paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
7acef441-09be-41a9-a5d3-4421387aa2d1 | why-did-the-chicken-cross-the-road-rephrasing | 2211.07516 | null | https://arxiv.org/abs/2211.07516v2 | https://arxiv.org/pdf/2211.07516v2.pdf | Why Did the Chicken Cross the Road? Rephrasing and Analyzing Ambiguous Questions in VQA | Natural language is ambiguous. Resolving ambiguous questions is key to successfully answering them. Focusing on questions about images, we create a dataset of ambiguous examples. We annotate these, grouping answers by the underlying question they address and rephrasing the question for each group to reduce ambiguity. O... | ['Benjamin Van Durme', 'Yi Zhou', 'Jimena Guallar-Blasco', 'Elias Stengel-Eskin'] | 2022-11-14 | null | null | null | null | ['question-generation'] | ['natural-language-processing'] | [ 4.57358301e-01 9.87892807e-01 2.68890411e-01 -6.72718823e-01
-1.30572629e+00 -1.17864025e+00 7.10371494e-01 1.88584432e-01
-2.47210488e-01 6.71857953e-01 7.59814978e-01 -7.96851218e-01
-9.86643806e-02 -5.54473698e-01 -6.44843042e-01 2.89983362e-01
4.31883305e-01 5.93755066e-01 4.11161035e-01 -4.75685239... | [10.994869232177734, 1.759125828742981] |
b4e4190b-a6ee-47f0-97b0-d142154be4f5 | vision-based-lane-detection-and-tracking | 2210.10233 | null | https://arxiv.org/abs/2210.10233v3 | https://arxiv.org/pdf/2210.10233v3.pdf | Vision-Based Robust Lane Detection and Tracking under Different Challenging Environmental Conditions | Lane marking detection is fundamental for both advanced driving assistance systems. However, detecting lane is highly challenging when the visibility of a road lane marking is low due to real-life challenging environment and adverse weather. Most of the lane detection methods suffer from four types of challenges: (i) l... | ['Shamim Ahmad', 'Muhammad Rafiqul Islam', 'Manoranjan Paul', 'Boshir Ahmed', 'Samia Sultana'] | 2022-10-19 | null | null | null | null | ['lane-detection'] | ['computer-vision'] | [ 5.64104021e-02 -4.53554988e-01 1.81591272e-01 1.02847219e-02
-9.48394835e-02 -5.37311316e-01 4.00579870e-01 -1.34383619e-01
-4.54315573e-01 9.30173039e-01 -5.45856841e-02 -8.42462420e-01
2.86385119e-01 -6.58178508e-01 -3.47427547e-01 -7.83277392e-01
-1.13742407e-02 -3.19838941e-01 1.10639906e+00 -2.08116010... | [8.006423950195312, -1.3952820301055908] |
9ab8c092-bde5-4bce-9c1e-45829dfba7cb | two-souls-in-an-adversarial-image-towards | 2109.12459 | null | https://arxiv.org/abs/2109.12459v2 | https://arxiv.org/pdf/2109.12459v2.pdf | Two Souls in an Adversarial Image: Towards Universal Adversarial Example Detection using Multi-view Inconsistency | In the evasion attacks against deep neural networks (DNN), the attacker generates adversarial instances that are visually indistinguishable from benign samples and sends them to the target DNN to trigger misclassifications. In this paper, we propose a novel multi-view adversarial image detector, namely Argos, based on ... | ['Bo Luo', 'Fengjun Li', 'Chao Lan', 'Sana Awan', 'Sohaib Kiani'] | 2021-09-25 | null | null | null | null | ['adversarial-attack-detection', 'adversarial-attack-detection'] | ['computer-vision', 'knowledge-base'] | [ 6.17373765e-01 2.96021521e-01 3.53101254e-01 -1.71095468e-02
-5.38580954e-01 -1.20864749e+00 6.08609617e-01 -4.43921596e-01
-1.41389975e-02 5.77630758e-01 -3.20569724e-01 -2.11353526e-01
5.56686401e-01 -9.41371441e-01 -1.26900172e+00 -1.11003208e+00
1.47932306e-01 2.06982568e-01 1.04956694e-01 -1.26549199... | [5.569993019104004, 7.92805290222168] |
4b97bc0d-8cfc-4c6f-9f87-6673209efc00 | studying-the-control-of-non-invasive | 1511.06004 | null | http://arxiv.org/abs/1511.06004v1 | http://arxiv.org/pdf/1511.06004v1.pdf | Studying the control of non invasive prosthetic hands over large time spans | The electromyography (EMG) signal is the electrical manifestation of a
neuromuscular activation that provides access to physiological processes which
cause the muscle to generate force and produce movement. Non invasive
prostheses use such signals detected by the electrodes placed on the user's
stump, as input to gener... | ['Mara Graziani'] | 2015-11-18 | null | null | null | null | ['electromyography-emg'] | ['medical'] | [ 5.02145350e-01 1.70605138e-01 -3.06985646e-01 2.00947180e-01
4.69275266e-02 -4.20972437e-01 2.73506165e-01 -5.74646831e-01
-4.67640102e-01 1.02258122e+00 -9.63503495e-02 -1.10883944e-01
-2.70088285e-01 -3.77642125e-01 -4.95468318e-01 -6.17689490e-01
-1.90404341e-01 1.59381390e-01 2.99818162e-02 -7.21210837... | [6.874439239501953, 0.22244961559772491] |
3938254d-16b3-4dc5-a670-bf211bdfae84 | polarity-loss-for-zero-shot-object-detection | 1811.08982 | null | https://arxiv.org/abs/1811.08982v3 | https://arxiv.org/pdf/1811.08982v3.pdf | Polarity Loss for Zero-shot Object Detection | Conventional object detection models require large amounts of training data. In comparison, humans can recognize previously unseen objects by merely knowing their semantic description. To mimic similar behaviour, zero-shot object detection aims to recognize and localize 'unseen' object instances by using only their sem... | ['Nick Barnes', 'Shafin Rahman', 'Salman Khan'] | 2018-11-22 | null | null | null | null | ['zero-shot-object-detection'] | ['computer-vision'] | [ 2.89967448e-01 1.64227411e-01 -1.29255414e-01 -5.93841970e-01
-2.25867167e-01 -3.85847658e-01 8.74117136e-01 5.49200296e-01
-5.82348466e-01 3.13757300e-01 1.53194740e-01 1.39112353e-01
-3.25877443e-02 -8.49054098e-01 -6.84131742e-01 -6.62516236e-01
1.35173634e-01 2.47987345e-01 4.59385633e-01 -2.63344705... | [9.950551986694336, 2.015659809112549] |
29d6c760-7cba-48e6-9896-7bd83dce922d | task-agnostic-structured-pruning-of-speech | 2306.01385 | null | https://arxiv.org/abs/2306.01385v2 | https://arxiv.org/pdf/2306.01385v2.pdf | Task-Agnostic Structured Pruning of Speech Representation Models | Self-supervised pre-trained models such as Wav2vec2, Hubert, and WavLM have been shown to significantly improve many speech tasks. However, their large memory and strong computational requirements hinder their industrial applicability. Structured pruning is a hardware-friendly model compression technique but usually re... | ['Yulong Wan', 'Hongbin Suo', 'Wei-Qiang Zhang', 'Siyuan Wang', 'Haoyu Wang'] | 2023-06-02 | null | null | null | null | ['model-compression'] | ['methodology'] | [-1.09050088e-01 -1.59150381e-02 -5.85337937e-01 -4.66249406e-01
-7.85743356e-01 1.52466312e-01 3.76324326e-01 1.31055312e-02
-6.71372056e-01 6.37624860e-01 4.57383722e-01 -6.99295640e-01
2.71536082e-01 -5.69606066e-01 -5.70993304e-01 -4.79394197e-01
3.49136114e-01 5.59298337e-01 3.67918968e-01 -7.32703879... | [8.867088317871094, 3.608731508255005] |
13b6362b-0c25-4079-8fda-15f38a14f83c | alibaba-translate-china-s-submission-for-wmt-1 | 2210.10049 | null | https://arxiv.org/abs/2210.10049v2 | https://arxiv.org/pdf/2210.10049v2.pdf | Alibaba-Translate China's Submission for WMT 2022 Quality Estimation Shared Task | In this paper, we present our submission to the sentence-level MQM benchmark at Quality Estimation Shared Task, named UniTE (Unified Translation Evaluation). Specifically, our systems employ the framework of UniTE, which combined three types of input formats during training with a pre-trained language model. First, we ... | ['Jun Xie', 'Derek F. Wong', 'Xiangnan He', 'Wenqiang Lei', 'Baosong Yang', 'Dayiheng Liu', 'Yu Wan', 'Keqin Bao'] | 2022-10-18 | null | null | null | null | ['xlm-r'] | ['natural-language-processing'] | [ 9.77120176e-03 -2.99930602e-01 -2.38340795e-01 -6.97347879e-01
-1.78602970e+00 -6.88411295e-01 4.55932021e-01 2.42145315e-01
-8.02111208e-01 1.03556454e+00 5.18587649e-01 -5.38515389e-01
-4.23500165e-02 -4.70711410e-01 -6.50606096e-01 -5.52857071e-02
3.41743946e-01 7.43957818e-01 -1.59451142e-01 -7.46750474... | [11.644231796264648, 10.304625511169434] |
0a942087-8285-4efb-8923-c8f7d730ef89 | dataset-bias-in-the-natural-sciences-a-case | 2105.02637 | null | https://arxiv.org/abs/2105.02637v1 | https://arxiv.org/pdf/2105.02637v1.pdf | Dataset Bias in the Natural Sciences: A Case Study in Chemical Reaction Prediction and Synthesis Design | Datasets in the Natural Sciences are often curated with the goal of aiding scientific understanding and hence may not always be in a form that facilitates the application of machine learning. In this paper, we identify three trends within the fields of chemical reaction prediction and synthesis design that require a ch... | ['Alpha A. Lee', 'Philippe Schwaller', 'Ryan-Rhys Griffiths'] | 2021-05-06 | null | null | null | null | ['chemical-reaction-prediction', 'reagent-prediction'] | ['medical', 'medical'] | [ 6.48520112e-01 -1.69896930e-01 -2.15445578e-01 -2.45358154e-01
-3.90751570e-01 -7.34407842e-01 6.49157405e-01 7.14951575e-01
-5.22802949e-01 9.19432998e-01 2.54880428e-01 -8.45206559e-01
-2.91713700e-03 -5.78839958e-01 -7.67011881e-01 -9.27548230e-01
5.50304413e-01 2.51308471e-01 -2.35470936e-01 -5.38466461... | [4.92990779876709, 5.754281044006348] |
a69c3067-f6ad-48d2-a83e-ae0a8c85d52e | comprehensive-soccer-video-understanding | 1912.04465 | null | https://arxiv.org/abs/1912.04465v4 | https://arxiv.org/pdf/1912.04465v4.pdf | SoccerDB: A Large-Scale Database for Comprehensive Video Understanding | Soccer videos can serve as a perfect research object for video understanding because soccer games are played under well-defined rules while complex and intriguing enough for researchers to study. In this paper, we propose a new soccer video database named SoccerDB, comprising 171,191 video segments from 346 high-qualit... | ['Yudong Jiang', 'Leilei Chen', 'Canjin Wang', 'Changliang Xu', 'Kaixu Cui'] | 2019-12-10 | null | null | null | null | ['highlight-detection'] | ['computer-vision'] | [-2.35794038e-01 -4.98909473e-01 -7.52373397e-01 -6.09854646e-02
-8.48413944e-01 -6.39936924e-01 1.27172157e-01 -2.70467699e-01
-5.57788908e-01 6.38223171e-01 4.29381639e-01 2.02217221e-01
2.82251328e-01 -3.90983999e-01 -1.04095912e+00 -5.31096339e-01
-3.46421987e-01 2.09262982e-01 8.96214068e-01 -4.02290583... | [7.950480937957764, 0.2038504034280777] |
56f9997f-5125-429b-864c-807c407f31db | meta-learning-runge-kutta | null | null | https://openreview.net/forum?id=rkesVkHtDr | https://openreview.net/pdf?id=rkesVkHtDr | Meta-Learning Runge-Kutta | Initial value problems, i.e. differential equations with specific, initial conditions, represent a classic problem within the field of ordinary differential equations(ODEs). While the simplest types of ODEs may have closed-form solutions, most interesting cases typically rely on iterative schemes for numerical integrat... | ['Kristian Kersting', 'Patrick Schramowski', 'Nadine Behrmann'] | 2019-09-25 | null | null | null | null | ['numerical-integration'] | ['miscellaneous'] | [-3.61645132e-01 -2.00183704e-01 -1.69003487e-01 3.11153457e-02
-5.59960604e-01 -6.60445035e-01 2.73998886e-01 4.97764722e-02
-3.63759547e-01 1.00399899e+00 -3.77254635e-01 -5.42825639e-01
-3.60258460e-01 -4.81691480e-01 -7.58624256e-01 -1.05529213e+00
1.37848958e-01 1.46228522e-01 1.20600365e-01 -6.02620244... | [6.532041072845459, 3.4455363750457764] |
7793df3e-f9d9-4ec9-b19b-ad1347186669 | tune-your-place-recognition-self-supervised | 2203.04446 | null | https://arxiv.org/abs/2203.04446v3 | https://arxiv.org/pdf/2203.04446v3.pdf | Self-Supervised Domain Calibration and Uncertainty Estimation for Place Recognition | Visual place recognition techniques based on deep learning, which have imposed themselves as the state-of-the-art in recent years, do not generalize well to environments visually different from the training set. Thus, to achieve top performance, it is sometimes necessary to fine-tune the networks to the target environm... | ['Giovanni Beltrame', 'Pierre-Yves Lajoie'] | 2022-03-08 | null | null | null | null | ['visual-place-recognition'] | ['computer-vision'] | [-2.03131825e-01 -6.72093257e-02 -1.35480672e-01 -6.41438186e-01
-1.02351749e+00 -8.33458066e-01 4.50545341e-01 8.75964612e-02
-3.91249239e-01 7.76420414e-01 -1.95426181e-01 -2.68248707e-01
-2.87173893e-02 -6.38778925e-01 -1.19641221e+00 -4.24550295e-01
-7.75839388e-02 6.45533025e-01 2.53106743e-01 3.67110893... | [7.568046569824219, -2.083448648452759] |
4280234b-e56c-495c-8cc9-6445cf313e01 | a-multimodal-perceived-stress-classification | 2206.10846 | null | https://arxiv.org/abs/2206.10846v1 | https://arxiv.org/pdf/2206.10846v1.pdf | A Multimodal Perceived Stress Classification Framework using Wearable Physiological Sensors | Mental stress is a largely prevalent condition known to affect many people and could be a serious health concern. The quality of human life can be significantly improved if mental health is properly managed. Towards this, we propose a robust method for perceived stress classification, which is based on using multimodal... | ['Syed Muhammad Anwar', 'Aamir Arsalan', 'Muhammad Majid'] | 2022-06-22 | null | null | null | null | ['photoplethysmography-ppg'] | ['medical'] | [ 3.48604262e-01 -2.94969827e-01 3.29561122e-02 -4.25702870e-01
-2.89536625e-01 -1.32097498e-01 1.05231171e-02 6.00387812e-01
-5.25017738e-01 8.61168027e-01 1.11782879e-01 2.88897842e-01
-3.44716161e-01 -5.26246727e-01 1.37174368e-01 -8.37100208e-01
-3.42148483e-01 -4.97420162e-01 -1.39503211e-01 -2.85431087... | [13.517129898071289, 3.1150341033935547] |
f90fe6bf-60aa-4acc-8d0b-a40c4bdf9b67 | an-empirical-comparison-of-lm-based-question | 2305.17002 | null | https://arxiv.org/abs/2305.17002v1 | https://arxiv.org/pdf/2305.17002v1.pdf | An Empirical Comparison of LM-based Question and Answer Generation Methods | Question and answer generation (QAG) consists of generating a set of question-answer pairs given a context (e.g. a paragraph). This task has a variety of applications, such as data augmentation for question answering (QA) models, information retrieval and education. In this paper, we establish baselines with three diff... | ['Jose Camacho-Collados', 'Fernando Alva-Manchego', 'Asahi Ushio'] | 2023-05-26 | null | null | null | null | ['answer-generation'] | ['natural-language-processing'] | [ 5.56629658e-01 4.86957401e-01 2.10861564e-01 -5.34764946e-01
