-non-autoregressive-multiresolution-sequence-imputation | http://papers.nips.cc/paper/9302-naomi-non-autoregressive-multiresolution-sequence-imputation.pdf | neurips-2019-12 | ['multivariate-time-series-imputation'] | ['time-series'] | [ 3.22095841e-01 -4.31007355e-01 -1.21548414e-01 -3.04166734e-01
-1.21425533e+00 -5.53739250e-01 6.23132706e-01 -5.57352901e-01
-9.17713568e-02 1.30961454e+00 5.81006765e-01 -1.84053808e-01
-2.86322623e-01 -8.70208561e-01 -1.12278318e+00 -7.96005785e-01
-2.48650044e-01 4.14170921e-01 -8.49206522e-02 -2.34860331... | [7.036133289337158, 3.2579617500305176] |
935fe2c8-a11f-447a-a309-b385fdf836ca | attentive-one-dimensional-heatmap-regression | 2004.02108 | null | https://arxiv.org/abs/2004.02108v7 | https://arxiv.org/pdf/2004.02108v7.pdf | Attentive One-Dimensional Heatmap Regression for Facial Landmark Detection and Tracking | Although heatmap regression is considered a state-of-the-art method to locate facial landmarks, it suffers from huge spatial complexity and is prone to quantization error. To address this, we propose a novel attentive one-dimensional heatmap regression method for facial landmark localization. First, we predict two grou... | ['Shangfei Wang', 'Enhong Chen', 'Xiaoping Chen', 'Shi Yin'] | 2020-04-05 | null | null | null | null | ['landmark-tracking'] | ['computer-vision'] | [-2.78916627e-01 -1.78394392e-01 -2.37146780e-01 -5.05352736e-01
-8.01326513e-01 -7.07847178e-02 4.39498335e-01 5.60688786e-02
-2.75644273e-01 2.17268690e-01 -1.29432958e-02 1.00791410e-01
6.22986350e-03 -6.89357340e-01 -5.68264663e-01 -8.41033995e-01
-7.94461966e-02 1.71275765e-01 9.72067192e-02 2.89396942... | [13.506365776062012, 0.38331639766693115] |
755c2bbb-b8a7-4328-862d-80742262e01c | ur-channel-robust-synthetic-speech-detection | 2107.12018 | null | https://arxiv.org/abs/2107.12018v2 | https://arxiv.org/pdf/2107.12018v2.pdf | UR Channel-Robust Synthetic Speech Detection System for ASVspoof 2021 | In this paper, we present UR-AIR system submission to the logical access (LA) and the speech deepfake (DF) tracks of the ASVspoof 2021 Challenge. The LA and DF tasks focus on synthetic speech detection (SSD), i.e. detecting text-to-speech and voice conversion as spoofing attacks. Different from previous ASVspoof challe... | ['Zhiyao Duan', 'Ge Zhu', 'You Zhang', 'Xinhui Chen'] | 2021-07-26 | null | null | null | null | ['synthetic-speech-detection'] | ['audio'] | [ 1.34471014e-01 -1.52493753e-02 -1.75963029e-01 -2.64629517e-02
-1.17068434e+00 -4.67887998e-01 4.44847375e-01 -1.02367416e-01
-2.59938270e-01 2.81032473e-01 4.32915092e-01 -1.11047435e+00
3.21068704e-01 -1.82009861e-01 -7.31048346e-01 -3.88772368e-01
-2.68728107e-01 -2.13285595e-01 2.19670057e-01 -1.02965541... | [14.7681303024292, 6.0637969970703125] |
78663235-6759-45ca-ac60-a181baeccb57 | transforming-musical-signals-through-a-genre | 1706.09553 | null | http://arxiv.org/abs/1706.09553v1 | http://arxiv.org/pdf/1706.09553v1.pdf | Transforming Musical Signals through a Genre Classifying Convolutional Neural Network | Convolutional neural networks (CNNs) have been successfully applied on both
discriminative and generative modeling for music-related tasks. For a
particular task, the trained CNN contains information representing the decision
making or the abstracting process. One can hope to manipulate existing music
based on this 'in... | ['G. Ren', 'S. Geng', 'M. Ogihara'] | 2017-06-29 | null | null | null | null | ['genre-classification'] | ['computer-vision'] | [ 5.39851069e-01 3.10338348e-01 7.09720477e-02 -2.59014100e-01
-2.59159803e-01 -6.60958588e-01 4.06254917e-01 -1.72255903e-01
-2.18542278e-01 4.72754747e-01 3.71316969e-01 4.48550105e-01
-1.96339726e-01 -1.08251667e+00 -6.85699642e-01 -7.82385290e-01
4.90903743e-02 3.12746406e-01 -2.40688309e-01 -2.57696450... | [15.921854972839355, 5.36971378326416] |
460c29de-0e93-4021-9ade-6dbe9b272aaa | cross-lingual-evidence-improves-monolingual | null | null | https://aclanthology.org/2021.acl-srw.32 | https://aclanthology.org/2021.acl-srw.32.pdf | Cross-lingual Evidence Improves Monolingual Fake News Detection | Misleading information spreads on the Internet at an incredible speed, which can lead to irreparable consequences in some cases. Therefore, it is becoming essential to develop fake news detection technologies. While substantial work has been done in this direction, one of the limitations of the current approaches is th... | ['Alexander Panchenko', 'Daryna Dementieva'] | 2021-08-01 | null | null | null | acl-2021-5 | ['news-classification'] | ['natural-language-processing'] | [-3.89270365e-01 3.07939685e-04 -4.95084137e-01 -1.51538640e-01
-9.78847742e-01 -6.71608090e-01 1.40256643e+00 2.99828976e-01
-2.39112213e-01 9.69387174e-01 2.82273501e-01 -3.14339310e-01
3.46986383e-01 -7.25727499e-01 -8.53362203e-01 -3.28133404e-01
3.71794373e-01 4.10146385e-01 6.58519387e-01 -6.98303699... | [8.160930633544922, 10.259340286254883] |