-1.55162668e+00 -9.08856332e-01 9.40562487e-01 1.80507049e-01
-1.95677280e-01 8.13233137e-01 4.05145377e-01 -9.48475957e-01
1.68600157e-01 -9.40228403e-01 -7.93712795e-01 2.47839792e-03
5.76120138e-01 8.24467063e-01 1.47352025e-01 -6.05936170... | [11.462923049926758, 8.217885971069336] |
56414871-6d83-4829-959a-7ab0825b641f | an-augmented-linear-mixing-model-to-address | 1810.12000 | null | http://arxiv.org/abs/1810.12000v1 | http://arxiv.org/pdf/1810.12000v1.pdf | An Augmented Linear Mixing Model to Address Spectral Variability for Hyperspectral Unmixing | Hyperspectral imagery collected from airborne or satellite sources inevitably
suffers from spectral variability, making it difficult for spectral unmixing to
accurately estimate abundance maps. The classical unmixing model, the linear
mixing model (LMM), generally fails to handle this sticky issue effectively. To
this ... | ['Xiao Xiang Zhu', 'Naoto Yokoya', 'Danfeng Hong', 'Jocelyn Chanussot'] | 2018-10-29 | null | null | null | null | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 5.41786075e-01 -8.02082062e-01 -3.37260999e-02 8.23360160e-02
-2.58390367e-01 -5.31823277e-01 5.85358739e-01 -1.30999461e-01
-8.76422673e-02 8.03385198e-01 2.89737713e-02 -8.81695375e-02
-2.72265643e-01 -8.46080244e-01 -4.47077751e-01 -1.36420763e+00
2.59790003e-01 2.69822359e-01 -3.48424971e-01 -2.43727699... | [10.105111122131348, -2.07995867729187] |
59acbfd7-a38b-4aa8-b419-1e28676eb2b7 | deep-shape-matching | 1709.03409 | null | http://arxiv.org/abs/1709.03409v2 | http://arxiv.org/pdf/1709.03409v2.pdf | Deep Shape Matching | We cast shape matching as metric learning with convolutional networks. We
break the end-to-end process of image representation into two parts. Firstly,
well established efficient methods are chosen to turn the images into edge
maps. Secondly, the network is trained with edge maps of landmark images, which
are automatic... | ['Ondřej Chum', 'Filip Radenović', 'Giorgos Tolias'] | 2017-09-11 | deep-shape-matching-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Filip_Radenovic_Deep_Shape_Matching_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Filip_Radenovic_Deep_Shape_Matching_ECCV_2018_paper.pdf | eccv-2018-9 | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 1.65093198e-01 -3.47741604e-01 -4.04147804e-01 -4.64002669e-01
-9.76390839e-01 -7.99658000e-01 1.06624460e+00 8.85644332e-02
-5.99131107e-01 2.20445663e-01 2.40083069e-01 1.14026949e-01
-1.82315022e-01 -6.66437745e-01 -7.49578476e-01 -3.37572008e-01
1.39180496e-01 7.36928701e-01 3.35977525e-01 -2.72952616... | [11.601428985595703, 0.5544795989990234] |
a0050d3d-93a5-4cda-9555-b1cd26e2eb96 | let-the-chart-spark-embedding-semantic | 2304.14630 | null | https://arxiv.org/abs/2304.14630v2 | https://arxiv.org/pdf/2304.14630v2.pdf | Let the Chart Spark: Embedding Semantic Context into Chart with Text-to-Image Generative Model | Pictorial visualization seamlessly integrates data and semantic context into visual representation, conveying complex information in a manner that is both engaging and informative. Extensive studies have been devoted to developing authoring tools to simplify the creation of pictorial visualizations. However, mainstream... | ['Wei Zeng', 'Yilin Ye', 'Yue Lin', 'Suizi Huang', 'Shishi Xiao'] | 2023-04-28 | null | null | null | null | ['text-guided-generation'] | ['computer-vision'] | [ 2.02991202e-01 -3.01560443e-02 2.69121081e-01 -3.31516981e-01
-1.26587972e-01 -7.36562252e-01 1.05888402e+00 4.52741861e-01
6.48527453e-03 4.69483376e-01 6.41179740e-01 -6.31698668e-01
-1.93356292e-03 -8.27298522e-01 -2.29578450e-01 -2.72691876e-01
3.23862791e-01 2.71116998e-02 1.38037741e-01 -2.08339840... | [11.2767333984375, 1.8061137199401855] |
8fe59320-9736-47a9-99c3-7fd16f67f859 | hitrans-a-hierarchical-transformer-network | null | null | https://aclanthology.org/2021.findings-emnlp.12 | https://aclanthology.org/2021.findings-emnlp.12.pdf | HiTRANS: A Hierarchical Transformer Network for Nested Named Entity Recognition | Nested Named Entity Recognition (NNER) has been extensively studied, aiming to identify all nested entities from potential spans (i.e., one or more continuous tokens). However, recent studies for NNER either focus on tedious tagging schemas or utilize complex structures, which fail to learn effective span representatio... | ['Yi Chang', 'Yunke Zhang', 'Hechang Chen', 'Jing Ma', 'Zhiwei Yang'] | null | null | null | null | findings-emnlp-2021-11 | ['nested-named-entity-recognition'] | ['natural-language-processing'] | [ 9.16050449e-02 2.15022638e-01 -1.84616834e-01 -4.46114510e-01
-7.98827946e-01 -5.47164142e-01 1.83433324e-01 2.67379671e-01
-3.00914288e-01 8.18257630e-01 7.74550736e-01 -1.40778765e-01
1.24068938e-01 -1.01458943e+00 -5.44128478e-01 -2.44577676e-01
-3.84676792e-02 1.84046760e-01 3.20030272e-01 8.07056483... | [9.588216781616211, 9.387736320495605] |
41f6f345-5fdf-47fc-93d5-c2b822789f1e | improving-expressivity-of-graph-neural-1 | 2305.19659 | null | https://arxiv.org/abs/2305.19659v1 | https://arxiv.org/pdf/2305.19659v1.pdf | Improving Expressivity of Graph Neural Networks using Localization | In this paper, we propose localized versions of Weisfeiler-Leman (WL) algorithms in an effort to both increase the expressivity, as well as decrease the computational overhead. We focus on the specific problem of subgraph counting and give localized versions of $k-$WL for any $k$. We analyze the power of Local $k-$WL a... | ['Anirban Dasgupta', 'Binita Maity', 'Shubhajit Roy', 'Shrutimoy Das', 'Anant Kumar'] | 2023-05-31 | null | null | null | null | ['subgraph-counting'] | ['graphs'] | [ 8.01444575e-02 3.25002968e-01 -2.38881946e-01 -2.14008093e-02
-5.08001387e-01 -7.94946790e-01 -1.41341746e-01 3.22624534e-01
-5.35492361e-01 7.53200531e-01 -1.47233009e-01 -5.70606232e-01
-5.50417483e-01 -1.49514306e+00 -7.91566610e-01 -4.26603585e-01
-7.45514452e-01 4.37311143e-01 8.89812887e-01 -2.55504251... | [6.892168045043945, 5.204005718231201] |
e3a27ea7-8295-48cc-bcff-cfe837b215ef | local-discriminant-hyperalignment-for-multi | 1611.08366 | null | http://arxiv.org/abs/1611.08366v1 | http://arxiv.org/pdf/1611.08366v1.pdf | Local Discriminant Hyperalignment for multi-subject fMRI data alignment | Multivariate Pattern (MVP) classification can map different cognitive states
to the brain tasks. One of the main challenges in MVP analysis is validating
the generated results across subjects. However, analyzing multi-subject fMRI
data requires accurate functional alignments between neuronal activities of
different sub... | ['Muhammad Yousefnezhad', 'Daoqiang Zhang'] | 2016-11-25 | null | null | null | null | ['multi-subject-fmri-data-alignment'] | ['medical'] | [-2.19100546e-02 -8.02633882e-01 1.15981199e-01 -4.24677432e-01
-4.68482196e-01 -4.61275578e-01 5.88502526e-01 -5.80604114e-02
-4.10742670e-01 6.02836490e-01 2.60195136e-01 1.86096489e-01
-3.97976577e-01 -3.35616410e-01 -3.36555600e-01 -1.23955727e+00
-1.62389681e-01 1.47480145e-01 1.85881242e-01 7.20568895... | [12.658282279968262, 3.3936026096343994] |
58c195a5-d7ad-43c1-82f4-9352c21e8e83 | what-you-see-is-what-you-read-improving-text | 2305.10400 | null | https://arxiv.org/abs/2305.10400v2 | https://arxiv.org/pdf/2305.10400v2.pdf | What You See is What You Read? Improving Text-Image Alignment Evaluation | Automatically determining whether a text and a corresponding image are semantically aligned is a significant challenge for vision-language models, with applications in generative text-to-image and image-to-text tasks. In this work, we study methods for automatic text-image alignment evaluation. We first introduce SeeTR... | ['Idan Szpektor', 'Eran Ofek', 'Oran Lang', 'Jonathan Herzig', 'Roee Aharoni', 'Soravit Changpinyo', 'Yonatan Bitton', 'Michal Yarom'] | 2023-05-17 | null | null | null | null | ['visual-reasoning', 'question-generation', 'visual-reasoning'] | ['computer-vision', 'natural-language-processing', 'reasoning'] | [ 8.88431311e-01 1.21363215e-01 2.99969465e-01 -6.25013709e-01
-1.44653881e+00 -8.43823016e-01 1.14616334e+00 1.74513429e-01
-3.69527042e-01 3.21723342e-01 2.88902730e-01 -2.58296549e-01
4.67887133e-01 -3.35369080e-01 -9.18115616e-01 -4.29782182e-01
6.91899717e-01 9.84558344e-01 2.29413211e-01 -1.17002651... | [10.99161434173584, 1.3196877241134644] |
80864d9c-f6a2-428f-99bc-e2d23c351b70 | atm-fraud-detection-using-streaming-data | 2303.04946 | null | https://arxiv.org/abs/2303.04946v1 | https://arxiv.org/pdf/2303.04946v1.pdf | ATM Fraud Detection using Streaming Data Analytics | Gaining the trust and confidence of customers is the essence of the growth and success of financial institutions and organizations. Of late, the financial industry is significantly impacted by numerous instances of fraudulent activities. Further, owing to the generation of large voluminous datasets, it is highly essent... | ['Laveti Ramesh Naidu', 'Abhay Anand Mane', 'Vadlamani Ravi', 'Yelleti Vivek'] | 2023-03-08 | null | null | null | null | ['fraud-detection'] | ['miscellaneous'] | [-6.72903359e-02 -4.07075703e-01 1.04277700e-01 -3.34706694e-01
-4.97751117e-01 -4.59461123e-01 3.12509656e-01 4.14031625e-01
-4.09013361e-01 1.07724881e+00 1.42292948e-02 -5.65189302e-01
-5.73607087e-02 -1.05196023e+00 -4.93806809e-01 -4.50116009e-01
-1.67185869e-02 5.41426003e-01 3.58436882e-01 -2.79557616... | [7.951199531555176, 5.052997589111328] |
3c3f0b9f-8016-4bef-994b-e804cd79eeb9 | investigating-language-relationships-in | null | null | https://aclanthology.org/2022.cl-3.5 | https://aclanthology.org/2022.cl-3.5.pdf | Investigating Language Relationships in Multilingual Sentence Encoders Through the Lens of Linguistic Typology | Multilingual sentence encoders have seen much success in cross-lingual model transfer for downstream NLP tasks. The success of this transfer is, however, dependent on the model’s ability to encode the patterns of cross-lingual similarity and variation. Yet, we know relatively little about the properties of individual l... | ['Ekaterina Shutova', 'Rochelle Choenni'] | null | null | null | null | cl-acl-2022-9 | ['xlm-r'] | ['natural-language-processing'] | [-3.60517681e-01 -2.21220538e-01 -4.82569367e-01 -5.38952649e-01
-7.05046475e-01 -8.85444701e-01 6.51995242e-01 2.57895559e-01
-6.53767765e-01 6.39946878e-01 7.63460338e-01 -4.29729193e-01
3.05267386e-02 -6.49345458e-01 -1.02512860e+00 -3.09097975e-01
6.11975417e-02 6.61555409e-01 -1.88096762e-01 -4.44448650... | [10.854185104370117, 9.91356086730957] |
af1d2f97-9525-4cef-beda-7613b9dc1403 | parallel-computation-of-pdfs-on-big-spatial | 1805.03141 | null | http://arxiv.org/abs/1805.03141v1 | http://arxiv.org/pdf/1805.03141v1.pdf | Parallel Computation of PDFs on Big Spatial Data Using Spark | We consider big spatial data, which is typically produced in scientific areas
such as geological or seismic interpretation. The spatial data can be produced
by observation (e.g. using sensors or soil instrument) or numerical simulation
programs and correspond to points that represent a 3D soil cube area. However,
error... | ['Patrick Valduriez', 'Esther Pacitti', 'Noel Moreno Lemus', 'Ji Liu', 'Fabio Porto'] | 2018-05-08 | null | null | null | null | ['seismic-interpretation'] | ['miscellaneous'] | [-2.04088166e-01 -2.08203822e-01 5.79748750e-01 -1.26395285e-01
-9.92769957e-01 -5.37615597e-01 3.82893652e-01 6.88548326e-01
-3.87956440e-01 9.16681349e-01 2.27327663e-02 -4.78590637e-01
-1.85568556e-01 -1.56076682e+00 -9.65640068e-01 -8.44278336e-01
-4.71235693e-01 7.34224617e-01 7.67965853e-01 1.72834828... | [6.993375778198242, 3.9449167251586914] |
5eccdbab-eff3-4b92-bb61-9b107bbb02b7 | multi-fidelity-active-learning-with-gflownets | 2306.11715 | null | https://arxiv.org/abs/2306.11715v1 | https://arxiv.org/pdf/2306.11715v1.pdf | Multi-Fidelity Active Learning with GFlowNets | In the last decades, the capacity to generate large amounts of data in science and engineering applications has been growing steadily. Meanwhile, the progress in machine learning has turned it into a suitable tool to process and utilise the available data. Nonetheless, many relevant scientific and engineering problems ... | ['Yoshua Bengio', 'Cheng-Hao Liu', 'Moksh Jain', 'Nikita Saxena', 'Alex Hernandez-Garcia'] | 2023-06-20 | null | null | null | null | ['active-learning', 'active-learning'] | ['methodology', 'natural-language-processing'] | [ 3.41733336e-01 -1.05275497e-01 -3.86103094e-01 4.63311467e-03
-1.40820527e+00 -6.27323031e-01 6.82234824e-01 6.46133125e-01
-8.19852471e-01 1.36804712e+00 -3.73846769e-01 -4.22801763e-01
-6.49691343e-01 -9.23043668e-01 -1.10625732e+00 -1.10910463e+00
-2.93923408e-01 1.05996442e+00 9.16180164e-02 3.03920984... | [5.2215423583984375, 5.278721332550049] |
a4ee5041-960c-478a-8412-820e011471fc | ngram-lstm-open-rate-prediction-model-nlorp | 2302.00651 | null | https://arxiv.org/abs/2302.00651v2 | https://arxiv.org/pdf/2302.00651v2.pdf | Ngram-LSTM Open Rate Prediction Model (NLORP) and Error_accuracy@C metric: Simple effective, and easy to implement approach to predict open rates for marketing email | Our generation has seen an exponential increase in digital tools adoption. One of the unique areas where digital tools have made an exponential foray is in the sphere of digital marketing, where goods and services have been extensively promoted through the use of digital advertisements. Following this growth, multiple ... | ['Indradumna Banerjee', 'Shubham Joshi'] | 2023-01-25 | null | null | null | null | ['marketing'] | ['miscellaneous'] | [ 2.75185704e-01 1.46252820e-02 -7.03386843e-01 -5.32763004e-01
-6.14658594e-01 -4.81107116e-01 7.57971883e-01 3.24598074e-01
-3.13137889e-01 4.75168824e-01 -2.05542683e-03 -4.34452325e-01
4.97478154e-03 -1.11226451e+00 -6.62969530e-01 -3.86680178e-02
4.63707857e-02 6.29093409e-01 2.48216927e-01 -3.37514997... | [9.923748016357422, 5.943975925445557] |
37e44ec1-a5f7-4f60-8930-177905ca0a81 | learning-parameters-for-balanced-index | 2012.08067 | null | https://arxiv.org/abs/2012.08067v1 | https://arxiv.org/pdf/2012.08067v1.pdf | Learning Parameters for Balanced Index Influence Maximization | Influence maximization is the task of finding the smallest set of nodes whose activation in a social network can trigger an activation cascade that reaches the targeted network coverage, where threshold rules determine the outcome of influence. This problem is NP-hard and it has generated a significant amount of recent... | ['Boleslaw K. Szymanski', 'Gyorgy Korniss', 'Manqing Ma'] | 2020-12-15 | null | null | null | null | ['graph-sampling'] | ['graphs'] | [ 2.79869556e-01 4.58844990e-01 -5.65929174e-01 -8.79744962e-02
-4.12729353e-01 -7.76215792e-01 7.56656766e-01 9.54421088e-02
-2.32094169e-01 8.64202797e-01 -1.14412848e-02 -3.35984945e-01
-5.92636228e-01 -1.22943866e+00 -6.22233748e-01 -7.07344115e-01
-6.53115034e-01 1.01732874e+00 4.82391238e-01 -2.10872248... | [6.927389621734619, 5.40385627746582] |
76724824-14e6-4f89-bc72-44bf856c1545 | about-evaluation-of-f1-score-for-recent | 2305.09410 | null | https://arxiv.org/abs/2305.09410v1 | https://arxiv.org/pdf/2305.09410v1.pdf | About Evaluation of F1 Score for RECENT Relation Extraction System | This document contains a discussion of the F1 score evaluation used in the article 'Relation Classification with Entity Type Restriction' by Shengfei Lyu, Huanhuan Chen published on Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021. The authors created a system named RECENT and claim it achieve... | ['Michał Olek'] | 2023-05-16 | null | null | null | null | ['relation-extraction', 'relation-classification'] | ['natural-language-processing', 'natural-language-processing'] | [-1.39064878e-01 6.66560948e-01 -6.53646052e-01 -4.29754555e-01
-6.37491405e-01 -2.75032282e-01 6.51694596e-01 5.92722833e-01
-9.18724179e-01 1.14646375e+00 4.33206260e-01 -4.48623627e-01
-4.05014008e-01 -5.23236990e-01 -4.81864512e-01 -8.09855089e-02
3.52645181e-02 5.83863974e-01 2.35249132e-01 -4.08532798... | [9.323450088500977, 8.891791343688965] |
8e373a9f-f5e3-41d8-bc3b-e4e96d43cc4f | 3d-graph-embedding-learning-with-a-structure | 1902.05247 | null | http://arxiv.org/abs/1902.05247v1 | http://arxiv.org/pdf/1902.05247v1.pdf | 3D Graph Embedding Learning with a Structure-aware Loss Function for Point Cloud Semantic Instance Segmentation | This paper introduces a novel approach for 3D semantic instance segmentation
on point clouds. A 3D convolutional neural network called submanifold sparse
convolutional network is used to generate semantic predictions and instance
embeddings simultaneously. To obtain discriminative embeddings for each 3D
instance, a str... | ['Ming Yang', 'Chunxiang Wang', 'Zhidong Liang'] | 2019-02-14 | null | null | null | null | ['3d-instance-segmentation-1', '3d-semantic-instance-segmentation'] | ['computer-vision', 'computer-vision'] | [-5.00177816e-02 3.01784933e-01 -1.91398934e-01 -6.68707252e-01
-4.50053573e-01 -5.59396595e-02 3.47303271e-01 3.79706062e-02
-7.95062408e-02 6.05958626e-02 1.53640389e-01 -9.62562039e-02
-2.22692892e-01 -8.86342525e-01 -9.07268465e-01 -3.55215073e-01
-8.45839083e-03 6.67260826e-01 4.52665627e-01 1.40801281... | [7.978462219238281, -3.208561897277832] |
6a11376a-fbcd-407f-9804-34e9ce2ec92d | learning-to-detect-adversarial-examples-based | 2107.04435 | null | https://arxiv.org/abs/2107.04435v1 | https://arxiv.org/pdf/2107.04435v1.pdf | Learning to Detect Adversarial Examples Based on Class Scores | Given the increasing threat of adversarial attacks on deep neural networks (DNNs), research on efficient detection methods is more important than ever. In this work, we take a closer look at adversarial attack detection based on the class scores of an already trained classification model. We propose to train a support ... | ['Oliver De Candido', 'Felix Michels', 'Tobias Uelwer'] | 2021-07-09 | null | null | null | null | ['adversarial-attack-detection', 'adversarial-attack-detection'] | ['computer-vision', 'knowledge-base'] | [ 4.43537891e-01 -2.75127012e-02 3.40569079e-01 -1.05478592e-01
-5.50511062e-01 -1.14573216e+00 9.74375010e-01 2.15784147e-01
-5.61392128e-01 5.72800756e-01 -3.21085632e-01 -6.01609707e-01
1.07257627e-01 -9.79106545e-01 -8.11880231e-01 -7.32404292e-01
-2.76600510e-01 1.60890639e-01 6.00633979e-01 -4.03564990... | [5.640776634216309, 7.772590637207031] |
45bd795e-e359-4d91-87c0-170ee6a9fefe | variational-quantum-regression-algorithm-with | 2307.03334 | null | https://arxiv.org/abs/2307.03334v1 | https://arxiv.org/pdf/2307.03334v1.pdf | Variational quantum regression algorithm with encoded data structure | Variational quantum algorithms (VQAs) prevail to solve practical problems such as combinatorial optimization, quantum chemistry simulation, quantum machine learning, and quantum error correction on noisy quantum computers. For variational quantum machine learning, a variational algorithm with model interpretability bui... | ['Ryan S. Bennink', 'C. -C. Joseph Wang'] | 2023-07-07 | null | null | null | null | ['combinatorial-optimization'] | ['methodology'] | [ 5.56207478e-01 1.65829390e-01 -5.93712479e-02 -2.09847689e-01
-9.31814194e-01 -3.97824049e-01 4.32218015e-01 3.51234138e-01
-7.06174850e-01 9.73607957e-01 -5.45617521e-01 -4.77615446e-01
-1.95508540e-01 -1.15896952e+00 -7.56844997e-01 -1.27115989e+00
4.86897901e-02 5.87530255e-01 -1.51741326e-01 -6.50118589... | [5.567449569702148, 4.952250957489014] |
989b73e7-14d4-4a4d-94f9-dd23036b15b9 | transfer-learning-from-high-resource-to-low | 2103.11764 | null | https://arxiv.org/abs/2103.11764v1 | https://arxiv.org/pdf/2103.11764v1.pdf | Transfer learning from High-Resource to Low-Resource Language Improves Speech Affect Recognition Classification Accuracy | Speech Affect Recognition is a problem of extracting emotional affects from audio data. Low resource languages corpora are rear and affect recognition is a difficult task in cross-corpus settings. We present an approach in which the model is trained on high resource language and fine-tune to recognize affects in low re... | ['Umair Arshad', 'Sara Durrani'] | 2021-03-04 | null | null | null | null | ['cross-corpus'] | ['computer-vision'] | [-2.72240430e-01 4.96494919e-02 8.74871686e-02 -4.91112828e-01
-1.19833958e+00 -6.94645166e-01 3.87832046e-01 -2.78986961e-01
-5.39265573e-01 8.21447730e-01 6.02074444e-01 1.82001680e-01
7.03333139e-01 -1.44544825e-01 -1.20525032e-01 -3.13430756e-01
1.71872690e-01 -2.73681898e-03 -4.33730572e-01 -4.37355161... | [13.567543029785156, 5.8486223220825195] |
fd2f669f-c76b-4f96-9f51-a131b1505e09 | meet-spinky-an-open-source-spindle-and-k | null | null | https://doi.org/10.3389/fninf.2017.00015 | https://www.frontiersin.org/articles/10.3389/fninf.2017.00015/pdf | Meet Spinky: An Open-Source Spindle and K-Complex Detection Toolbox Validated on the Open-Access Montreal Archive of Sleep Studies (MASS). | Sleep spindles and K-complexes are among the most prominent micro-events observed in electroencephalographic (EEG) recordings during sleep. These EEG microstructures are thought to be hallmarks of sleep-related cognitive processes. Although tedious and time-consuming, their identification and quantification is importan... | ['Sonia Frenette', 'Pierre-Emanuel Aguera', 'Sahbi Ch1aibi', "Christian O'Reilly", 'Karim Jerbi', 'Jb Eichenlaub', 'Etienne Combrisson', 'Abdennaceur Kachouri', 'Perrine Ruby', 'Mounir Samet', 'Julie Carrier', 'Tarek Lajnef'] | 2017-03-02 | null | null | null | frontiers-in-neuroinformatics-2017-3 | ['k-complex-detection', 'spindle-detection'] | ['medical', 'medical'] | [ 7.13720694e-02 -2.41593316e-01 1.94738984e-01 -1.40299246e-01
-5.62826693e-01 -5.90131283e-01 3.77244383e-01 5.96587598e-01
-7.34214723e-01 9.14307535e-01 -5.50489016e-02 -1.20127246e-01
-5.59018552e-01 -3.82850587e-01 -1.52986282e-02 -7.77373672e-01
-4.18398082e-01 1.31719783e-01 2.79358298e-01 5.02869263... | [13.407393455505371, 3.443270444869995] |
20778b43-335f-4731-8b35-4d0687ef5fbf | cross-modal-data-discovery-over-structured | 2306.00932 | null | https://arxiv.org/abs/2306.00932v2 | https://arxiv.org/pdf/2306.00932v2.pdf | Cross Modal Data Discovery over Structured and Unstructured Data Lakes | Organizations are collecting increasingly large amounts of data for data driven decision making. These data are often dumped into a centralized repository, e.g., a data lake, consisting of thousands of structured and unstructured datasets. Perversely, such mixture of datasets makes the problem of discovering elements (... | ['Mohammad Shahmeer Ahmad', 'Ahmed Elmagarmid', 'Mayuresh Kunjir', 'Mohamed Y. Eltabakh'] | 2023-06-01 | null | null | null | null | ['data-integration'] | ['knowledge-base'] | [-1.47475049e-01 -2.14283884e-01 -2.32526705e-01 -3.18454534e-01
-4.93855059e-01 -5.82525253e-01 3.87572289e-01 1.08984506e+00
-1.84190914e-01 6.20803773e-01 4.64898556e-01 -1.79380029e-01
-7.02231050e-01 -1.20375049e+00 -1.43510669e-01 -3.65789622e-01
-6.66950345e-02 7.19018340e-01 3.77791107e-01 -1.70606703... | [9.199874877929688, 7.899125576019287] |
af490730-29d0-4988-8949-aa867af63d1f | visual-question-answering-based-on-local | 2101.08978 | null | https://arxiv.org/abs/2101.08978v1 | https://arxiv.org/pdf/2101.08978v1.pdf | Visual Question Answering based on Local-Scene-Aware Referring Expression Generation | Visual question answering requires a deep understanding of both images and natural language. However, most methods mainly focus on visual concept; such as the relationships between various objects. The limited use of object categories combined with their relationships or simple question embedding is insufficient for re... | ['Seong-Whan Lee', 'Hong-Gyu Jung', 'Jialin Wu', 'Dong-Gyu Lee', 'Jung-Jun Kim'] | 2021-01-22 | null | null | null | null | ['referring-expression-generation'] | ['computer-vision'] | [-2.09263489e-01 1.50327617e-02 5.23123294e-02 -6.03404403e-01
-5.51811278e-01 -4.67385203e-01 7.22360969e-01 1.68556303e-01
-4.29725736e-01 5.20014048e-01 4.47675407e-01 -1.26537085e-01
2.02021748e-01 -5.50005138e-01 -6.73461616e-01 -4.03823018e-01
5.14779508e-01 2.73748428e-01 2.74005979e-01 -2.00698435... | [10.715779304504395, 1.71048903465271] |
6974f377-aaf8-4197-8173-5d238560f9a6 | classify-or-select-neural-architectures-for | 1611.04244 | null | http://arxiv.org/abs/1611.04244v1 | http://arxiv.org/pdf/1611.04244v1.pdf | Classify or Select: Neural Architectures for Extractive Document Summarization | We present two novel and contrasting Recurrent Neural Network (RNN) based
architectures for extractive summarization of documents. The Classifier based
architecture sequentially accepts or rejects each sentence in the original
document order for its membership in the final summary. The Selector
architecture, on the oth... | ['Bo-Wen Zhou', 'Ramesh Nallapati', 'Mingbo Ma'] | 2016-11-14 | null | null | null | null | ['extractive-document-summarization'] | ['natural-language-processing'] | [ 5.91771305e-01 4.62680876e-01 -2.84253478e-01 -3.60096574e-01
-5.91584027e-01 -5.84789872e-01 8.18893790e-01 6.77154839e-01
-4.02101845e-01 5.89600265e-01 9.20494378e-01 -4.93887812e-01
-2.57545233e-01 -4.67584461e-01 -3.24942708e-01 -5.35987079e-01
-1.27958506e-01 4.69469130e-01 -1.17621897e-02 -2.96140552... | [12.447826385498047, 9.473484992980957] |
87914cb4-af05-4938-a7b2-dc64f4c51296 | semantics-enhanced-task-oriented-dialogue | null | null | https://aclanthology.org/I17-3009 | https://aclanthology.org/I17-3009.pdf | Semantics-Enhanced Task-Oriented Dialogue Translation: A Case Study on Hotel Booking | We showcase TODAY, a semantics-enhanced task-oriented dialogue translation system, whose novelties are: (i) task-oriented named entity (NE) definition and a hybrid strategy for NE recognition and translation; and (ii) a novel grounded semantic method for dialogue understanding and task-order management. TODAY is a case... | ['Long-Yue Wang', 'Qun Liu', 'Zhaopeng Tu', 'Liangyou Li', 'Andy Way', 'Jinhua Du'] | 2017-11-01 | semantics-enhanced-task-oriented-dialogue-1 | https://aclanthology.org/I17-3009 | https://aclanthology.org/I17-3009.pdf | ijcnlp-2017-11 | ['dialogue-understanding'] | ['natural-language-processing'] | [-4.01828066e-02 6.11314058e-01 2.66772121e-01 -6.52357101e-01
-8.69695485e-01 -8.10051441e-01 1.04857266e+00 6.22956865e-02
-5.79778612e-01 1.41869009e+00 5.91950297e-01 -4.10315394e-01
-1.09412640e-01 -3.37473661e-01 2.26273537e-01 1.33150578e-01
2.76304960e-01 1.61931169e+00 1.56178847e-01 -1.33285964... | [12.719876289367676, 8.088706970214844] |
a1baa2d5-7d0f-4c27-a90b-00a79de577ca | a-dataset-for-starcraft-ai-an-example-of | 1211.4552 | null | http://arxiv.org/abs/1211.4552v1 | http://arxiv.org/pdf/1211.4552v1.pdf | A Dataset for StarCraft AI \& an Example of Armies Clustering | This paper advocates the exploration of the full state of recorded real-time
strategy (RTS) games, by human or robotic players, to discover how to reason
about tactics and strategy. We present a dataset of StarCraft games
encompassing the most of the games' state (not only player's orders). We
explain one of the possib... | ['Gabriel Synnaeve', 'Pierre Bessiere'] | 2012-11-19 | null | null | null | null | ['real-time-strategy-games'] | ['playing-games'] | [-2.65560776e-01 2.85437554e-01 -3.07723135e-02 -1.38046354e-01
-6.47919029e-02 -1.42296004e+00 1.07577765e+00 -2.83114374e-01
-2.50595421e-01 4.06961590e-01 5.71120620e-01 -6.72775507e-01
-8.24126601e-01 -5.80453277e-01 -7.05237165e-02 -5.76799691e-01
-2.59075135e-01 1.42282140e+00 6.19163871e-01 -9.20239806... | [3.4836955070495605, 1.4696345329284668] |
5a1b5279-cd54-4b76-886a-7e01aef57f79 | deepfit-3d-surface-fitting-via-neural-network | 2003.10826 | null | https://arxiv.org/abs/2003.10826v1 | https://arxiv.org/pdf/2003.10826v1.pdf | DeepFit: 3D Surface Fitting via Neural Network Weighted Least Squares | We propose a surface fitting method for unstructured 3D point clouds. This method, called DeepFit, incorporates a neural network to learn point-wise weights for weighted least squares polynomial surface fitting. The learned weights act as a soft selection for the neighborhood of surface points thus avoiding the scale s... | ['Yizhak Ben-Shabat', 'Stephen Gould'] | 2020-03-23 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/283_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123460018.pdf | eccv-2020-8 | ['surface-normals-estimation'] | ['computer-vision'] | [ 1.11117221e-01 -2.17761821e-03 1.42467529e-01 -2.92755067e-01