b0304d18-559c-46a6-b7c3-015493cec72f | vulcan-solving-the-steiner-tree-problem-with | 2111.10810 | null | https://arxiv.org/abs/2111.10810v1 | https://arxiv.org/pdf/2111.10810v1.pdf | Vulcan: Solving the Steiner Tree Problem with Graph Neural Networks and Deep Reinforcement Learning | Steiner Tree Problem (STP) in graphs aims to find a tree of minimum weight in the graph that connects a given set of vertices. It is a classic NP-hard combinatorial optimization problem and has many real-world applications (e.g., VLSI chip design, transportation network planning and wireless sensor networks). Many exac... | ['Qinqing Zhan', 'Qiao Xiang', 'Zong Yan', 'Haizhou Du'] | 2021-11-21 | null | null | null | null | ['steiner-tree-problem'] | ['graphs'] | [ 9.48594734e-02 3.96589100e-01 -4.85051960e-01 -3.08435082e-01
-3.11023444e-01 -5.73959112e-01 -1.64355770e-01 2.06504613e-01
-1.00500159e-01 7.74588823e-01 -4.34153855e-01 -5.78307152e-01
-6.91485643e-01 -1.44083977e+00 -1.11138058e+00 -6.22538507e-01
-4.76273715e-01 8.24754477e-01 1.64547592e-01 -3.35736483... | [5.248708724975586, 2.8035025596618652] |
2b534832-51d4-46df-adbb-cf4b959c081a | change-point-detection-in-wind-turbine-scada | null | null | https://wes.copernicus.org/articles/5/1375/2020/ | https://wes.copernicus.org/articles/5/1375/2020/wes-5-1375-2020.pdf | Change-point detection in wind turbine SCADA data for robust condition monitoring with normal behaviour models | Analysis of data from wind turbine supervisory control and data acquisition (SCADA) systems has attracted considerable research interest in recent years. Its predominant application is to monitor turbine condition without the need for additional sensing equipment. Most approaches apply semi-supervised anomaly detection... | ['Simon Letzgus'] | 2020-10-27 | null | null | null | wind-energy-science-2020-10 | ['supervised-anomaly-detection', 'semi-supervised-anomaly-detection'] | ['computer-vision', 'computer-vision'] | [ 1.55060142e-01 -4.24147516e-01 2.14485854e-01 5.07311635e-02
-4.31604236e-01 -8.74018788e-01 5.78898311e-01 7.32745647e-01
-2.71423846e-01 6.01941049e-01 -5.29978931e-01 -4.03964877e-01
-4.90989655e-01 -6.07046545e-01 -1.21451803e-01 -9.91362691e-01
-4.87902373e-01 1.68012604e-02 4.34028208e-01 -1.95888668... | [6.609753608703613, 2.4783434867858887] |
a4b8ebcf-441e-426a-b473-185e4a2c5d6e | real-time-pose-and-shape-reconstruction-of | 2106.08059 | null | https://arxiv.org/abs/2106.08059v1 | https://arxiv.org/pdf/2106.08059v1.pdf | Real-time Pose and Shape Reconstruction of Two Interacting Hands With a Single Depth Camera | We present a novel method for real-time pose and shape reconstruction of two strongly interacting hands. Our approach is the first two-hand tracking solution that combines an extensive list of favorable properties, namely it is marker-less, uses a single consumer-level depth camera, runs in real time, handles inter- an... | ['Christian Theobalt', 'Dan Casas', 'Miguel A. Otaduy', 'Mickeal Verschoor', 'Oleksandr Sotnychenko', 'Florian Bernard', 'Micah Davis', 'Franziska Mueller'] | 2021-06-15 | null | null | null | null | ['physical-simulations'] | ['miscellaneous'] | [ 3.77302691e-02 -2.21828863e-01 1.00301959e-01 -6.16404265e-02
-6.98154569e-01 -5.43701410e-01 3.65421288e-02 -1.69761032e-01
-5.48399210e-01 3.33882719e-01 -2.65345424e-01 -1.15628920e-01
-1.74033225e-01 -2.93339670e-01 -7.75068223e-01 -4.31042254e-01
-7.32859671e-02 1.21413267e+00 4.31252599e-01 -2.24446714... | [6.603609561920166, -0.9152875542640686] |
8624e8ae-a9dd-4d8a-9747-695b2e948157 | fast-distributed-submodular-cover-public | null | null | http://papers.nips.cc/paper/6540-fast-distributed-submodular-cover-public-private-data-summarization | http://papers.nips.cc/paper/6540-fast-distributed-submodular-cover-public-private-data-summarization.pdf | Fast Distributed Submodular Cover: Public-Private Data Summarization | In this paper, we introduce the public-private framework of data summarization motivated by privacy concerns in personalized recommender systems and online social services. Such systems have usually access to massive data generated by a large pool of users. A major fraction of the data is public and is visible to (and ... | ['Morteza Zadimoghaddam', 'Baharan Mirzasoleiman', 'Amin Karbasi'] | 2016-12-01 | null | null | null | neurips-2016-12 | ['movie-recommendation', 'data-summarization'] | ['miscellaneous', 'miscellaneous'] | [ 1.40181277e-02 4.74259943e-01 -2.97781199e-01 -2.89250761e-01
-1.02526677e+00 -9.14409220e-01 -1.08381189e-01 6.83871269e-01
-1.27833456e-01 9.81584370e-01 5.45471787e-01 2.00412422e-01
-2.48011321e-01 -9.04267192e-01 -7.79627621e-01 -7.07734466e-01
-3.00750379e-02 6.22034371e-01 6.01535738e-02 -2.23855913... | [6.567826271057129, 4.987446308135986] |
170ee4c6-2cdb-4254-8027-fb42dfbd5c25 | vrebert-a-simple-and-flexible-transformer-for | 2206.09111 | null | https://arxiv.org/abs/2206.09111v1 | https://arxiv.org/pdf/2206.09111v1.pdf | VReBERT: A Simple and Flexible Transformer for Visual Relationship Detection | Visual Relationship Detection (VRD) impels a computer vision model to 'see' beyond an individual object instance and 'understand' how different objects in a scene are related. The traditional way of VRD is first to detect objects in an image and then separately predict the relationship between the detected object insta... | ['Moshiur Farazi', 'Yu Cui'] | 2022-06-18 | null | null | null | null | ['visual-relationship-detection'] | ['computer-vision'] | [ 2.49854401e-01 3.37565154e-01 8.71488079e-02 -3.90404165e-01