-7.64051437e-01 -1.78744838e-01 3.28398705e-01 7.54431114e-02
-3.58733505e-01 1.70646384e-01 -3.84959430e-01 -5.85673861e-02
-5.67016006e-02 -8.07628989e-01 -9.80238497e-01 -5.73181868e-01
-2.47529224e-01 6.55875742e-01 3.87448519e-01 -2.48729438... | [8.201544761657715, -3.4246022701263428] |
83f72902-e811-4d7f-b33d-fd08114810ae | context-encoding-for-semantic-segmentation | 1803.08904 | null | http://arxiv.org/abs/1803.08904v1 | http://arxiv.org/pdf/1803.08904v1.pdf | Context Encoding for Semantic Segmentation | Recent work has made significant progress in improving spatial resolution for
pixelwise labeling with Fully Convolutional Network (FCN) framework by
employing Dilated/Atrous convolution, utilizing multi-scale features and
refining boundaries. In this paper, we explore the impact of global contextual
information in sema... | ['Ambrish Tyagi', 'Hang Zhang', 'Amit Agrawal', 'Zhongyue Zhang', 'Kristin Dana', 'Xiaogang Wang', 'Jianping Shi'] | 2018-03-23 | context-encoding-for-semantic-segmentation-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Zhang_Context_Encoding_for_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Zhang_Context_Encoding_for_CVPR_2018_paper.pdf | cvpr-2018-6 | ['thermal-image-segmentation'] | ['computer-vision'] | [ 3.45696479e-01 5.51463850e-02 -6.94340467e-02 -5.60115635e-01
-7.78652847e-01 -5.06212533e-01 3.51594359e-01 -1.42628282e-01
-9.32139099e-01 6.53702319e-01 -6.46589249e-02 -1.48476645e-01
1.18826397e-01 -6.92807794e-01 -8.98729682e-01 -5.43498576e-01
-7.23755583e-02 6.94702193e-02 5.11819601e-01 -8.29190761... | [9.545992851257324, 0.28491559624671936] |
8e63f22d-b7ca-4e9b-b01b-dbc871aace51 | cd-fsod-a-benchmark-for-cross-domain-few-shot | 2210.05311 | null | https://arxiv.org/abs/2210.05311v3 | https://arxiv.org/pdf/2210.05311v3.pdf | CD-FSOD: A Benchmark for Cross-domain Few-shot Object Detection | In this paper, we propose a study of the cross-domain few-shot object detection (CD-FSOD) benchmark, consisting of image data from a diverse data domain. On the proposed benchmark, we evaluate state-of-art FSOD approaches, including meta-learning FSOD approaches and fine-tuning FSOD approaches. The results show that th... | ['Wuti Xiong'] | 2022-10-11 | null | null | null | null | ['cross-domain-few-shot'] | ['computer-vision'] | [ 1.52821332e-01 -2.43008822e-01 -3.26053768e-01 -1.51993826e-01
-1.08999348e+00 -4.83466089e-01 7.94231892e-01 -4.09705400e-01
-4.04988140e-01 6.12604141e-01 2.03566760e-01 1.30069092e-01
-1.66515335e-02 -5.47379375e-01 -5.51463902e-01 -4.71043915e-01
2.93006748e-01 3.51441830e-01 9.82623875e-01 -1.75555393... | [9.852492332458496, 2.246732234954834] |
363f7a70-5929-4aa0-992f-6e3c29d7dca9 | codexglue-a-machine-learning-benchmark | 2102.04664 | null | https://arxiv.org/abs/2102.04664v2 | https://arxiv.org/pdf/2102.04664v2.pdf | CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation | Benchmark datasets have a significant impact on accelerating research in programming language tasks. In this paper, we introduce CodeXGLUE, a benchmark dataset to foster machine learning research for program understanding and generation. CodeXGLUE includes a collection of 10 tasks across 14 datasets and a platform for ... | ['Shujie Liu', 'Shengyu Fu', 'Shao Kun Deng', 'Neel Sundaresan', 'Nan Duan', 'Ming Zhou', 'Ming Gong', 'Michele Tufano', 'Long Zhou', 'Linjun Shou', 'Lidong Zhou', 'Ge Li', 'Duyu Tang', 'Daxin Jiang', 'Dawn Drain', 'Colin Clement', 'Ambrosio Blanco', 'Alexey Svyatkovskiy', 'JunJie Huang', 'Shuo Ren', 'Daya Guo', 'Shuai... | 2021-02-09 | null | null | null | null | ['code-translation', 'text-to-code-generation', 'code-search', 'code-search', 'cloze-test'] | ['computer-code', 'computer-code', 'computer-code', 'computer-vision', 'natural-language-processing'] | [-6.68363869e-02 -2.65296161e-01 -6.54597759e-01 -7.74810314e-01
-7.07821906e-01 -5.14096320e-01 4.90598351e-01 2.04189032e-01
-2.09246632e-02 3.38040888e-01 1.71631038e-01 -9.27461505e-01
6.74214363e-01 -1.01591969e+00 -9.47685361e-01 9.86658931e-02
-7.97975361e-02 1.48413882e-01 1.09532125e-01 -4.46398854... | [7.79059362411499, 7.813659191131592] |
762c9a35-caf5-416d-8134-0edda1460960 | openapmax-abnormal-patterns-based-model-for | 2307.00936 | null | https://arxiv.org/abs/2307.00936v1 | https://arxiv.org/pdf/2307.00936v1.pdf | OpenAPMax: Abnormal Patterns-based Model for Real-World Alzheimer's Disease Diagnosis | Alzheimer's disease (AD) cannot be reversed, but early diagnosis will significantly benefit patients' medical treatment and care. In recent works, AD diagnosis has the primary assumption that all categories are known a prior -- a closed-set classification problem, which contrasts with the open-set recognition problem. ... | ['Jianfeng Zhan', 'Zhifei Zhang', 'Suqin Tang', 'Li Ma', 'Wenjing Liu', 'Jiyue Xie', 'Xiuxia Miao', 'Xiaoshuang Liang', 'Xiangjiang Lu', 'Xianglong Guan', 'Yunyou Huang'] | 2023-07-03 | null | null | null | null | ['open-set-learning'] | ['miscellaneous'] | [ 2.90688187e-01 1.93712890e-01 -1.86420888e-01 -5.01806021e-01
-5.08971214e-01 -3.29555839e-01 1.47139147e-01 1.10800564e-01
-4.35847044e-02 8.84253204e-01 -1.05587818e-01 -8.28423202e-02
-5.60884178e-01 -6.24691725e-01 -2.73394346e-01 -7.78933227e-01
-2.02928230e-01 9.87723649e-01 3.13942991e-02 2.08366126... | [12.541380882263184, 3.0207161903381348] |
f05a8572-b13e-48f3-b058-e13766a8d6fc | refined-an-efficient-zero-shot-capable-1 | 2207.04108 | null | https://arxiv.org/abs/2207.04108v1 | https://arxiv.org/pdf/2207.04108v1.pdf | ReFinED: An Efficient Zero-shot-capable Approach to End-to-End Entity Linking | We introduce ReFinED, an efficient end-to-end entity linking model which uses fine-grained entity types and entity descriptions to perform linking. The model performs mention detection, fine-grained entity typing, and entity disambiguation for all mentions within a document in a single forward pass, making it more than... | ['Andrea Pierleoni', 'Christos Christodoulopoulos', 'Joseph Fisher', 'Shubhi Tyagi', 'Tom Ayoola'] | 2022-07-08 | refined-an-efficient-zero-shot-capable | https://aclanthology.org/2022.naacl-industry.24 | https://aclanthology.org/2022.naacl-industry.24.pdf | naacl-acl-2022-7 | ['entity-typing', 'entity-disambiguation'] | ['natural-language-processing', 'natural-language-processing'] | [-7.15319574e-01 4.32393700e-01 -4.36113864e-01 -6.03582002e-02
-1.15466964e+00 -1.03705466e+00 7.90631115e-01 7.63036668e-01
-7.87100613e-01 1.15600240e+00 3.21537942e-01 -5.01795635e-02
3.90879102e-02 -9.64360774e-01 -7.50749171e-01 1.35411128e-01
-2.55736530e-01 9.55938935e-01 5.73692203e-01 -3.33010048... | [9.467817306518555, 8.896150588989258] |
e55923aa-6a3b-469f-ba10-eaf1f96033f6 | multiclass-mri-brain-tumor-segmentation-using | 2305.06203 | null | https://arxiv.org/abs/2305.06203v1 | https://arxiv.org/pdf/2305.06203v1.pdf | Multiclass MRI Brain Tumor Segmentation using 3D Attention-based U-Net | This paper proposes a 3D attention-based U-Net architecture for multi-region segmentation of brain tumors using a single stacked multi-modal volume created by combining three non-native MRI volumes. The attention mechanism added to the decoder side of the U-Net helps to improve segmentation accuracy by de-emphasizing h... | ['Maryann M. Gitonga'] | 2023-05-10 | null | null | null | null | ['tumor-segmentation', 'brain-tumor-segmentation'] | ['computer-vision', 'medical'] | [ 4.37069505e-01 4.59383696e-01 -2.38447800e-01 -3.05903673e-01
-1.05832374e+00 -5.03252372e-02 3.11332762e-01 1.43168885e-02
-3.88516873e-01 5.84498644e-01 4.64734823e-01 -3.68874133e-01
2.66905546e-01 -5.80905855e-01 -4.46881145e-01 -6.77210987e-01
-3.21768411e-02 4.06853825e-01 2.66849220e-01 -3.53675857... | [14.580185890197754, -2.4225728511810303] |
17341240-4984-463b-a1a0-9c853e032580 | semi-supervised-medical-image-segmentation-1 | 2009.04448 | null | https://arxiv.org/abs/2009.04448v3 | https://arxiv.org/pdf/2009.04448v3.pdf | Semi-supervised Medical Image Segmentation through Dual-task Consistency | Deep learning-based semi-supervised learning (SSL) algorithms have led to promising results in medical images segmentation and can alleviate doctors' expensive annotations by leveraging unlabeled data. However, most of the existing SSL algorithms in literature tend to regularize the model training by perturbing network... | ['Shaoting Zhang', 'Guotai Wang', 'Yinan Chen', 'Jieneng Chen', 'Xiangde Luo', 'Tao Song'] | 2020-09-09 | null | null | null | null | ['semi-supervised-medical-image-segmentation'] | ['computer-vision'] | [ 4.10638213e-01 6.70753777e-01 -4.41986531e-01 -7.43207276e-01
-1.27523983e+00 -2.75453746e-01 1.45716339e-01 -9.47383791e-03
-2.75796384e-01 4.55901563e-01 -3.60232405e-02 -2.52811372e-01
1.16764292e-01 -6.08569264e-01 -9.63786066e-01 -8.22410524e-01
3.11356783e-01 6.01310432e-01 3.49223882e-01 -1.16000235... | [14.660948753356934, -2.0838422775268555] |
738dc22c-ff72-4558-a420-ad3619b244fb | implicit-functions-in-feature-space-for-3d | 2003.01456 | null | https://arxiv.org/abs/2003.01456v2 | https://arxiv.org/pdf/2003.01456v2.pdf | Implicit Functions in Feature Space for 3D Shape Reconstruction and Completion | While many works focus on 3D reconstruction from images, in this paper, we focus on 3D shape reconstruction and completion from a variety of 3D inputs, which are deficient in some respect: low and high resolution voxels, sparse and dense point clouds, complete or incomplete. Processing of such 3D inputs is an increasin... | ['Gerard Pons-Moll', 'Julian Chibane', 'Thiemo Alldieck'] | 2020-03-03 | implicit-functions-in-feature-space-for-3d-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Chibane_Implicit_Functions_in_Feature_Space_for_3D_Shape_Reconstruction_and_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Chibane_Implicit_Functions_in_Feature_Space_for_3D_Shape_Reconstruction_and_CVPR_2020_paper.pdf | cvpr-2020-6 | ['3d-object-reconstruction'] | ['computer-vision'] | [-9.44928452e-02 4.05323990e-02 1.30188555e-01 -3.12788278e-01
-3.97062838e-01 -6.72503293e-01 6.73839688e-01 -1.75581157e-01
-1.60553038e-01 4.75719124e-01 3.68099272e-01 -9.77953598e-02
-3.14427257e-01 -9.50417042e-01 -9.30231452e-01 -5.65895796e-01
2.93316948e-03 1.00929761e+00 4.19272147e-02 -1.63055196... | [8.652941703796387, -3.659486770629883] |
c37dd893-471d-464c-b32a-1d780f07c105 | guiding-teacher-forcing-with-seer-forcing-for | 2106.06751 | null | https://arxiv.org/abs/2106.06751v1 | https://arxiv.org/pdf/2106.06751v1.pdf | Guiding Teacher Forcing with Seer Forcing for Neural Machine Translation | Although teacher forcing has become the main training paradigm for neural machine translation, it usually makes predictions only conditioned on past information, and hence lacks global planning for the future. To address this problem, we introduce another decoder, called seer decoder, into the encoder-decoder framework... | ['Chenze Shao', 'Zhengxin Yang', 'Dengji Guo', 'Shuhao Gu', 'Yang Feng'] | 2021-06-12 | null | https://aclanthology.org/2021.acl-long.223 | https://aclanthology.org/2021.acl-long.223.pdf | acl-2021-5 | ['l2-regularization'] | ['methodology'] | [ 2.41335258e-01 6.68419063e-01 -5.40648878e-01 -1.79645225e-01
-1.05893528e+00 -6.76863968e-01 8.01756203e-01 -6.44346774e-01
-2.46820301e-01 1.16887379e+00 5.80237091e-01 -7.76017964e-01
7.44160950e-01 -5.74778080e-01 -1.31911409e+00 -4.62466389e-01
3.76586407e-01 6.12706602e-01 -1.50271147e-01 -4.20824736... | [11.705164909362793, 10.077173233032227] |
c664645d-5b11-4da3-bc1d-49fab28a2aea | joint-learning-of-intrinsic-images-and | 1807.11857 | null | http://arxiv.org/abs/1807.11857v1 | http://arxiv.org/pdf/1807.11857v1.pdf | Joint Learning of Intrinsic Images and Semantic Segmentation | Semantic segmentation of outdoor scenes is problematic when there are
variations in imaging conditions. It is known that albedo (reflectance) is
invariant to all kinds of illumination effects. Thus, using reflectance images
for semantic segmentation task can be favorable. Additionally, not only
segmentation may benefit... | ['Hoang-An Le', 'Thomas T. Groenestege', 'Theo Gevers', 'Sezer Karaoglu', 'Anil S. Baslamisli', 'Partha Das'] | 2018-07-31 | joint-learning-of-intrinsic-images-and-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Anil_Baslamisli_Joint_Learning_of_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Anil_Baslamisli_Joint_Learning_of_ECCV_2018_paper.pdf | eccv-2018-9 | ['intrinsic-image-decomposition'] | ['computer-vision'] | [ 5.94442129e-01 -1.19596057e-01 2.31879309e-01 -7.44607806e-01
-4.32307422e-01 -3.84885997e-01 1.92856148e-01 -2.68681616e-01
-2.65095472e-01 4.37951297e-01 -5.92327528e-02 -1.08237348e-01
2.23172113e-01 -1.03350759e+00 -8.54108751e-01 -1.02558041e+00
4.54850197e-01 8.02816376e-02 6.00120761e-02 -1.66601911... | [9.969810485839844, -2.478238582611084] |
df9273f6-84f0-4eb5-b8bc-eee25679dfaa | ugent-t2k-at-the-2nd-dialdoc-shared-task-a | null | null | https://aclanthology.org/2022.dialdoc-1.12 | https://aclanthology.org/2022.dialdoc-1.12.pdf | UGent-T2K at the 2nd DialDoc Shared Task: A Retrieval-Focused Dialog System Grounded in Multiple Documents | This work presents the contribution from the Text-to-Knowledge team of Ghent University (UGent-T2K) to the MultiDoc2Dial shared task on modeling dialogs grounded in multiple documents. We propose a pipeline system, comprising (1) document retrieval, (2) passage retrieval, and (3) response generation. We engineered thes... | ['Chris Develder', 'Thomas Demeester', 'Johannes Deleu', 'Amir Hadifar', 'Yiwei Jiang'] | null | null | null | null | dialdoc-acl-2022-5 | ['passage-retrieval'] | ['natural-language-processing'] | [ 1.63514331e-01 4.47156668e-01 3.67577851e-01 -3.86337340e-01
-1.48458362e+00 -7.55381525e-01 1.12954855e+00 4.51778501e-01
-4.98713821e-01 9.70854163e-01 7.57108748e-01 -3.24135244e-01
-1.90467816e-02 -3.49447727e-01 -7.07138717e-01 -2.30786115e-01
2.15834275e-01 1.11619377e+00 6.27967954e-01 -5.56533456... | [12.351924896240234, 8.033326148986816] |
77ffa945-fea7-4438-89ab-74b1c0c7b5c2 | trapacc-and-trapaccs-at-parseme-shared-task | null | null | https://aclanthology.org/W18-4930 | https://aclanthology.org/W18-4930.pdf | TRAPACC and TRAPACCS at PARSEME Shared Task 2018: Neural Transition Tagging of Verbal Multiword Expressions | We describe the TRAPACC system and its variant TRAPACCS that participated in the closed track of the PARSEME Shared Task 2018 on labeling verbal multiword expressions (VMWEs). TRAPACC is a modified arc-standard transition system based on Constant and Nivre{'}s (2016) model of joint syntactic and lexical analysis in whi... | ['Behrang Qasemizadeh', 'Regina Stodden', 'Laura Kallmeyer'] | 2018-08-01 | null | null | null | coling-2018-8 | ['lexical-analysis'] | ['natural-language-processing'] | [-2.22179756e-01 3.76923084e-01 -6.34783685e-01 -6.61150694e-01
-9.77160573e-01 -7.38762617e-01 5.69462478e-01 3.75640929e-01
-8.65193784e-01 7.53525794e-01 3.01481605e-01 -8.00339460e-01
4.26637143e-01 -3.56326282e-01 -8.00539732e-01 -2.94195920e-01
-1.40160903e-01 6.65483236e-01 2.70367172e-02 1.78036522... | [10.516159057617188, 10.044373512268066] |
2006fa64-dafe-4635-a2d6-29679eb27017 | deep-denerative-models-for-drug-design-and | 2109.06469 | null | https://arxiv.org/abs/2109.06469v1 | https://arxiv.org/pdf/2109.06469v1.pdf | Deep Denerative Models for Drug Design and Response | Designing new chemical compounds with desired pharmaceutical properties is a challenging task and takes years of development and testing. Still, a majority of new drugs fail to prove efficient. Recent success of deep generative modeling holds promises of generation and optimization of new molecules. In this review pape... | ['Lada Nuzhna', 'Karina Zadorozhny'] | 2021-09-14 | null | null | null | null | ['drug-response-prediction'] | ['medical'] | [ 3.42793316e-01 -2.25282907e-01 -5.12867808e-01 -9.49959829e-02
-7.95125723e-01 -7.90640056e-01 4.31364805e-01 3.13248158e-01
1.33515924e-01 1.47296786e+00 5.76106308e-04 -5.76287389e-01
-1.43718615e-01 -6.32931709e-01 -5.89031816e-01 -1.08926237e+00
7.65009820e-02 7.99767733e-01 -2.60647446e-01 -1.18715942... | [4.966722011566162, 5.826615810394287] |
d4ec7839-d6b3-473c-86b4-cd4cc2f54707 | feature-learning-for-chord-recognition-the | 1612.05065 | null | http://arxiv.org/abs/1612.05065v1 | http://arxiv.org/pdf/1612.05065v1.pdf | Feature Learning for Chord Recognition: The Deep Chroma Extractor | We explore frame-level audio feature learning for chord recognition using
artificial neural networks. We present the argument that chroma vectors
potentially hold enough information to model harmonic content of audio for
chord recognition, but that standard chroma extractors compute too noisy
features. This leads us to... | ['Filip Korzeniowski', 'Gerhard Widmer'] | 2016-12-15 | null | null | null | null | ['chord-recognition'] | ['audio'] | [ 4.47472095e-01 3.20176296e-02 3.11941542e-02 -2.29607657e-01
-1.03266811e+00 -6.73427284e-01 3.66502315e-01 -2.84253955e-02
-4.39306200e-01 5.00122905e-01 3.97086829e-01 2.14438647e-01
-3.90503317e-01 -7.69288838e-01 -3.89433712e-01 -8.17889690e-01
-2.63124049e-01 -1.01629846e-01 1.39843374e-01 -5.14882922... | [15.810012817382812, 5.299386024475098] |
a63a5038-e2c3-4b4a-b44b-3da6a684a215 | weakly-supervised-cross-lingual-named-entity | 1707.02483 | null | http://arxiv.org/abs/1707.02483v1 | http://arxiv.org/pdf/1707.02483v1.pdf | Weakly Supervised Cross-Lingual Named Entity Recognition via Effective Annotation and Representation Projection | The state-of-the-art named entity recognition (NER) systems are supervised
machine learning models that require large amounts of manually annotated data
to achieve high accuracy. However, annotating NER data by human is expensive
and time-consuming, and can be quite difficult for a new language. In this
paper, we prese... | ['Jian Ni', 'Georgiana Dinu', 'Radu Florian'] | 2017-07-08 | weakly-supervised-cross-lingual-named-entity-1 | https://aclanthology.org/P17-1135 | https://aclanthology.org/P17-1135.pdf | acl-2017-7 | ['cross-lingual-ner'] | ['natural-language-processing'] | [-1.17656216e-01 1.40371576e-01 6.89059421e-02 -6.50537014e-01
-1.28939486e+00 -7.54418433e-01 4.64571923e-01 1.32569253e-01
-1.01874149e+00 7.79500067e-01 5.59844434e-01 -1.95241198e-01
4.36450690e-01 -7.30762959e-01 -3.78647685e-01 -5.74209690e-01
4.17478502e-01 7.00367212e-01 2.13833824e-01 -2.19481781... | [9.946303367614746, 9.728140830993652] |
be7b5d4f-7acc-4e56-969b-d545b9fc544d | improving-adversarial-text-generation-by | 2005.01279 | null | https://arxiv.org/abs/2005.01279v1 | https://arxiv.org/pdf/2005.01279v1.pdf | Improving Adversarial Text Generation by Modeling the Distant Future | Auto-regressive text generation models usually focus on local fluency, and may cause inconsistent semantic meaning in long text generation. Further, automatically generating words with similar semantics is challenging, and hand-crafted linguistic rules are difficult to apply. We consider a text planning scheme and pres... | ['Lawrence Carin', 'Dinghan Shen', 'Wenlin Wang', 'Changyou Chen', 'Zheng Wen', 'Zhe Gan', 'Ruiyi Zhang', 'Guoyin Wang'] | 2020-05-04 | improving-adversarial-text-generation-by-1 | https://aclanthology.org/2020.acl-main.227 | https://aclanthology.org/2020.acl-main.227.pdf | acl-2020-6 | ['adversarial-text'] | ['adversarial'] | [ 8.61499831e-02 4.16856229e-01 -3.20135742e-01 -1.25991836e-01
-6.90843225e-01 -3.79180849e-01 8.41621101e-01 -8.12747553e-02
-6.65276274e-02 1.12453854e+00 4.81783360e-01 -1.89878896e-01
1.13012798e-01 -8.68365347e-01 -5.39259553e-01 -4.22360599e-01
3.75010788e-01 6.13469779e-01 -2.20336139e-01 -4.61702883... | [11.89515209197998, 9.130379676818848] |
18f5f108-39c8-4209-a098-86ab4beff4b7 | permutation-invariance-and-uncertainty-in | 2105.12409 | null | https://arxiv.org/abs/2105.12409v1 | https://arxiv.org/pdf/2105.12409v1.pdf | Permutation invariance and uncertainty in multitemporal image super-resolution | Recent advances have shown how deep neural networks can be extremely effective at super-resolving remote sensing imagery, starting from a multitemporal collection of low-resolution images. However, existing models have neglected the issue of temporal permutation, whereby the temporal ordering of the input images does n... | ['Enrico Magli', 'Diego Valsesia'] | 2021-05-26 | null | null | null | null | ['multi-frame-super-resolution'] | ['computer-vision'] | [ 2.93439090e-01 -2.69519180e-01 1.65646181e-01 -4.72538918e-01
-9.11309958e-01 -5.99242449e-01 6.06589854e-01 -1.54402152e-01
-4.90367234e-01 7.87506759e-01 1.07854694e-01 -8.26553181e-02
-6.10584915e-01 -9.12263036e-01 -8.16171348e-01 -7.85257220e-01
-3.79315078e-01 2.92219400e-01 2.47051015e-01 -3.92542213... | [9.845919609069824, -1.6535167694091797] |
771f2b52-d37e-4848-baa5-03fb223da2b2 | an-effective-entropy-assisted-mind-wandering | 2005.12076 | null | https://arxiv.org/abs/2005.12076v2 | https://arxiv.org/pdf/2005.12076v2.pdf | An Effective Entropy-assisted Mind-wandering Detection System with EEG Signals based on MM-SART Database | Mind-wandering (MW), which usually defined as a lapse of attention, occurs between 20%-40% of the time, has negative effects on our daily life. Therefore, detecting when MW occurs can prevent us from those negative outcomes resulting from MW, such as failing to keep track of course during learning. In this work, we fir... | ['An-Yeu Wu', 'Su-Ling Yeh', 'Win-Ken Beh', 'Zih-Ling Chen', 'Ching-Yen Shih', 'Hsing-Hao Lee', 'Yi-Ta Chen'] | 2020-05-25 | null | null | null | null | ['photoplethysmography-ppg'] | ['medical'] | [ 1.23706073e-01 -2.63166755e-01 1.72384173e-01 -1.74495041e-01
-3.74074399e-01 -1.30590990e-01 -1.05289489e-01 8.67696106e-02
-5.53936005e-01 9.30643916e-01 -6.96124360e-02 -2.36904457e-01
-4.50489849e-01 -6.70551121e-01 -1.42815694e-01 -7.16043174e-01
-1.74957514e-01 -5.65825045e-01 2.27356348e-02 -6.85168579... | [13.304965019226074, 3.3495094776153564] |
e829c409-7a1a-4367-801e-fa1eb77476d0 | i-dont-know-where-he-is-not-does-deception | null | null | https://aclanthology.org/W12-0402 | https://aclanthology.org/W12-0402.pdf | ``I Don't Know Where He is Not'': Does Deception Research yet Offer a Basis for Deception Detectives? | null | ['Lee Gillam', 'Anna Vartapetiance'] | 2012-04-01 | null | null | null | ws-2012-4 | ['deception-detection'] | ['miscellaneous'] | [-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... | [-7.2529401779174805, 3.779604911804199] |
c7c69d0f-9d86-40d3-a868-e9306e9463b1 | 2305-14405 | 2305.14405 | null | https://arxiv.org/abs/2305.14405v1 | https://arxiv.org/pdf/2305.14405v1.pdf | NeuralMatrix: Moving Entire Neural Networks to General Matrix Multiplication for Efficient Inference | In this study, we introduce NeuralMatrix, a novel framework that enables the computation of versatile deep neural networks (DNNs) on a single general matrix multiplication (GEMM) accelerator. The proposed approach overcomes the specificity limitations of ASIC-based accelerators while achieving application-specific acce... | ['An Zou', 'Yiran Li', 'Xin He', 'Jie Zhao', 'Ruiqi Sun'] | 2023-05-23 | null | null | null | null | ['specificity'] | ['natural-language-processing'] | [-1.25549987e-01 -3.61858070e-01 2.29093060e-01 -4.82985556e-01
-5.25305159e-02 -4.64476138e-01 6.39734149e-01 -9.47149470e-03
-8.84499550e-01 5.96016943e-01 -3.95971864e-01 -9.52829003e-01
3.65461171e-01 -9.79281604e-01 -9.05926108e-01 -7.68705070e-01
2.18734860e-01 2.31727242e-01 2.15590626e-01 -1.56467661... | [8.432574272155762, 2.944458246231079] |
673958f9-667c-4c0b-a2f8-ff674c1de7c9 | i-2-sdf-intrinsic-indoor-scene-reconstruction | 2303.07634 | null | https://arxiv.org/abs/2303.07634v2 | https://arxiv.org/pdf/2303.07634v2.pdf | I$^2$-SDF: Intrinsic Indoor Scene Reconstruction and Editing via Raytracing in Neural SDFs | In this work, we present I$^2$-SDF, a new method for intrinsic indoor scene reconstruction and editing using differentiable Monte Carlo raytracing on neural signed distance fields (SDFs). Our holistic neural SDF-based framework jointly recovers the underlying shapes, incident radiance and materials from multi-view imag... | ['Rui Wang', 'Hujun Bao', 'Wei Hua', 'Rui Tang', 'Lisha Wang', 'Dianbing Xi', 'Jifan Li', 'Fujun Luan', 'Qi Ye', 'Yuchi Huo', 'Jingsen Zhu'] | 2023-03-14 | null | null | null | null | ['indoor-scene-reconstruction'] | ['computer-vision'] | [ 6.30288005e-01 -3.00564200e-01 7.17350304e-01 -7.00846612e-01
-6.69413030e-01 -6.72957420e-01 5.80779076e-01 -3.18295687e-01
4.67678503e-04 1.00980520e+00 3.24492633e-01 1.00643970e-01
-8.24834555e-02 -1.34712708e+00 -1.22106838e+00 -4.20492768e-01
3.73683572e-01 3.02061588e-01 -4.75084223e-02 -2.02846482... | [9.604225158691406, -3.1153600215911865] |
d7f91bd7-abde-4e06-8b7c-cd30ba1be3d8 | homogcl-rethinking-homophily-in-graph | 2306.09614 | null | https://arxiv.org/abs/2306.09614v1 | https://arxiv.org/pdf/2306.09614v1.pdf | HomoGCL: Rethinking Homophily in Graph Contrastive Learning | Contrastive learning (CL) has become the de-facto learning paradigm in self-supervised learning on graphs, which generally follows the "augmenting-contrasting" learning scheme. However, we observe that unlike CL in computer vision domain, CL in graph domain performs decently even without augmentation. We conduct a syst... | ['Jian-Huang Lai', 'Hui Xiong', 'Chang-Dong Wang', 'Wen-Zhi Li'] | 2023-06-16 | null | null | null | null | ['contrastive-learning', 'contrastive-learning'] | ['computer-vision', 'methodology'] | [-2.20209107e-01 4.26888555e-01 -6.35157108e-01 -2.31831849e-01
-1.55987427e-01 -6.23376608e-01 7.93422580e-01 4.14028138e-01
3.97841968e-02 5.30802965e-01 1.14881195e-01 -5.29556811e-01
-1.19576402e-01 -9.08035934e-01 -8.39441240e-01 -8.49507630e-01
-2.97494769e-01 2.13032410e-01 4.83956747e-02 -2.12600365... | [7.163616180419922, 6.203781604766846] |
b1b29463-eb62-4c52-818e-dac6aa6ac4d4 | shifted-chunk-transformer-for-spatio-temporal | 2108.11575 | null | https://arxiv.org/abs/2108.11575v5 | https://arxiv.org/pdf/2108.11575v5.pdf | Shifted Chunk Transformer for Spatio-Temporal Representational Learning | Spatio-temporal representational learning has been widely adopted in various fields such as action recognition, video object segmentation, and action anticipation. Previous spatio-temporal representational learning approaches primarily employ ConvNets or sequential models,e.g., LSTM, to learn the intra-frame and inter-... | ['Ji Liu', 'Sen yang', 'Tingxun Lv', 'Wentao Zhu', 'Xuefan Zha'] | 2021-08-26 | null | http://proceedings.neurips.cc/paper/2021/hash/5edc4f7dce28c711afc6265b4f99bf57-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/5edc4f7dce28c711afc6265b4f99bf57-Paper.pdf | neurips-2021-12 | ['action-anticipation'] | ['computer-vision'] | [ 9.93682742e-02 -5.03362477e-01 -5.29056609e-01 -3.46162409e-01
-8.94935608e-01 -1.86040953e-01 5.55939257e-01 -8.06656629e-02
-5.07803738e-01 5.51519990e-01 4.21778053e-01 -2.69340375e-03
-1.99936509e-01 -4.93921071e-01 -9.05394912e-01 -7.84614205e-01
-2.23856121e-01 -1.36895860e-02 6.63155258e-01 6.78408816... | [8.688278198242188, 0.4486660659313202] |
1464e741-9b7f-46b1-981c-edfbbc9d6b5d | data-refinement-for-fully-unsupervised-visual | 2202.12759 | null | https://arxiv.org/abs/2202.12759v1 | https://arxiv.org/pdf/2202.12759v1.pdf | Data refinement for fully unsupervised visual inspection using pre-trained networks | Anomaly detection has recently seen great progress in the field of visual inspection. More specifically, the use of classical outlier detection techniques on features extracted by deep pre-trained neural networks have been shown to deliver remarkable performances on the MVTec Anomaly Detection (MVTec AD) dataset. Howev... | ['Pierre Gutierrez', 'Benjamin Missaoui', 'Antoine Cordier'] | 2022-02-25 | null | null | null | null | ['one-class-classification'] | ['miscellaneous'] | [ 3.52606565e-01 8.16078708e-02 4.83574510e-01 -1.66205894e-02
-4.69253749e-01 -5.36337614e-01 6.46937549e-01 6.08664036e-01
-4.17463243e-01 3.41212511e-01 -2.61886597e-01 -2.69982159e-01
-4.13755298e-01 -7.25065649e-01 -8.05633664e-01 -1.04012167e+00
-1.82211846e-01 2.78553426e-01 5.85566044e-01 -1.03886768... | [7.671454906463623, 2.1881444454193115] |