-5.05414307e-01 -4.65440184e-01 7.12537348e-01 6.41974032e-01
-8.28424096e-02 1.09286450e-01 -1.38630107e-01 -2.28218630e-01
-9.45275947e-02 -8.65719318e-01 -9.58171725e-01 -2.35229775e-01
7.07935691e-02 7.71775961e-01 9.55610037e-01 -1.89405277... | [10.275918006896973, 1.6510984897613525] |
d2303093-74b6-45d6-a0dc-bbf06aff9304 | a-role-for-prior-knowledge-in-statistical | 2012.00538 | null | https://arxiv.org/abs/2012.00538v1 | https://arxiv.org/pdf/2012.00538v1.pdf | A Role for Prior Knowledge in Statistical Classification of the Transition from MCI to Alzheimer's Disease | The transition from mild cognitive impairment (MCI) to Alzheimer's disease (AD) is of great interest to clinical researchers. This phenomenon also serves as a valuable data source for quantitative methodological researchers developing new approaches for classification. However, the growth of machine learning (ML) appro... | ['Andrew R. Bender', 'Tapabrate Maiti', 'Zihuan Liu'] | 2020-11-28 | null | null | null | null | ['clinical-knowledge'] | ['miscellaneous'] | [ 2.09509760e-01 -2.79762685e-01 -3.78873646e-01 -7.30389893e-01
-8.09672058e-01 -1.55199811e-01 4.75405276e-01 4.62862968e-01
-8.86267960e-01 1.05516422e+00 1.23626597e-01 -5.19941628e-01
-3.31126273e-01 -6.74294114e-01 -1.14835575e-01 -5.33596873e-01
-2.56708801e-01 7.19628990e-01 2.10941121e-01 1.51405320... | [14.167820930480957, -1.7490606307983398] |
eb75dbe9-81a1-4b2d-9fc4-03c43eaed73b | a-novel-structured-argumentation-framework | 2306.15500 | null | https://arxiv.org/abs/2306.15500v1 | https://arxiv.org/pdf/2306.15500v1.pdf | A novel structured argumentation framework for improved explainability of classification tasks | This paper presents a novel framework for structured argumentation, named extend argumentative decision graph ($xADG$). It is an extension of argumentative decision graphs built upon Dung's abstract argumentation graphs. The $xADG$ framework allows for arguments to use boolean logic operators and multiple premises (sup... | ['Luca Longo', 'Lucas Rizzo'] | 2023-06-27 | null | null | null | null | ['abstract-argumentation', 'abstract-argumentation'] | ['natural-language-processing', 'reasoning'] | [-9.99796763e-02 1.44010544e+00 -5.98814487e-01 -4.65098053e-01
-2.68439621e-01 -6.63591623e-01 7.29949296e-01 1.03319263e+00
3.43884267e-02 1.16288245e+00 -3.46814990e-02 -1.40474260e+00
-8.42392623e-01 -1.31086659e+00 -6.06729567e-01 -9.83750075e-02
-3.09856594e-01 4.24887151e-01 1.34893715e-01 -5.77829838... | [8.91783618927002, 7.027563095092773] |
49d3df84-cf4f-42e9-9692-18e3e7b22a6c | learning-to-draw-emergent-communication | 2106.02067 | null | https://arxiv.org/abs/2106.02067v2 | https://arxiv.org/pdf/2106.02067v2.pdf | Learning to Draw: Emergent Communication through Sketching | Evidence that visual communication preceded written language and provided a basis for it goes back to prehistory, in forms such as cave and rock paintings depicting traces of our distant ancestors. Emergent communication research has sought to explore how agents can learn to communicate in order to collaboratively solv... | ['Jonathon Hare', 'Daniela Mihai'] | 2021-06-03 | null | http://proceedings.neurips.cc/paper/2021/hash/39d0a8908fbe6c18039ea8227f827023-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/39d0a8908fbe6c18039ea8227f827023-Paper.pdf | neurips-2021-12 | ['prehistory'] | ['miscellaneous'] | [ 7.94983283e-02 4.84117061e-01 2.66882420e-01 -2.39040300e-01
1.49145290e-01 -9.79720354e-01 1.44442260e+00 2.81045921e-02
-5.94171762e-01 8.14695835e-01 4.91923541e-01 -6.76590443e-01
2.10095927e-01 -9.06964898e-01 -7.15809822e-01 -4.98673975e-01
-5.59214473e-01 7.12076604e-01 -3.91939908e-01 -3.24172199... | [4.408349990844727, 1.5164806842803955] |
82b4a68e-c5fb-4fba-bb50-f25675233ed8 | vn-transformer-rotation-equivariant-attention | 2206.04176 | null | https://arxiv.org/abs/2206.04176v3 | https://arxiv.org/pdf/2206.04176v3.pdf | VN-Transformer: Rotation-Equivariant Attention for Vector Neurons | Rotation equivariance is a desirable property in many practical applications such as motion forecasting and 3D perception, where it can offer benefits like sample efficiency, better generalization, and robustness to input perturbations. Vector Neurons (VN) is a recently developed framework offering a simple yet effecti... | ['Ben Sapp', 'Nigamaa Nayakanti', 'Rami Al-Rfou', 'Carlton Downey', 'Serge Assaad'] | 2022-06-08 | null | null | null | null | ['3d-shape-retrieval'] | ['computer-vision'] | [ 5.94295897e-02 -2.14899123e-01 -1.30363926e-01 -3.20678294e-01
-7.30336368e-01 -5.86389244e-01 5.43824971e-01 -4.37892199e-01
-3.56467426e-01 3.79821002e-01 -2.98752449e-03 -6.93423569e-01
-1.69307634e-01 -6.58188522e-01 -1.12779593e+00 -6.37265980e-01
-1.77882388e-01 1.41256049e-01 -2.63890829e-02 -4.69362199... | [8.004807472229004, -3.591398000717163] |