889f2107-27d3-4ec1-b5a6-cd386502e0e7 | sequence-to-sequence-pre-training-with-data | 1909.06002 | null | https://arxiv.org/abs/1909.06002v2 | https://arxiv.org/pdf/1909.06002v2.pdf | Sequence-to-sequence Pre-training with Data Augmentation for Sentence Rewriting | We study sequence-to-sequence (seq2seq) pre-training with data augmentation for sentence rewriting. Instead of training a seq2seq model with gold training data and augmented data simultaneously, we separate them to train in different phases: pre-training with the augmented data and fine-tuning with the gold data. We al... | ['Xu sun', 'Furu Wei', 'Tao Ge', 'Ming Zhou', 'Yi Zhang'] | 2019-09-13 | null | null | null | null | ['formality-style-transfer'] | ['natural-language-processing'] | [ 6.53158128e-01 2.93433338e-01 2.11829796e-01 -5.70878029e-01
-1.18769419e+00 -8.07557285e-01 4.64794636e-01 9.61293653e-03
-7.50960708e-01 9.92676198e-01 2.00051606e-01 -7.30295360e-01
4.93909866e-01 -5.55296540e-01 -8.23319554e-01 -2.54797310e-01
3.87943536e-01 3.37287545e-01 -1.02814063e-01 -9.98834789... | [11.34938907623291, 10.252737045288086] |
c11fd423-d249-4a63-8f0c-a2be2478ed2d | one-step-and-two-step-classification-for | 1706.01206 | null | http://arxiv.org/abs/1706.01206v1 | http://arxiv.org/pdf/1706.01206v1.pdf | One-step and Two-step Classification for Abusive Language Detection on Twitter | Automatic abusive language detection is a difficult but important task for
online social media. Our research explores a two-step approach of performing
classification on abusive language and then classifying into specific types and
compares it with one-step approach of doing one multi-class classification for
detecting... | ['Pascale Fung', 'Ji Ho Park'] | 2017-06-05 | one-step-and-two-step-classification-for-1 | https://aclanthology.org/W17-3006 | https://aclanthology.org/W17-3006.pdf | ws-2017-8 | ['abuse-detection'] | ['natural-language-processing'] | [-8.94070193e-02 -1.57835316e-02 -7.06542373e-01 -4.30547088e-01
-4.26967710e-01 -5.54386735e-01 1.09987962e+00 4.15415317e-01
-9.94619548e-01 9.47680175e-01 3.31427783e-01 -4.41568404e-01
3.12341362e-01 -8.41444790e-01 1.50559932e-01 -3.39186221e-01
1.72005147e-01 5.32466710e-01 1.00479671e-03 -6.97042465... | [8.747007369995117, 10.516603469848633] |
f28fe92e-840d-4427-9435-0ab1b64afdf1 | claws-contrastive-learning-with-hard | 2112.00847 | null | https://arxiv.org/abs/2112.00847v2 | https://arxiv.org/pdf/2112.00847v2.pdf | CLAWS: Contrastive Learning with hard Attention and Weak Supervision | Learning effective visual representations without human supervision is a long-standing problem in computer vision. Recent advances in self-supervised learning algorithms have utilized contrastive learning, with methods such as SimCLR, which applies a composition of augmentations to an image, and minimizes a contrastive... | ['Ali Jannesari', 'Matthew Darr', 'John Just', 'Ramakrishnan Sundareswaran', 'Jansel Herrera-Gerena'] | 2021-12-01 | null | null | null | null | ['hard-attention'] | ['methodology'] | [ 2.86742628e-01 1.70242295e-01 -1.78374887e-01 -3.02475095e-01
-1.02906249e-01 -6.11540794e-01 4.20110554e-01 4.82252300e-01
-2.26595700e-01 1.22161783e-01 -3.18045467e-01 -2.40377113e-01
-2.78770290e-02 -6.75305426e-01 -8.27297866e-01 -8.79049122e-01
-2.94608355e-01 4.18771029e-01 7.22000003e-02 -4.31309529... | [9.566758155822754, 1.8660024404525757] |
efdae121-bc97-41df-b90c-5e2d42f55e71 | kevin-a-knowledge-enhanced-validity-and | null | null | https://aclanthology.org/2022.argmining-1.9 | https://aclanthology.org/2022.argmining-1.9.pdf | KEViN: A Knowledge Enhanced Validity and Novelty Classifier for Arguments | The ArgMining 2022 Shared Task is concerned with predicting the validity and novelty of an inference for a given premise and conclusion pair. We propose two feed-forward network based models (KEViN1 and KEViN2), which combine features generated from several pretrained transformers and the WikiData knowledge graph. The ... | ['Nadin Kökciyan', 'Jeff Z. Pan', 'Björn Ross', 'Vaishak Belle', 'Sandrine Chausson', 'Xue Li', 'Ameer Saadat-Yazdi'] | null | null | null | null | argmining-acl-2022-10 | ['valnov'] | ['natural-language-processing'] | [-1.43748429e-02 5.69381893e-01 -2.60269552e-01 -6.65090084e-01
-3.73572558e-01 -3.50225061e-01 8.72981787e-01 4.03859943e-01
-3.94663930e-01 7.24746943e-01 4.06991631e-01 -3.58096719e-01
-8.92292082e-01 -1.07343388e+00 -7.43086457e-01 -2.04747152e-02
-6.00926913e-02 5.27463078e-01 2.66484231e-01 -2.32608333... | [9.392732620239258, 8.145943641662598] |
2d9fe227-af9c-4363-8fc0-4dd89d877e6b | adaptive-loose-optimization-for-robust | 2305.03971 | null | https://arxiv.org/abs/2305.03971v2 | https://arxiv.org/pdf/2305.03971v2.pdf | Adaptive loose optimization for robust question answering | Question answering methods are well-known for leveraging data bias, such as the language prior in visual question answering and the position bias in machine reading comprehension (extractive question answering). Current debiasing methods often come at the cost of significant in-distribution performance to achieve favor... | ['Jun Liu', 'Ting Han', 'Min Hu', 'Dechen Kong', 'Zewei Wang', 'Pinghui Wang', 'Jie Ma'] | 2023-05-06 | null | null | null | null | ['reading-comprehension', 'machine-reading-comprehension'] | ['natural-language-processing', 'natural-language-processing'] | [ 9.41124279e-03 1.66173995e-01 -9.95929465e-02 -5.12993991e-01
-1.29950607e+00 -7.06253469e-01 3.04032207e-01 1.84306696e-01
-4.20931399e-01 5.38122833e-01 9.77280065e-02 -6.49030805e-01
-1.02446266e-01 -6.26050472e-01 -8.22118223e-01 -6.28773928e-01
4.98532504e-01 4.25455123e-01 3.72758299e-01 -4.22214240... | [11.243130683898926, 8.071991920471191] |
dd047223-9163-4fdd-bb1a-7aa112cb2f84 | cloudbrain-reconai-an-online-platform-for-mri | 2212.01878 | null | https://arxiv.org/abs/2212.01878v1 | https://arxiv.org/pdf/2212.01878v1.pdf | CloudBrain-ReconAI: An Online Platform for MRI Reconstruction and Image Quality Evaluation | Efficient collaboration between engineers and radiologists is important for image reconstruction algorithm development and image quality evaluation in magnetic resonance imaging (MRI). Here, we develop CloudBrain-ReconAI, an online cloud computing platform, for algorithm deployment, fast and blind reader study. This pl... | ['Xiaobo Qu', 'Di Guo', 'Jiyang Dong', 'Qing Hong', 'Jianzhong Lin', 'Taishan Kang', 'Jianjun Zhou', 'Liuhong Zhu', 'Biao Qu', 'Yu Hu', 'Zi Wang', 'Jiayu Li', 'Chen Qian', 'Yirong Zhou'] | 2022-12-04 | null | null | null | null | ['mri-reconstruction'] | ['computer-vision'] | [ 1.11206017e-01 -5.98107755e-01 -1.17887288e-01 -2.10128069e-01
-8.95737648e-01 -2.71203220e-01 -1.97355032e-01 -1.22919239e-01
-3.83738458e-01 4.04081076e-01 9.89628658e-02 -5.45121133e-01
-2.12642521e-01 -3.81238669e-01 -1.02461122e-01 -8.70025814e-01
-2.94630885e-01 6.20727122e-01 3.74533311e-02 4.32166755... | [13.745709419250488, -2.3208060264587402] |
54913555-3938-4a61-b378-9bcf60bb124f | recurrent-segmentation-meets-block-models-in | 2205.09862 | null | https://arxiv.org/abs/2205.09862v1 | https://arxiv.org/pdf/2205.09862v1.pdf | Recurrent segmentation meets block models in temporal networks | A popular approach to model interactions is to represent them as a network with nodes being the agents and the interactions being the edges. Interactions are often timestamped, which leads to having timestamped edges. Many real-world temporal networks have a recurrent or possibly cyclic behaviour. For example, social n... | ['Nikolaj Tatti', '{Chamalee Wickrama Arachchi'] | 2022-05-19 | null | null | null | null | ['stochastic-block-model'] | ['graphs'] | [ 0.09965593 0.25420046 -0.13893837 -0.01050542 0.09151454 -0.6790573
0.5584532 0.10223176 -0.600989 0.8124717 -0.39039457 -0.26035225
-0.4505368 -1.116752 -0.7513039 -0.81184053 -0.82838935 0.90485924
0.54677933 0.04734632 0.08677974 0.42411488 -1.0582666 -0.4527895
0.50216323 0.67180985 -0.03... | [6.849013805389404, 5.173060417175293] |
74cd2084-9bfb-41b6-b358-5beae370511a | rurebus-a-case-study-of-joint-named-entity | 2010.15939 | null | https://arxiv.org/abs/2010.15939v1 | https://arxiv.org/pdf/2010.15939v1.pdf | RuREBus: a Case Study of Joint Named Entity Recognition and Relation Extraction from e-Government Domain | We show-case an application of information extraction methods, such as named entity recognition (NER) and relation extraction (RE) to a novel corpus, consisting of documents, issued by a state agency. The main challenges of this corpus are: 1) the annotation scheme differs greatly from the one used for the general doma... | ['Ivan Smurov', 'Elena Tutubalina', 'Veronika Sarkisyan', 'Vladimir Ivanov', 'Tatiana Batura', 'Ekaterina Artemova', 'Vitaly Ivanin'] | 2020-10-29 | null | null | null | null | ['text-annotation'] | ['natural-language-processing'] | [ 1.78032249e-01 6.03389978e-01 -5.88477664e-02 -3.81529331e-01
-1.04322910e+00 -7.84632266e-01 9.96601224e-01 2.67724037e-01
-7.91210115e-01 1.13677716e+00 3.92655015e-01 -5.30443370e-01
1.34245783e-01 -4.81679142e-01 -3.19179267e-01 -1.94109842e-01
1.70960240e-02 9.19204950e-01 5.33761263e-01 -4.25936490... | [9.705061912536621, 9.315640449523926] |
1c6092cb-0957-43b2-9fbd-98504ea3ab54 | gesgpt-speech-gesture-synthesis-with-text | 2303.13013 | null | https://arxiv.org/abs/2303.13013v1 | https://arxiv.org/pdf/2303.13013v1.pdf | GesGPT: Speech Gesture Synthesis With Text Parsing from GPT | Gesture synthesis has gained significant attention as a critical research area, focusing on producing contextually appropriate and natural gestures corresponding to speech or textual input. Although deep learning-based approaches have achieved remarkable progress, they often overlook the rich semantic information prese... | ['Dongdong Weng', 'Shuwu Zhang', 'Zhi Zeng', 'Zeyu Zhao', 'Nan Gao'] | 2023-03-23 | null | null | null | null | ['gesture-generation'] | ['robots'] | [ 5.59817016e-01 3.56481830e-03 -2.58831024e-01 -4.05239642e-01
-8.41614246e-01 -5.64813435e-01 8.55079114e-01 -3.36308688e-01
-1.74374491e-01 1.86519444e-01 1.05353320e+00 -1.75438508e-01
-4.71013924e-03 -7.55071998e-01 -3.31155181e-01 -3.62458438e-01
2.17307523e-01 5.08396029e-01 4.13472876e-02 -2.73184747... | [5.623302459716797, -0.1225379928946495] |
578e6da6-82af-4d63-97a6-d8d039db7ccd | structured-state-space-models-for-multiple | 2306.15789 | null | https://arxiv.org/abs/2306.15789v1 | https://arxiv.org/pdf/2306.15789v1.pdf | Structured State Space Models for Multiple Instance Learning in Digital Pathology | Multiple instance learning is an ideal mode of analysis for histopathology data, where vast whole slide images are typically annotated with a single global label. In such cases, a whole slide image is modelled as a collection of tissue patches to be aggregated and classified. Common models for performing this classific... | ['Stergios Christodoulidis', 'Paul-Henry Cournède', 'Maria Vakalopoulou', 'Joseph Boyd', 'Leo Fillioux'] | 2023-06-27 | null | null | null | null | ['whole-slide-images', 'multiple-instance-learning'] | ['computer-vision', 'methodology'] | [ 8.53731573e-01 -9.42621380e-02 -5.49787283e-01 -2.13515341e-01
-1.35135305e+00 -3.79522473e-01 3.04832637e-01 3.58300179e-01
-4.58182186e-01 7.58664966e-01 -4.52328362e-02 -4.46292639e-01
-1.40158027e-01 -4.84151363e-01 -6.95359111e-01 -1.31557393e+00
5.08639142e-02 6.27672136e-01 6.68705702e-02 8.84771496... | [15.117100715637207, -2.8752505779266357] |
5e0bff08-4359-4f1c-863a-a556c28844f4 | lattice-protein-design-using-bayesian | 2003.06601 | null | https://arxiv.org/abs/2003.06601v5 | https://arxiv.org/pdf/2003.06601v5.pdf | Lattice protein design using Bayesian learning | Protein design is the inverse approach of the three-dimensional (3D) structure prediction for elucidating the relationship between the 3D structures and amino acid sequences. In general, the computation of the protein design involves a double loop: a loop for amino acid sequence changes and a loop for an exhaustive con... | ['Tomoei Takahashi', 'Kei Tokita', 'George Chikenji'] | 2020-03-14 | null | null | null | null | ['protein-design'] | ['medical'] | [ 1.74135908e-01 6.07540831e-02 -1.32506654e-01 -7.50074387e-02
-1.97227746e-01 -4.66038048e-01 4.38080460e-01 7.35755265e-02
-4.93058443e-01 1.12833285e+00 -1.22539975e-01 -7.11889505e-01
9.08511803e-02 -7.41053760e-01 -9.64338601e-01 -1.31103444e+00
-1.08174115e-01 7.08317876e-01 5.61766803e-01 -2.52505898... | [4.801727294921875, 5.311549663543701] |
78b9602c-fb25-4079-84f1-d29bd2d7a269 | distilled-semantics-for-comprehensive-scene | 2003.14030 | null | https://arxiv.org/abs/2003.14030v1 | https://arxiv.org/pdf/2003.14030v1.pdf | Distilled Semantics for Comprehensive Scene Understanding from Videos | Whole understanding of the surroundings is paramount to autonomous systems. Recent works have shown that deep neural networks can learn geometry (depth) and motion (optical flow) from a monocular video without any explicit supervision from ground truth annotations, particularly hard to source for these two tasks. In th... | ['Luigi Di Stefano', 'Pierluigi Zama Ramirez', 'Filippo Aleotti', 'Stefano Mattoccia', 'Samuele Salti', 'Matteo Poggi', 'Fabio Tosi'] | 2020-03-31 | distilled-semantics-for-comprehensive-scene-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Tosi_Distilled_Semantics_for_Comprehensive_Scene_Understanding_from_Videos_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Tosi_Distilled_Semantics_for_Comprehensive_Scene_Understanding_from_Videos_CVPR_2020_paper.pdf | cvpr-2020-6 | ['motion-segmentation'] | ['computer-vision'] | [ 2.24149629e-01 6.93852827e-02 -1.26496151e-01 -4.29923564e-01
-2.26096362e-01 -7.44317114e-01 6.95180833e-01 -3.38075846e-01
-6.33034468e-01 8.80270064e-01 3.30788791e-02 -2.47468174e-01
2.86711425e-01 -7.45021105e-01 -9.89057541e-01 -5.50491750e-01
4.44524698e-02 3.12424779e-01 3.70163083e-01 9.94769335... | [8.574851036071777, -2.1358020305633545] |
f7036e1b-9ed7-4f07-bfa7-428a31834e03 | noise-aware-unsupervised-deep-lidar-stereo | 1904.03868 | null | http://arxiv.org/abs/1904.03868v1 | http://arxiv.org/pdf/1904.03868v1.pdf | Noise-Aware Unsupervised Deep Lidar-Stereo Fusion | In this paper, we present LidarStereoNet, the first unsupervised Lidar-stereo