88cb6766-307e-441d-b9a2-5fd8a8ad2b78 | heterogeneous-neuronal-and-synaptic-dynamics | 2302.11618 | null | https://arxiv.org/abs/2302.11618v1 | https://arxiv.org/pdf/2302.11618v1.pdf | Heterogeneous Neuronal and Synaptic Dynamics for Spike-Efficient Unsupervised Learning: Theory and Design Principles | This paper shows that the heterogeneity in neuronal and synaptic dynamics reduces the spiking activity of a Recurrent Spiking Neural Network (RSNN) while improving prediction performance, enabling spike-efficient (unsupervised) learning. We analytically show that the diversity in neurons' integration/relaxation dynamic... | ['Saibal Mukhopadhyay', 'Biswadeep Chakraborty'] | 2023-02-22 | null | null | null | null | ['time-series-classification'] | ['time-series'] | [ 3.99261981e-01 -2.42988631e-01 -1.67546347e-02 3.12246084e-02
-1.44001961e-01 -4.28751469e-01 1.65623143e-01 -2.76569307e-01
-5.91881752e-01 1.18833518e+00 -1.29507840e-01 9.25444886e-02
-3.69445920e-01 -6.42251790e-01 -7.93502867e-01 -1.41173172e+00
-2.14759871e-01 -2.50930130e-03 6.37909710e-01 6.71408847... | [8.10244083404541, 2.6119487285614014] |
3064b594-489b-403c-93f8-28bc83f14a92 | measuring-the-privacy-leakage-via-graph | 2302.04373 | null | https://arxiv.org/abs/2302.04373v1 | https://arxiv.org/pdf/2302.04373v1.pdf | Measuring the Privacy Leakage via Graph Reconstruction Attacks on Simplicial Neural Networks (Student Abstract) | In this paper, we measure the privacy leakage via studying whether graph representations can be inverted to recover the graph used to generate them via graph reconstruction attack (GRA). We propose a GRA that recovers a graph's adjacency matrix from the representations via a graph decoder that minimizes the reconstruct... | ['Victor S. Sheng', 'Keyi Lu', 'Kun Zhang', 'Huixin Zhan'] | 2023-02-08 | null | null | null | null | ['graph-reconstruction'] | ['graphs'] | [ 4.84441906e-01 8.82699251e-01 9.10854563e-02 6.07651286e-03
-4.12727058e-01 -1.14662492e+00 4.54676062e-01 1.40960500e-01
1.70891300e-01 6.63810492e-01 2.32642964e-01 -7.70168841e-01
2.57144906e-02 -1.35872602e+00 -1.27366996e+00 -7.53959835e-01
-3.42530191e-01 -2.92488723e-04 -1.44561604e-01 -3.69215548... | [6.063649654388428, 7.128176212310791] |
f4d5f5d1-a24f-40e5-98aa-0e3e4d1ed577 | automatic-player-identification-in-dota-2 | 2008.12401 | null | https://arxiv.org/abs/2008.12401v1 | https://arxiv.org/pdf/2008.12401v1.pdf | Automatic Player Identification in Dota 2 | Dota 2 is a popular, multiplayer online video game. Like many online games, players are mostly anonymous, being tied only to online accounts which can be readily obtained, sold and shared between multiple people. This makes it difficult to track or ban players who exhibit unwanted behavior online. In this paper, we pre... | ['Sizhe Yuen', 'Oliver Don', 'John D. Thomson'] | 2020-08-27 | null | null | null | null | ['dota-2'] | ['playing-games'] | [-2.65101314e-01 -8.16549361e-02 -2.60557830e-01 1.17475323e-01
-5.91075957e-01 -1.05320847e+00 3.85562927e-01 2.70024426e-02
-7.71138191e-01 7.17179060e-01 -1.66726291e-01 -5.26334822e-01
-4.01659012e-02 -9.77247238e-01 -2.99851000e-01 -1.25642166e-01
-2.95721740e-01 5.94816685e-01 9.46635842e-01 -1.34222224... | [3.542489767074585, 1.4548343420028687] |
d8253507-6d6f-4f2e-b9bc-9b716ec8d0df | towards-modality-agnostic-person-re | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Chen_Towards_Modality-Agnostic_Person_Re-Identification_With_Descriptive_Query_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Chen_Towards_Modality-Agnostic_Person_Re-Identification_With_Descriptive_Query_CVPR_2023_paper.pdf | Towards Modality-Agnostic Person Re-Identification With Descriptive Query | Person re-identification (ReID) with descriptive query (text or sketch) provides an important supplement for general image-image paradigms, which is usually studied in a single cross-modality matching manner, e.g., text-to-image or sketch-to-photo. However, without a camera-captured photo query, it is uncertain whe... | ['Ding Jiang', 'Mang Ye', 'Cuiqun Chen'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['person-re-identification'] | ['computer-vision'] | [ 1.96551234e-01 -7.15543747e-01 -1.56439498e-01 -4.66703117e-01
-8.38416755e-01 -6.17733955e-01 8.83384824e-01 -1.99169755e-01
-6.46892667e-01 5.29971540e-01 2.63265908e-01 2.71566331e-01
-1.50604725e-01 -4.65112031e-01 -6.06586277e-01 -5.80020130e-01
7.85432577e-01 5.88740587e-01 -1.53145134e-01 -1.17973043... | [14.657798767089844, 0.9324417114257812] |
bec8551f-18bc-4bf5-b41a-92a2384cf4e3 | i-vector-text-independent-speaker | null | null | https://aclanthology.org/O13-1016 | https://aclanthology.org/O13-1016.pdf | 結合I-Vector 及深層神經網路之語者驗證系統 (Text-independent Speaker Verification using a Hybrid I-Vector/DNN Approach) [In Chinese] | null | ['Wen-Tsung Chang', 'Chia-Wei Liao', 'Kai-Hsuan Chan', 'Shao-Hua Cheng', 'Yun-Fan Chang', 'Yu Tsao'] | 2013-10-01 | i-vector-text-independent-speaker-1 | https://aclanthology.org/O13-1016 | https://aclanthology.org/O13-1016.pdf | roclingijclclp-2013-10 | ['text-independent-speaker-verification'] | ['speech'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.244976997375488, 3.671905755996704] |
ce8a7e69-52e1-47c0-97d3-6cd7d05d0cd8 | towards-unsupervised-speech-to-text | 1811.01307 | null | http://arxiv.org/abs/1811.01307v1 | http://arxiv.org/pdf/1811.01307v1.pdf | Towards Unsupervised Speech-to-Text Translation | We present a framework for building speech-to-text translation (ST) systems
using only monolingual speech and text corpora, in other words, speech
utterances from a source language and independent text from a target language.
As opposed to traditional cascaded systems and end-to-end architectures, our
system does not r... | ['Wei-Hung Weng', 'Yu-An Chung', 'Schrasing Tong', 'James Glass'] | 2018-11-04 | null | null | null | null | ['speech-to-text-translation'] | ['natural-language-processing'] | [ 2.21990570e-01 1.78647354e-01 -3.20237964e-01 -5.94402075e-01
-1.44831026e+00 -7.49270439e-01 6.73482060e-01 -2.74413556e-01
-3.86251211e-01 6.82667494e-01 4.66075063e-01 -7.48028278e-01
7.72595525e-01 -3.30456644e-01 -8.74341905e-01 -4.31811780e-01
3.62328172e-01 8.66895914e-01 -2.27472544e-01 -3.78225178... | [14.500219345092773, 7.162752628326416] |
1d22315e-178a-4f8c-a1d8-c3ae6cff9279 | gexse-generative-explanatory-sensor-system-an | 2306.15857 | null | https://arxiv.org/abs/2306.15857v1 | https://arxiv.org/pdf/2306.15857v1.pdf | GeXSe (Generative Explanatory Sensor System): An Interpretable Deep Generative Model for Human Activity Recognition in Smart Spaces | We introduce GeXSe (Generative Explanatory Sensor System), a novel framework designed to extract interpretable sensor-based and vision domain features from non-invasive smart space sensors. We combine these to provide a comprehensive explanation of sensor-activation patterns in activity recognition tasks. This system l... | ['Jorge Ortiz', 'Murtadha Aldeer', 'Viswa Vijeth Ramesh', 'Nandana Pai', 'Yuan Sun'] | 2023-06-28 | null | null | null | null | ['activity-recognition', 'human-activity-recognition', 'human-activity-recognition'] | ['computer-vision', 'computer-vision', 'time-series'] | [ 6.81063473e-01 4.41477388e-01 -3.34913164e-01 -3.68285030e-01
-4.99430060e-01 -5.79586983e-01 6.00579679e-01 -9.64904670e-03
4.63317223e-02 5.16874611e-01 6.48489475e-01 -3.14351231e-01
-3.36470306e-01 -3.58318746e-01 -7.68375397e-01 -6.56211257e-01
-2.52481848e-01 -1.91964403e-01 -3.11314732e-01 7.04722404... | [7.9894700050354, 0.6596238613128662] |
9dde3202-a1b8-475c-9b55-d3f039f080fb | attention-based-3d-object-reconstruction-from | 2008.04738 | null | https://arxiv.org/abs/2008.04738v1 | https://arxiv.org/pdf/2008.04738v1.pdf | Attention-based 3D Object Reconstruction from a Single Image | Recently, learning-based approaches for 3D reconstruction from 2D images have gained popularity due to its modern applications, e.g., 3D printers, autonomous robots, self-driving cars, virtual reality, and augmented reality. The computer vision community has applied a great effort in developing functions to reconstruct... | ['Nathan Gavenski', 'Rodrigo Barros', 'Felipe Tasoniero', 'Eduardo Pooch', 'Andrey Salvi'] | 2020-08-11 | null | null | null | null | ['3d-object-reconstruction', '3d-object-reconstruction-from-a-single-image'] | ['computer-vision', 'computer-vision'] | [-3.48979654e-03 3.32009643e-01 6.47862628e-02 -2.44402662e-01
-6.05135381e-01 -2.87687510e-01 5.79729855e-01 -7.02723414e-02
-1.28451303e-01 4.95543480e-01 6.19875118e-02 -3.94191081e-03
-1.64155304e-01 -1.01169157e+00 -1.19231498e+00 -2.27916703e-01
-7.43002370e-02 7.16489971e-01 3.60417396e-01 -3.66809428... | [8.420536041259766, -3.4987101554870605] |
eb739eac-56b0-43b9-96ea-6b0f4f647287 | integrating-geometric-control-into-text-to | 2306.04607 | null | https://arxiv.org/abs/2306.04607v4 | https://arxiv.org/pdf/2306.04607v4.pdf | Integrating Geometric Control into Text-to-Image Diffusion Models for High-Quality Detection Data Generation via Text Prompt | Diffusion models have attracted significant attention due to their remarkable ability to create content and generate data for tasks such as image classification. However, the usage of diffusion models to generate high-quality object detection data remains an underexplored area, where not only the image-level perceptual... | ['Dit-yan Yeung', 'Zhenguo Li', 'Lanqing Hong', 'Zhe Chen', 'Enze Xie', 'Kai Chen'] | 2023-06-07 | null | null | null | null | ['layout-to-image-generation'] | ['computer-vision'] | [ 3.41363043e-01 -6.87683672e-02 2.05913365e-01 -3.19305539e-01
-4.87422705e-01 -7.05839336e-01 9.41196620e-01 -5.37680686e-02
-4.44986783e-02 3.22425157e-01 -5.92542067e-02 -4.81631070e-01
1.70388073e-01 -1.08883679e+00 -8.71630251e-01 -4.61767703e-01
2.75438726e-01 3.59490603e-01 7.10757554e-01 -2.30816349... | [11.360339164733887, -0.20658333599567413] |
3319d64a-3937-452b-b5bc-7441c2c9dec0 | inesc-id-a-regression-model-for-large-scale | null | null | https://aclanthology.org/S15-2102 | https://aclanthology.org/S15-2102.pdf | INESC-ID: A Regression Model for Large Scale Twitter Sentiment Lexicon Induction | null | ['Wang Ling', 'Ramon Astudillo', 'Silvio Amir', 'Isabel Trancoso', 'Mario J. Silva', 'Bruno Martins'] | 2015-06-01 | null | null | null | semeval-2015-6 | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.206246852874756, 3.7183029651641846] |
e6274458-14b3-4f15-85fb-f363ed0f9b0d | dynamic-epistemic-logic-with-asp-updates | 1905.10621 | null | https://arxiv.org/abs/1905.10621v1 | https://arxiv.org/pdf/1905.10621v1.pdf | Dynamic Epistemic Logic with ASP Updates: Application to Conditional Planning | Dynamic Epistemic Logic (DEL) is a family of multimodal logics that has proved to be very successful for epistemic reasoning in planning tasks. In this logic, the agent's knowledge is captured by modal epistemic operators whereas the system evolution is described in terms of (some subset of) dynamic logic modalities in... | ['Luis Fariñas del Cerro', 'Jorge Fandinno', 'Pedro Cabalar'] | 2019-05-25 | null | null | null | null | ['epistemic-reasoning'] | ['miscellaneous'] | [ 1.56951427e-01 1.04471004e+00 -9.63051394e-02 -3.87846470e-01