fusion network, which can be trained in an end-to-end manner without the need
of ground truth depth maps. By introducing a novel "Feedback Loop'' to connect
the network input with output, LidarStereoNet could tackle both noisy Lidar
points an... | ['Pan Ji', 'Yiran Zhong', 'Yuchao Dao', 'Xuelian Cheng', 'Hongdong Li'] | 2019-04-08 | noise-aware-unsupervised-deep-lidar-stereo-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Cheng_Noise-Aware_Unsupervised_Deep_Lidar-Stereo_Fusion_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Cheng_Noise-Aware_Unsupervised_Deep_Lidar-Stereo_Fusion_CVPR_2019_paper.pdf | cvpr-2019-6 | ['stereo-matching'] | ['computer-vision'] | [ 3.18112552e-01 1.59306660e-01 1.07271463e-01 -7.27221310e-01
-8.12271237e-01 -5.05502641e-01 5.89679658e-01 7.41338581e-02
-5.64516544e-01 6.43114388e-01 7.04768747e-02 -1.51812598e-01
6.29945938e-03 -9.25364733e-01 -9.12293673e-01 -1.72243431e-01
3.39611232e-01 6.36469007e-01 3.59000117e-01 -8.90807584... | [8.22388744354248, -2.8222124576568604] |
3b646418-fa2c-42e8-8a67-16cdff051c0a | linking-graph-entities-with-multiplicity-and | 1908.04464 | null | https://arxiv.org/abs/1908.04464v2 | https://arxiv.org/pdf/1908.04464v2.pdf | Linking Graph Entities with Multiplicity and Provenance | Entity linking and resolution is a fundamental database problem with applications in data integration, data cleansing, information retrieval, knowledge fusion, and knowledge-base population. It is the task of accurately identifying multiple, differing, and possibly contradicting representations of the same real-world e... | ['Michael Bewong', 'Selasi Kwashie', 'Lin Liu', 'Jiuyong Li', 'Jixue Liu'] | 2019-08-13 | null | null | null | null | ['knowledge-base-population'] | ['natural-language-processing'] | [-5.07630289e-01 1.06923141e-01 -2.56893128e-01 -1.97883561e-01
-2.54441410e-01 -5.12304068e-01 6.51295781e-01 1.25539911e+00
-2.53414840e-01 9.74759042e-01 6.89457655e-02 1.29108071e-01
-5.27442515e-01 -1.25849390e+00 -3.23436052e-01 -2.23669276e-01
-2.86231041e-01 5.86118519e-01 9.36332405e-01 -1.61722094... | [9.155673027038574, 7.846449851989746] |
054af165-ccd7-4b64-9e3e-c198d6fc3f86 | improving-description-based-person-re | 1906.09610 | null | https://arxiv.org/abs/1906.09610v1 | https://arxiv.org/pdf/1906.09610v1.pdf | Improving Description-based Person Re-identification by Multi-granularity Image-text Alignments | Description-based person re-identification (Re-id) is an important task in video surveillance that requires discriminative cross-modal representations to distinguish different people. It is difficult to directly measure the similarity between images and descriptions due to the modality heterogeneity (the cross-modal pr... | ['Kai Niu', 'Liang Wang', 'Yan Huang', 'Wanli Ouyang'] | 2019-06-23 | null | null | null | null | ['nlp-based-person-retrival'] | ['computer-vision'] | [ 1.17546305e-01 -4.03501272e-01 -6.12399876e-02 -4.14370865e-01
-8.73715281e-01 -3.39392185e-01 9.08526361e-01 1.00434855e-01
-5.74997187e-01 4.26200122e-01 4.07750189e-01 1.94005355e-01
-2.00147986e-01 -6.16976380e-01 -4.05174762e-01 -8.88051569e-01
1.58264980e-01 5.51587045e-01 4.12375689e-01 -1.63505152... | [14.699137687683105, 0.8866704106330872] |
64af894b-a514-4678-a30c-29ddd8251792 | learning-to-detect-3d-reflection-symmetry-for | 2006.10042 | null | https://arxiv.org/abs/2006.10042v1 | https://arxiv.org/pdf/2006.10042v1.pdf | Learning to Detect 3D Reflection Symmetry for Single-View Reconstruction | 3D reconstruction from a single RGB image is a challenging problem in computer vision. Previous methods are usually solely data-driven, which lead to inaccurate 3D shape recovery and limited generalization capability. In this work, we focus on object-level 3D reconstruction and present a geometry-based end-to-end deep ... | ['Yi Ma', 'Yichao Zhou', 'Shichen Liu'] | 2020-06-17 | null | null | null | null | ['single-view-3d-reconstruction'] | ['computer-vision'] | [ 1.25529006e-01 -1.58375710e-01 1.16619788e-01 -4.01533753e-01
-7.05414772e-01 -6.21528327e-01 4.94003296e-01 -2.96192378e-01
-1.60019815e-01 2.94491053e-02 1.02427393e-01 -3.18816245e-01
7.00915754e-02 -6.79688096e-01 -1.00118566e+00 -3.14160645e-01
4.47177410e-01 7.78717339e-01 2.08591193e-01 -5.25762178... | [8.550950050354004, -2.872652292251587] |
d5a237d9-d118-446f-9523-5cbc4a028a58 | differentially-private-decision-trees-with | 2305.15394 | null | https://arxiv.org/abs/2305.15394v1 | https://arxiv.org/pdf/2305.15394v1.pdf | Differentially-Private Decision Trees with Probabilistic Robustness to Data Poisoning | Decision trees are interpretable models that are well-suited to non-linear learning problems. Much work has been done on extending decision tree learning algorithms with differential privacy, a system that guarantees the privacy of samples within the training data. However, current state-of-the-art algorithms for this ... | ['Sicco Verwer', 'Zekeriya Erkin', 'Tianyu Li', 'Jelle Vos', 'Daniël Vos'] | 2023-05-24 | null | null | null | null | ['data-poisoning'] | ['adversarial'] | [ 1.84288114e-01 3.86888057e-01 -4.82814938e-01 -7.87062883e-01
-1.13576114e+00 -9.05276477e-01 2.05170020e-01 4.72366571e-01
-4.01426554e-01 7.96226561e-01 -2.48032883e-01 -8.64292860e-01
-1.05429284e-01 -1.22894478e+00 -6.19075298e-01 -1.14886796e+00
-1.83486074e-01 4.78579044e-01 7.36711323e-02 2.83457458... | [5.936432838439941, 6.887723445892334] |
0d8d6927-f8c5-4eee-8a8d-40ae1179b2f2 | emotion-cause-pair-extraction-in-customer | 2112.03984 | null | https://arxiv.org/abs/2112.03984v1 | https://arxiv.org/pdf/2112.03984v1.pdf | Emotion-Cause Pair Extraction in Customer Reviews | Emotion-Cause Pair Extraction (ECPE) is a complex yet popular area in Natural Language Processing due to its importance and potential applications in various domains. In this report , we aim to present our work in ECPE in the domain of online reviews. With a manually annotated dataset, we explore an algorithm to extrac... | ['Wyatt Pease', 'Nathan Johns', 'Aishwarya Kaliki', 'Jeel Tejaskumar Vaishnav', 'Arpit Mittal'] | 2021-12-07 | null | null | null | null | ['emotion-cause-pair-extraction'] | ['natural-language-processing'] | [ 1.28190488e-01 4.25040781e-01 -2.82306254e-01 -7.42411435e-01
-6.05415642e-01 -4.48861480e-01 4.30166841e-01 4.38005090e-01
-7.51413226e-01 6.25505030e-01 4.09480691e-01 -2.55497266e-02
3.60586122e-02 -4.66791600e-01 -3.28082561e-01 -1.68814704e-01
-1.04979709e-01 1.73209980e-01 -6.21440768e-01 -1.38138562... | [12.694828033447266, 6.2060699462890625] |
d409ce8f-9a36-48eb-a3c9-8c882277417b | iseeu2-visually-interpretable-icu-mortality | 2005.09284 | null | https://arxiv.org/abs/2005.09284v1 | https://arxiv.org/pdf/2005.09284v1.pdf | ISeeU2: Visually Interpretable ICU mortality prediction using deep learning and free-text medical notes | Accurate mortality prediction allows Intensive Care Units (ICUs) to adequately benchmark clinical practice and identify patients with unexpected outcomes. Traditionally, simple statistical models have been used to assess patient death risk, many times with sub-optimal performance. On the other hand deep learning holds ... | ['William Caicedo-Torres', 'Jairo Gutierrez'] | 2020-05-19 | null | null | null | null | ['icu-mortality'] | ['medical'] | [-1.50919169e-01 4.15058911e-01 -5.41086681e-02 -3.78613651e-01
-5.98744869e-01 -1.77642301e-01 9.36288312e-02 8.14236283e-01
-2.66930759e-01 8.54495466e-01 7.66702116e-01 -8.26965034e-01
-3.00429851e-01 -5.94013035e-01 -1.79879963e-02 -4.06793773e-01
-3.27071130e-01 8.22270274e-01 -5.31668305e-01 5.77423647... | [8.085128784179688, 6.0986762046813965] |
d70f9bf0-6305-406e-9276-e2fb4e574b75 | dcil-deep-contextual-internal-learning-for | 1912.04229 | null | https://arxiv.org/abs/1912.04229v1 | https://arxiv.org/pdf/1912.04229v1.pdf | DCIL: Deep Contextual Internal Learning for Image Restoration and Image Retargeting | Recently, there is a vast interest in developing methods which are independent of the training samples such as deep image prior, zero-shot learning, and internal learning. The methods above are based on the common goal of maximizing image features learning from a single image despite inherent technical diversity. In th... | ['Shanmuganathan Raman', 'Indra Deep Mastan'] | 2019-12-09 | null | null | null | null | ['image-retargeting'] | ['computer-vision'] | [ 8.68606985e-01 -4.44028936e-02 -1.35691449e-01 -1.11234456e-01
-9.71963465e-01 -2.18754664e-01 7.15763807e-01 -1.99558198e-01
-3.28224033e-01 6.27873778e-01 5.27753234e-01 2.13804826e-01
-2.13993028e-01 -7.03506768e-01 -8.37701559e-01 -8.50662589e-01
4.99340177e-01 -1.43484131e-01 2.95236588e-01 -2.79388785... | [11.201550483703613, -1.9918873310089111] |
b3f4852c-cea5-4544-96f7-6760ac21bb36 | toward-face-biometric-de-identification-using | 2302.03657 | null | https://arxiv.org/abs/2302.03657v1 | https://arxiv.org/pdf/2302.03657v1.pdf | Toward Face Biometric De-identification using Adversarial Examples | The remarkable success of face recognition (FR) has endangered the privacy of internet users particularly in social media. Recently, researchers turned to use adversarial examples as a countermeasure. In this paper, we assess the effectiveness of using two widely known adversarial methods (BIM and ILLC) for de-identify... | ['Raymond Veldhuis', 'Zohra Rezgui', 'Aythami Morales', 'Ruben Vera-Rodriguez', 'Luis Felipe Gomez', 'Julian Fierrez', 'Mahdi Ghafourian'] | 2023-02-07 | null | null | null | null | ['de-identification'] | ['natural-language-processing'] | [ 5.50812244e-01 4.21467960e-01 4.67050254e-01 -1.70751780e-01
-3.93071622e-01 -1.15158081e+00 8.31027448e-01 -4.28616464e-01
-3.12453657e-01 8.10245991e-01 -1.99633658e-01 -4.65067267e-01
-3.27219963e-02 -7.36517012e-01 -7.84771860e-01 -6.26663327e-01
-2.43921980e-01 -2.15675637e-01 -2.73933917e-01 -1.52305320... | [12.874418258666992, 1.0355737209320068] |
ad20f423-f714-40dd-8c57-3aabc68e77a4 | exploration-based-language-learning-for-text-1 | 2001.08868 | null | https://arxiv.org/abs/2001.08868v2 | https://arxiv.org/pdf/2001.08868v2.pdf | Exploration Based Language Learning for Text-Based Games | This work presents an exploration and imitation-learning-based agent capable of state-of-the-art performance in playing text-based computer games. Text-based computer games describe their world to the player through natural language and expect the player to interact with the game using text. These games are of interest... | ['Adrien Ecoffet', 'Piero Molino', 'Mahdi Namazifar', 'Alexandros Papangelis', 'Joost Huizinga', 'Huaixiu Zheng', 'Dian Yu', 'Andrea Madotto', 'Gokhan Tur', 'Chandra Khatri'] | 2020-01-24 | null | https://openreview.net/forum?id=BygSXCNFDB | https://openreview.net/pdf?id=BygSXCNFDB | null | ['text-based-games'] | ['playing-games'] | [ 2.86782961e-02 2.35354707e-01 2.20652111e-02 2.70096809e-01
-4.51945215e-01 -7.91182041e-01 8.62698734e-01 -4.34601940e-02
-7.52399743e-01 8.43598843e-01 -1.93695843e-01 -5.41162074e-01
-2.38161251e-01 -1.20889568e+00 -7.33066738e-01 -4.66477722e-01
-9.32980403e-02 1.12105572e+00 4.48745191e-01 -7.98231125... | [3.8117687702178955, 1.480571985244751] |
e189fad4-262c-4355-a793-fabdbff19b4e | egocentric-video-task-translation | 2212.06301 | null | https://arxiv.org/abs/2212.06301v2 | https://arxiv.org/pdf/2212.06301v2.pdf | Egocentric Video Task Translation | Different video understanding tasks are typically treated in isolation, and even with distinct types of curated data (e.g., classifying sports in one dataset, tracking animals in another). However, in wearable cameras, the immersive egocentric perspective of a person engaging with the world around them presents an inte... | ['Lorenzo Torresani', 'Kristen Grauman', 'Yale Song', 'Zihui Xue'] | 2022-12-13 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Xue_Egocentric_Video_Task_Translation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Xue_Egocentric_Video_Task_Translation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-understanding'] | ['computer-vision'] | [ 2.70645946e-01 -1.23218998e-01 -3.69525328e-02 -2.39762694e-01
-6.54995561e-01 -9.08211827e-01 7.32551873e-01 -3.98757160e-01
-3.37457716e-01 4.37788129e-01 6.36211038e-01 -3.08426414e-02
-3.66492532e-02 -3.60145211e-01 -1.14498889e+00 -2.84168839e-01
1.51394457e-02 3.98868322e-01 1.10413238e-01 -9.35521871... | [8.648500442504883, 0.5409883856773376] |
ba33bcc0-c72a-46e2-b2a6-ca385756e8ae | multi-view-class-incremental-learning | 2306.09675 | null | https://arxiv.org/abs/2306.09675v1 | https://arxiv.org/pdf/2306.09675v1.pdf | Multi-View Class Incremental Learning | Multi-view learning (MVL) has gained great success in integrating information from multiple perspectives of a dataset to improve downstream task performance. To make MVL methods more practical in an open-ended environment, this paper investigates a novel paradigm called multi-view class incremental learning (MVCIL), wh... | ['Zhigang Zeng', 'Cheng Lian', 'Kenji Kawaguchi', 'Junwei Chen', 'Tianqi Wang', 'Depeng Li'] | 2023-06-16 | null | null | null | null | ['class-incremental-learning', 'multi-view-learning', 'incremental-learning'] | ['computer-vision', 'computer-vision', 'methodology'] | [ 3.35663259e-01 -2.14414999e-01 -4.23644900e-01 -2.26170167e-01
-5.83191037e-01 -5.83172798e-01 5.08742571e-01 1.75546408e-01
-1.57508090e-01 6.52478635e-01 1.98024958e-01 8.00258741e-02
-3.52587849e-01 -5.81657767e-01 -4.77496386e-01 -9.09840286e-01
-3.70391421e-02 2.24547192e-01 3.03465605e-01 1.97231919... | [9.81478214263916, 3.42516827583313] |
a02c39ab-79d5-4875-80eb-bcd9da2ab0d6 | omnidirectional-dso-direct-sparse-odometry | 1808.02775 | null | http://arxiv.org/abs/1808.02775v1 | http://arxiv.org/pdf/1808.02775v1.pdf | Omnidirectional DSO: Direct Sparse Odometry with Fisheye Cameras | We propose a novel real-time direct monocular visual odometry for
omnidirectional cameras. Our method extends direct sparse odometry (DSO) by
using the unified omnidirectional model as a projection function, which can be
applied to fisheye cameras with a field-of-view (FoV) well above 180 degrees.