-1.59118980e-01 -8.45011115e-01 1.28036761e+00 2.53306895e-01
-2.24576861e-01 1.13788497e+00 3.30308914e-01 -2.85696179e-01
-4.60627079e-01 -1.25757074e+00 -6.94401264e-01 -5.37562072e-01
-2.63029873e-01 5.98653972e-01 8.47391248e-01 -5.11263728... | [8.60581111907959, 6.654294013977051] |
95c5282a-fa52-4c42-a8db-1d5073a808f8 | adversarial-defense-via-neural-oscillation | 2211.02223 | null | https://arxiv.org/abs/2211.02223v1 | https://arxiv.org/pdf/2211.02223v1.pdf | Adversarial Defense via Neural Oscillation inspired Gradient Masking | Spiking neural networks (SNNs) attract great attention due to their low power consumption, low latency, and biological plausibility. As they are widely deployed in neuromorphic devices for low-power brain-inspired computing, security issues become increasingly important. However, compared to deep neural networks (DNNs)... | ['Yilei Zhang', 'Chunming Jiang'] | 2022-11-04 | null | null | null | null | ['adversarial-defense'] | ['adversarial'] | [ 3.18029553e-01 -1.58263847e-01 3.88776183e-01 -1.43801883e-01
4.83898409e-02 -8.04263294e-01 4.49855059e-01 -5.10407865e-01
-6.90986335e-01 9.18637395e-01 -2.72889167e-01 -2.97444940e-01
2.95114219e-01 -7.56454527e-01 -8.82613301e-01 -9.07080173e-01
1.49732279e-02 -2.09145606e-01 6.55271411e-01 -3.55948657... | [5.572777271270752, 7.819549083709717] |
a625af99-35b3-43bc-81b1-cf6b25a5d758 | hifi-gan-high-fidelity-denoising-and | 2006.05694 | null | https://arxiv.org/abs/2006.05694v2 | https://arxiv.org/pdf/2006.05694v2.pdf | HiFi-GAN: High-Fidelity Denoising and Dereverberation Based on Speech Deep Features in Adversarial Networks | Real-world audio recordings are often degraded by factors such as noise, reverberation, and equalization distortion. This paper introduces HiFi-GAN, a deep learning method to transform recorded speech to sound as though it had been recorded in a studio. We use an end-to-end feed-forward WaveNet architecture, trained wi... | ['Adam Finkelstein', 'Zeyu Jin', 'Jiaqi Su'] | 2020-06-10 | null | null | null | null | ['speech-dereverberation'] | ['speech'] | [ 2.60852277e-01 -5.61143272e-03 5.27500093e-01 -2.53231466e-01
-1.19444799e+00 -6.75006211e-01 1.93829224e-01 -5.02851903e-01
-2.36359268e-01 6.04173601e-01 6.51978493e-01 -5.96038103e-02
3.04938525e-01 -5.66114843e-01 -8.53573024e-01 -5.02559841e-01
-9.27827358e-02 -2.55924642e-01 -1.95525512e-01 -3.39443952... | [15.228394508361816, 6.006433963775635] |
b00945be-a473-4030-97df-304b4856ec43 | the-larger-they-are-the-harder-they-fail | 2305.15507 | null | https://arxiv.org/abs/2305.15507v1 | https://arxiv.org/pdf/2305.15507v1.pdf | The Larger They Are, the Harder They Fail: Language Models do not Recognize Identifier Swaps in Python | Large Language Models (LLMs) have successfully been applied to code generation tasks, raising the question of how well these models understand programming. Typical programming languages have invariances and equivariances in their semantics that human programmers intuitively understand and exploit, such as the (near) in... | ['Shay B. Cohen', 'Ioannis Konstas', 'Fazl Barez', 'Antonio Valerio Miceli-Barone'] | 2023-05-24 | null | null | null | null | ['code-generation'] | ['computer-code'] | [ 1.72537252e-01 3.53530467e-01 -1.04176290e-01 -5.61605096e-01
-3.49916108e-02 -7.30127633e-01 7.39736438e-01 3.50953043e-01
-2.12693483e-01 3.92312884e-01 2.23034799e-01 -8.52137804e-01
2.41420016e-01 -9.77068126e-01 -1.08641791e+00 3.17507237e-02
-7.85625875e-02 1.81517169e-01 2.02590540e-01 -3.87791425... | [7.938297271728516, 7.668320655822754] |
aaa23feb-1514-4af6-aad9-ed6bd871aa51 | on-the-generalization-of-learned-structured | 2304.13001 | null | https://arxiv.org/abs/2304.13001v1 | https://arxiv.org/pdf/2304.13001v1.pdf | On the Generalization of Learned Structured Representations | Despite tremendous progress over the past decade, deep learning methods generally fall short of human-level systematic generalization. It has been argued that explicitly capturing the underlying structure of data should allow connectionist systems to generalize in a more predictable and systematic manner. Indeed, evide... | ['Andrea Dittadi'] | 2023-04-25 | null | null | null | null | ['systematic-generalization'] | ['reasoning'] | [ 4.35638517e-01 5.05285621e-01 -3.42138439e-01 -6.67269170e-01
-1.14601061e-01 -6.56260669e-01 8.46017182e-01 2.81902552e-01
-1.19615726e-01 6.32813931e-01 3.73353928e-01 -3.22821856e-01
-4.73697573e-01 -9.34184670e-01 -9.73631740e-01 -4.25327212e-01
-1.51979299e-02 6.73350394e-01 -1.06981486e-01 -5.24842322... | [9.552433967590332, 6.9841837882995605] |
fcca35b7-bd05-40f4-a11d-8631ce075fb7 | instructed-diffuser-with-temporal-condition | 2306.04875 | null | https://arxiv.org/abs/2306.04875v1 | https://arxiv.org/pdf/2306.04875v1.pdf | Instructed Diffuser with Temporal Condition Guidance for Offline Reinforcement Learning | Recent works have shown the potential of diffusion models in computer vision and natural language processing. Apart from the classical supervised learning fields, diffusion models have also shown strong competitiveness in reinforcement learning (RL) by formulating decision-making as sequential generation. However, inco... | ['DaCheng Tao', 'Yi Chang', 'Lichao Sun', 'Li Shen', 'Hechang Chen', 'Siyuan Guo', 'Sili Huang', 'Yanchao Sun', 'Jifeng Hu'] | 2023-06-08 | null | null | null | null | ['offline-rl'] | ['playing-games'] | [ 2.96665609e-01 -1.85214072e-01 -5.44772267e-01 -1.49778232e-01
-3.95086437e-01 -7.10003793e-01 1.26986766e+00 -1.37481689e-01
-6.20300889e-01 7.54141629e-01 6.11464143e-01 -3.43013078e-01
-1.98069483e-01 -7.38921642e-01 -3.90730292e-01 -8.81230950e-01
-1.88853025e-01 2.72971243e-01 7.65212923e-02 -4.69438642... | [4.170811653137207, 1.787376046180725] |
ccc243c7-1301-4f15-984e-0029907eb0ac | ensemble-creation-via-anchored-regularization | 2210.06829 | null | https://arxiv.org/abs/2210.06829v1 | https://arxiv.org/pdf/2210.06829v1.pdf | Ensemble Creation via Anchored Regularization for Unsupervised Aspect Extraction | Aspect Based Sentiment Analysis is the most granular form of sentiment analysis that can be performed on the documents / sentences. Besides delivering the most insights at a finer grain, it also poses equally daunting challenges. One of them being the shortage of labelled data. To bring in value right out of the box fo... | ['Manu Joseph', 'Pulah Dhandekar'] | 2022-10-13 | null | null | null | null | ['aspect-extraction', 'aspect-based-sentiment-analysis'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.23205952e-01 4.67061102e-01 -1.13166131e-01 -5.70358753e-01
-6.59387648e-01 -5.10946333e-01 8.11670899e-01 4.31570590e-01
-2.60427713e-01 6.59338295e-01 6.08105302e-01 -3.39410275e-01
-2.89710648e-02 -1.03243625e+00 -3.77996951e-01 -7.10463762e-01
4.10472542e-01 7.31546938e-01 2.46255682e-03 -6.19979143... | [11.304464340209961, 6.808529376983643] |
6be3de31-c4ce-4394-b1e1-574ad799d5d2 | execution-based-code-generation-using-deep | 2301.13816 | null | https://arxiv.org/abs/2301.13816v2 | https://arxiv.org/pdf/2301.13816v2.pdf | Execution-based Code Generation using Deep Reinforcement Learning | The utilization of programming language (PL) models, pretrained on large-scale code corpora, as a means of automating software engineering processes has demonstrated considerable potential in streamlining various code generation tasks such as code completion, code translation, and program synthesis. However, current ap... | ['Chandan K. Reddy', 'Sindhu Tipirneni', 'Aneesh Jain', 'Parshin Shojaee'] | 2023-01-31 | null | null | null | null | ['code-translation', 'program-synthesis'] | ['computer-code', 'computer-code'] | [-4.28055972e-02 1.19777493e-01 -4.50325489e-01 -1.54089585e-01
-9.43931341e-01 -5.97270131e-01 4.28529352e-01 1.35144740e-01
1.27122894e-01 5.05922318e-01 8.67284164e-02 -6.62267148e-01
1.89754486e-01 -8.47485423e-01 -9.26007569e-01 -1.41405150e-01
1.03653058e-01 1.06800802e-01 -2.10817173e-01 -2.39381343... | [7.7967000007629395, 7.767953395843506] |
24e6e5c9-127e-4b1b-a734-928694b58199 | land-cover-segmentation-with-sparse | 2306.16252 | null | https://arxiv.org/abs/2306.16252v1 | https://arxiv.org/pdf/2306.16252v1.pdf | Land Cover Segmentation with Sparse Annotations from Sentinel-2 Imagery | Land cover (LC) segmentation plays a critical role in various applications, including environmental analysis and natural disaster management. However, generating accurate LC maps is a complex and time-consuming task that requires the expertise of multiple annotators and regular updates to account for environmental chan... | ['Fabrizio Dominici', 'Claudio Rossi', 'Luca Barco', 'Edoardo Arnaudo', 'Marco Galatola'] | 2023-06-28 | null | null | null | null | ['management'] | ['miscellaneous'] | [ 1.51551872e-01 3.41651104e-02 -3.28656495e-01 -3.80039662e-01
-1.01432693e+00 -8.46023023e-01 4.53603834e-01 5.25006115e-01
-4.36255336e-01 7.57383168e-01 -3.16954106e-02 -3.05899411e-01
2.48090878e-01 -1.18148565e+00 -6.86432600e-01 -5.43938816e-01
7.23585337e-02 6.92863703e-01 6.96379662e-01 -1.93271130... | [9.152070999145508, -1.5616509914398193] |
9aca0d4c-912a-4932-8c37-d1042ee8258e | emergent-communication-under-competition | 2101.10276 | null | https://arxiv.org/abs/2101.10276v1 | https://arxiv.org/pdf/2101.10276v1.pdf | Emergent Communication under Competition | The literature in modern machine learning has only negative results for learning to communicate between competitive agents using standard RL. We introduce a modified sender-receiver game to study the spectrum of partially-competitive scenarios and show communication can indeed emerge in a competitive setting. We empiri... | ['Aaron Courville', 'Angeliki Lazaridou', 'Travis LaCroix', 'Michael Noukhovitch'] | 2021-01-25 | null | null | null | null | ['misconceptions'] | ['miscellaneous'] | [ 1.96138814e-01 6.35856152e-01 1.46167070e-01 1.63347483e-01
-5.59619486e-01 -9.48303998e-01 8.08584452e-01 1.75209761e-01
-6.75895393e-01 1.24034703e+00 -1.03285313e-02 -1.93812251e-01
-3.92915338e-01 -5.91294110e-01 -8.28698814e-01 -9.75798368e-01
-9.13079441e-01 4.01383251e-01 -2.10226968e-01 -6.99307501... | [3.8875296115875244, 2.0451467037200928] |
8616f380-25ff-449c-b7cb-ec3c156e1bdf | lijunyi-at-semeval-2019-task-9-an-attention | null | null | https://aclanthology.org/S19-2212 | https://aclanthology.org/S19-2212.pdf | Lijunyi at SemEval-2019 Task 9: An attention-based LSTM and ensemble of different models for suggestion mining from online reviews and forums | In this paper, we describe a suggestion mining system that participated in SemEval 2019 Task 9, SubTask A - Suggestion Mining from Online Reviews and Forums. Given some suggestions from online reviews and forums that can be classified into suggestion and non-suggestion classes. In this task, we combine the attention me... | ['Junyi Li'] | 2019-06-01 | null | null | null | semeval-2019-6 | ['suggestion-mining'] | ['natural-language-processing'] | [-1.45868555e-01 4.69374269e-01 -2.09418640e-01 -6.72801375e-01
-3.51777226e-01 -2.11186963e-03 8.48279297e-01 4.52508852e-02
-6.14324331e-01 6.51721954e-01 3.40441823e-01 -1.01545715e+00
1.71953410e-01 -5.54933310e-01 -6.42564595e-01 -1.54572308e-01
1.16495397e-02 3.31296653e-01 1.68719366e-01 -4.12616521... | [10.92123031616211, 7.499916076660156] |
a3425567-069d-45dc-bb6f-b46abba9c0cd | document-level-relation-extraction-with-5 | 2212.10171 | null | https://arxiv.org/abs/2212.10171v1 | https://arxiv.org/pdf/2212.10171v1.pdf | Document-level Relation Extraction with Relation Correlations | Document-level relation extraction faces two overlooked challenges: long-tail problem and multi-label problem. Previous work focuses mainly on obtaining better contextual representations for entity pairs, hardly address the above challenges. In this paper, we analyze the co-occurrence correlation of relations, and intr... | ['Xiang Wan', 'Lu Liu', 'Benyou Wang', 'Tao Peng', 'Ridong Han'] | 2022-12-20 | null | null | null | null | ['document-level-relation-extraction'] | ['natural-language-processing'] | [-0.24839471 0.22286598 -0.78734386 -0.51585287 -0.6375822 -0.5728418
0.67292666 0.6109512 -0.26286173 0.7651231 0.8293629 -0.16457714
-0.5450312 -0.9014157 -0.24467212 -0.41214782 -0.2679314 0.69430006
0.10359047 -0.49688938 -0.02161562 0.5159665 -1.1268258 0.3961969
0.80218124 1.0388381 -0.17... | [9.165081977844238, 8.574543952941895] |
26e26906-2bb5-41a0-9e71-1a3f53b033d8 | encoder-decoder-network-with-guided | 2212.05936 | null | https://arxiv.org/abs/2212.05936v2 | https://arxiv.org/pdf/2212.05936v2.pdf | Encoder-Decoder Network with Guided Transmission Map: Architecture | An insight into the architecture of the Encoder-Decoder Network with Guided Transmission Map (EDN-GTM), a novel and effective single image dehazing scheme, is presented in this paper. The EDN-GTM takes a conventional RGB hazy image in conjunction with the corresponding transmission map estimated by the dark channel pri... | ['Dong-Chul Park', 'Le-Anh Tran'] | 2022-12-07 | null | null | null | null | ['image-dehazing'] | ['computer-vision'] | [ 6.11872435e-01 4.80573744e-01 3.48299265e-01 -3.38920504e-02
7.79620954e-04 1.83141798e-01 5.17342508e-01 -6.66569531e-01
-3.14154506e-01 5.80143452e-01 3.01580995e-01 -3.45659792e-01
-1.91826671e-01 -7.98555374e-01 -5.76612532e-01 -1.12339497e+00
-3.14331353e-01 -3.70033771e-01 5.43975890e-01 -4.65889066... | [10.936141014099121, -3.0776195526123047] |
c0741372-224b-4d5f-9a87-bb7fca68ea06 | a-mcts-search-with-theoretical-guarantee | null | null | https://openreview.net/forum?id=SJloA0EYDr | https://openreview.net/pdf?id=SJloA0EYDr | A⋆MCTS: SEARCH WITH THEORETICAL GUARANTEE USING POLICY AND VALUE FUNCTIONS | Combined with policy and value neural networks, Monte Carlos Tree Search (MCTS) is a critical component of the recent success of AI agents in learning to play board games like Chess and Go (Silver et al., 2017). However, the theoretical foundations of MCTS with policy and value networks remains open. Inspired by MCTS, ... | ['Lexing Ying', 'Yuandong Tian', 'Xian Wu'] | 2019-09-25 | null | null | null | null | ['board-games'] | ['playing-games'] | [-1.70507491e-01 1.20715171e-01 -6.08107269e-01 3.95253971e-02
-5.93666375e-01 -8.52407753e-01 5.15990257e-01 1.53667450e-01
-7.97067940e-01 1.00265896e+00 -9.42566991e-02 -5.02713740e-01
-3.27716529e-01 -9.84624267e-01 -7.37201452e-01 -8.63296330e-01
-3.22256148e-01 7.49525547e-01 4.59876418e-01 -1.01129122... | [4.078307628631592, 2.3173129558563232] |
3f4cabd7-3f99-40ba-b8db-ed0fe4c7a403 | magnetic-resonance-fingerprinting-1 | 1909.06395 | null | https://arxiv.org/abs/1909.06395v1 | https://arxiv.org/pdf/1909.06395v1.pdf | Magnetic Resonance Fingerprinting Reconstruction Using Recurrent Neural Networks | Magnetic Resonance Fingerprinting (MRF) is an imaging technique acquiring unique time signals for different tissues. Although the acquisition is highly accelerated, the reconstruction time remains a problem, as the state-of-the-art template matching compares every signal with a set of possible signals. To overcome this... | ['Andreas Maier', 'Gregor Körzdörfer', 'Heiko Meyer', 'Franziska Schirrmacher', 'Elisabeth Hoppe', 'Mathias Nittka', 'Florian Thamm', 'Christopher Syben', 'Josef Pfeuffer'] | 2019-09-13 | null | null | null | null | ['magnetic-resonance-fingerprinting'] | ['medical'] | [ 4.86556023e-01 -8.23703483e-02 -1.13460243e-01 -3.65026385e-01
-6.66377008e-01 -5.29211946e-02 3.00078779e-01 -1.82538286e-01
-5.19684911e-01 4.80458081e-01 2.70405244e-02 1.39004961e-02
-5.37085056e-01 -5.12247205e-01 -4.63520020e-01 -6.68372929e-01
-3.20521891e-01 2.27922305e-01 2.57407784e-01 -1.49277225... | [13.506223678588867, -2.3914318084716797] |
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