This formulation allo... | ['Daniel Cremers', 'Jörg Stückler', 'Hidenobu Matsuki', 'Lukas von Stumberg', 'Vladyslav Usenko'] | 2018-08-08 | null | null | null | null | ['monocular-visual-odometry'] | ['robots'] | [-2.67415851e-01 5.42229749e-02 -1.48188815e-01 -1.56400546e-01
5.94480410e-02 -6.07122481e-01 7.35973120e-01 -5.62337160e-01
-6.74781919e-01 5.59692621e-01 2.17235968e-01 -2.17846021e-01
1.54694393e-01 -5.79094470e-01 -6.58257365e-01 -5.60973287e-01
4.86062258e-01 5.90693533e-01 3.83099914e-01 -1.88852817... | [7.710915565490723, -2.2315902709960938] |
b7838695-3659-4e37-a900-42738092c51e | improving-block-based-compensated-wavelet | 2212.04330 | null | https://arxiv.org/abs/2212.04330v1 | https://arxiv.org/pdf/2212.04330v1.pdf | Improving block-based compensated wavelet lifting by reconstructing unconnected pixels | This paper presents a new approach for improving the visual quality of the lowpass band of a compensated wavelet transform. A high quality of the lowpass band is very important as it can then be used as a downscaled version of the original signal. To adapt the transform to the signal, compensation methods can be implem... | ['André Kaup', 'Jürgen Seiler', 'Wolfgang Schnurrer'] | 2022-12-08 | null | null | null | null | ['motion-compensation'] | ['computer-vision'] | [ 6.20199561e-01 -7.75867179e-02 -1.44972861e-01 -2.84283608e-01
-5.10414004e-01 -1.74693361e-01 1.02658831e-02 9.99012496e-03
-5.88642180e-01 8.76215100e-01 3.10145885e-01 -1.81064382e-01
2.81647861e-01 -9.10655856e-01 -6.00282311e-01 -5.80463171e-01
1.39014497e-01 -4.67049956e-01 8.71240258e-01 -3.12238991... | [11.404601097106934, -2.3172309398651123] |
3e483472-b5d0-48b3-acc7-f5f711ced728 | adamae-adaptive-masking-for-efficient | 2211.09120 | null | https://arxiv.org/abs/2211.09120v1 | https://arxiv.org/pdf/2211.09120v1.pdf | AdaMAE: Adaptive Masking for Efficient Spatiotemporal Learning with Masked Autoencoders | Masked Autoencoders (MAEs) learn generalizable representations for image, text, audio, video, etc., by reconstructing masked input data from tokens of the visible data. Current MAE approaches for videos rely on random patch, tube, or frame-based masking strategies to select these tokens. This paper proposes AdaMAE, an ... | ['Vishal M. Patel', 'Motilal Agrawal', 'Mehdi Nikkhah', 'Ali Gholami', 'Naman Patel', 'Wele Gedara Chaminda Bandara'] | 2022-11-16 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Bandara_AdaMAE_Adaptive_Masking_for_Efficient_Spatiotemporal_Learning_With_Masked_Autoencoders_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Bandara_AdaMAE_Adaptive_Masking_for_Efficient_Spatiotemporal_Learning_With_Masked_Autoencoders_CVPR_2023_paper.pdf | cvpr-2023-1 | ['action-classification'] | ['computer-vision'] | [ 3.53368223e-01 2.19771892e-01 -3.59888017e-01 -7.36729056e-02
-9.81343865e-01 -1.86433703e-01 5.75270474e-01 -4.13118005e-01
-6.37614250e-01 6.98086500e-01 2.68098533e-01 6.20713644e-02
4.09464896e-01 -4.60020810e-01 -1.13541996e+00 -9.00177062e-01
-3.16340685e-01 5.04331887e-02 2.86121279e-01 -2.43034363... | [9.305830955505371, 1.0508308410644531] |
f5374584-3192-49fe-b563-29bf6d8812d1 | video-summarization-through-human-detection | 1901.10713 | null | https://arxiv.org/abs/1901.10713v2 | https://arxiv.org/pdf/1901.10713v2.pdf | A Mobile Robot Generating Video Summaries of Seniors' Indoor Activities | We develop a system which generates summaries from seniors' indoor-activity videos captured by a social robot to help remote family members know their seniors' daily activities at home. Unlike the traditional video summarization datasets, indoor videos captured from a moving robot poses additional challenges, namely, (... | ['Jane Yung-jen Hsu', 'Chih-Yuan Yang', 'Heeseung Yun', 'Srenavis Varadaraj'] | 2019-01-30 | null | null | null | null | ['person-identification'] | ['computer-vision'] | [ 3.54664356e-01 2.03262180e-01 -2.33162656e-01 -2.23585427e-01
-8.73618186e-01 -4.46634054e-01 1.59100816e-01 5.46932518e-02
-2.08359435e-01 1.02609575e+00 7.98172891e-01 7.26576388e-01
2.89415959e-02 -2.57967591e-01 -6.00393355e-01 -5.70326328e-01
-6.35949150e-02 4.19970632e-01 2.94179946e-01 6.92165596... | [8.152048110961914, 0.3767518699169159] |
7b1d5248-dc1b-4e82-a14e-f74778566e62 | spatial-gradient-consistency-for-unsupervised | 2302.10927 | null | https://arxiv.org/abs/2302.10927v1 | https://arxiv.org/pdf/2302.10927v1.pdf | Spatial gradient consistency for unsupervised learning of hyperspectral demosaicking: Application to surgical imaging | Hyperspectral imaging has the potential to improve intraoperative decision making if tissue characterisation is performed in real-time and with high-resolution. Hyperspectral snapshot mosaic sensors offer a promising approach due to their fast acquisition speed and compact size. However, a demosaicking algorithm is req... | ['Tom Vercauteren', 'Jonathan Shapey', 'Oscar MacCormac', 'Conor Horgan', 'Muhammad Asad', 'Peichao Li'] | 2023-02-21 | null | null | null | null | ['demosaicking'] | ['computer-vision'] | [ 8.07823777e-01 -8.26525241e-02 -3.94524708e-02 -1.33622572e-01
-8.46947849e-01 -1.57861292e-01 1.04166470e-01 -5.61714359e-02
-5.97882926e-01 6.86121941e-01 4.59743068e-02 -3.59230727e-01
-6.14273787e-01 -5.82461894e-01 -5.65418959e-01 -1.29960406e+00
-6.67875335e-02 1.67187318e-01 -6.40864074e-01 -1.87279388... | [10.267462730407715, -2.12937593460083] |
73f77f28-78f1-477a-894f-8a0fc2a237df | neural-collapse-inspired-feature-classifier-1 | 2302.03004 | null | https://arxiv.org/abs/2302.03004v1 | https://arxiv.org/pdf/2302.03004v1.pdf | Neural Collapse Inspired Feature-Classifier Alignment for Few-Shot Class Incremental Learning | Few-shot class-incremental learning (FSCIL) has been a challenging problem as only a few training samples are accessible for each novel class in the new sessions. Finetuning the backbone or adjusting the classifier prototypes trained in the prior sessions would inevitably cause a misalignment between the feature and cl... | ['DaCheng Tao', 'Philip Torr', 'Zhouchen Lin', 'Xiangtai Li', 'Haobo Yuan', 'Yibo Yang'] | 2023-02-06 | null | null | null | null | ['class-incremental-learning', 'few-shot-class-incremental-learning'] | ['computer-vision', 'methodology'] | [ 5.22307269e-02 7.59617463e-02 -1.21471487e-01 -3.75509262e-01
-2.50544876e-01 -3.73940796e-01 4.24734384e-01 3.96880247e-02
-3.97309095e-01 8.71213317e-01 -2.26229697e-01 3.42713565e-01
-1.78961411e-01 -6.02726698e-01 -1.00263917e+00 -1.19022954e+00
5.30048199e-02 5.67084670e-01 5.29315531e-01 8.79813880... | [9.78442096710205, 3.3367199897766113] |
5d15cb4b-cf78-4746-863e-81d49be9dd66 | cell-detection-in-microscopy-images-with-deep | 1708.03307 | null | http://arxiv.org/abs/1708.03307v3 | http://arxiv.org/pdf/1708.03307v3.pdf | Cell Detection in Microscopy Images with Deep Convolutional Neural Network and Compressed Sensing | The ability to automatically detect certain types of cells or cellular
subunits in microscopy images is of significant interest to a wide range of
biomedical research and clinical practices. Cell detection methods have evolved
from employing hand-crafted features to deep learning-based techniques. The
essential idea of... | ['Nilanjan Ray', 'Yao Xue'] | 2017-08-10 | null | null | null | null | ['cell-detection'] | ['computer-vision'] | [ 6.67689979e-01 -1.33036882e-01 -9.79464352e-02 -9.18133184e-02
-6.78423524e-01 -2.41920322e-01 4.86658037e-01 2.56954461e-01
-5.26783109e-01 7.80090749e-01 -2.17204422e-01 -2.22456399e-02
2.32811213e-01 -6.53303742e-01 -6.32103086e-01 -1.21924782e+00
-2.31387746e-02 1.06566608e-01 1.69818446e-01 2.12960139... | [14.72002124786377, -3.1635472774505615] |
65ac809e-c35a-4daf-8a45-4a927e0d6eee | towards-low-latency-energy-efficient-deep | 2107.12445 | null | https://arxiv.org/abs/2107.12445v1 | https://arxiv.org/pdf/2107.12445v1.pdf | Towards Low-Latency Energy-Efficient Deep SNNs via Attention-Guided Compression | Deep spiking neural networks (SNNs) have emerged as a potential alternative to traditional deep learning frameworks, due to their promise to provide increased compute efficiency on event-driven neuromorphic hardware. However, to perform well on complex vision applications, most SNN training frameworks yield large infer... | ['Peter A. Beerel', 'Massoud Pedram', 'Gourav Datta', 'Souvik Kundu'] | 2021-07-16 | null | null | null | null | ['sparse-learning'] | ['methodology'] | [ 4.48988020e-01 -1.38274193e-01 8.49957764e-02 -1.98267415e-01
-5.22304296e-01 -2.38930523e-01 3.45780730e-01 1.61602676e-01
-8.41157734e-01 8.64363194e-01 -2.55151689e-01 -2.31828213e-01
-1.55843586e-01 -7.74884284e-01 -9.85628724e-01 -7.85554290e-01
1.63403839e-01 9.62374881e-02 4.61571604e-01 2.23382935... | [8.234722137451172, 2.503411054611206] |
40f27020-d5ef-4cf3-b23c-9ce8c69c39cd | multi-task-identification-of-entities | 1808.09602 | null | http://arxiv.org/abs/1808.09602v1 | http://arxiv.org/pdf/1808.09602v1.pdf | Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction | We introduce a multi-task setup of identifying and classifying entities,
relations, and coreference clusters in scientific articles. We create SciERC, a
dataset that includes annotations for all three tasks and develop a unified
framework called Scientific Information Extractor (SciIE) for with shared span
representati... | ['Hannaneh Hajishirzi', 'Mari Ostendorf', 'Yi Luan', 'Luheng He'] | 2018-08-29 | multi-task-identification-of-entities-1 | https://aclanthology.org/D18-1360 | https://aclanthology.org/D18-1360.pdf | emnlp-2018-10 | ['joint-entity-and-relation-extraction'] | ['natural-language-processing'] | [-7.33574033e-02 4.28540945e-01 -7.04280198e-01 -2.16841057e-01
-1.28225827e+00 -9.99311566e-01 6.49787962e-01 6.24418020e-01
-2.21081167e-01 9.76662219e-01 5.17393708e-01 -4.32828158e-01
-5.66852152e-01 -5.46387672e-01 -9.38371778e-01 -2.49386489e-01
9.08569470e-02 7.16925561e-01 -7.62116686e-02 2.48502538... | [9.035056114196777, 8.4945707321167] |
92dc5b33-8235-44e8-ae97-7d387f669013 | nettailor-tuning-the-architecture-not-just-1 | 1907.00274 | null | https://arxiv.org/abs/1907.00274v1 | https://arxiv.org/pdf/1907.00274v1.pdf | NetTailor: Tuning the Architecture, Not Just the Weights | Real-world applications of object recognition often require the solution of multiple tasks in a single platform. Under the standard paradigm of network fine-tuning, an entirely new CNN is learned per task, and the final network size is independent of task complexity. This is wasteful, since simple tasks require smaller... | ['Pedro Morgado', 'Nuno Vasconcelos'] | 2019-06-29 | nettailor-tuning-the-architecture-not-just | http://openaccess.thecvf.com/content_CVPR_2019/html/Morgado_NetTailor_Tuning_the_Architecture_Not_Just_the_Weights_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Morgado_NetTailor_Tuning_the_Architecture_Not_Just_the_Weights_CVPR_2019_paper.pdf | cvpr-2019-6 | ['traffic-sign-recognition'] | ['computer-vision'] | [ 4.22336847e-01 9.43192318e-02 4.70647663e-02 -4.02861565e-01
-1.06786653e-01 -4.77184117e-01 3.45586747e-01 -2.77135700e-01
-6.87467396e-01 7.31684923e-01 -2.80025840e-01 -7.92194977e-02
-8.72144997e-02 -6.76102519e-01 -9.37373817e-01 -7.02858210e-01
4.31505948e-01 2.70969063e-01 5.14993012e-01 2.17121877... | [9.08887004852295, 3.028538703918457] |
92eb48dd-a32a-43ea-9a22-8e634bba03b5 | localised-generative-flows | null | null | https://openreview.net/forum?id=SyegvgHtwr | https://openreview.net/pdf?id=SyegvgHtwr | Localised Generative Flows | We argue that flow-based density models based on continuous bijections are limited in their ability to learn target distributions with complicated topologies, and propose localised generative flows (LGFs) to address this problem. LGFs are composed of stacked continuous mixtures of bijections, which enables each bijecti... | ['Arnaud Doucet', 'George Deligiannidis', 'Anthony Caterini', 'Rob Cornish'] | 2019-09-25 | null | null | null | null | ['normalising-flows'] | ['methodology'] | [-3.57069910e-01 3.29184420e-02 -4.23554510e-01 -2.37193421e-01
-5.91474295e-01 -6.94375277e-01 1.21686625e+00 -4.28728580e-01
-7.97397271e-02 1.13368976e+00 3.82555485e-01 -4.07413125e-01
-3.21969211e-01 -1.01481366e+00 -7.86067009e-01 -6.25007927e-01
-2.53395647e-01 8.95319641e-01 4.46093649e-01 2.27326840... | [7.071974754333496, 3.904134511947632] |
0ffd7819-a257-4463-a08e-6913d447abd9 | a-modulation-domain-loss-for-neural-network-1 | 2102.07330 | null | https://arxiv.org/abs/2102.07330v1 | https://arxiv.org/pdf/2102.07330v1.pdf | A Modulation-Domain Loss for Neural-Network-based Real-time Speech Enhancement | We describe a modulation-domain loss function for deep-learning-based speech enhancement systems. Learnable spectro-temporal receptive fields (STRFs) were adapted to optimize for a speaker identification task. The learned STRFs were then used to calculate a weighted mean-squared error (MSE) in the modulation domain for... | [] | 2021-02-15 | a-modulation-domain-loss-for-neural-network | https://arxiv.org/pdf/2102.07330.pdf | https://arxiv.org/pdf/2102.07330.pdf | null | ['speaker-identification', 'speech-denoising'] | ['speech', 'speech'] | [ 4.49688196e-01 3.95537689e-02 3.15529346e-01 -9.30178821e-01
-1.14095640e+00 4.95285401e-03 3.55035216e-01 -1.66019663e-01
-5.20519376e-01 6.08419597e-01 3.99261415e-01 -3.45647782e-01
-2.34560445e-01 -4.30944115e-01 -4.91265148e-01 -7.84083247e-01
-4.40895140e-01 -5.32773495e-01 -2.38494836e-02 -3.53984565... | [14.986944198608398, 5.932658672332764] |
9c25e410-7f82-4b50-b674-542fbdb75fbf | ru-net-regularized-unrolling-network-for | 2205.01297 | null | https://arxiv.org/abs/2205.01297v1 | https://arxiv.org/pdf/2205.01297v1.pdf | RU-Net: Regularized Unrolling Network for Scene Graph Generation | Scene graph generation (SGG) aims to detect objects and predict the relationships between each pair of objects. Existing SGG methods usually suffer from several issues, including 1) ambiguous object representations, as graph neural network-based message passing (GMP) modules are typically sensitive to spurious inter-no... | ['DaCheng Tao', 'Yibing Zhan', 'Jing Zhang', 'Changxing Ding', 'Xin Lin'] | 2022-05-03 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Lin_RU-Net_Regularized_Unrolling_Network_for_Scene_Graph_Generation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Lin_RU-Net_Regularized_Unrolling_Network_for_Scene_Graph_Generation_CVPR_2022_paper.pdf | cvpr-2022-1 | ['scene-graph-generation'] | ['computer-vision'] | [ 2.87067235e-01 1.10837400e-01 -1.87549844e-01 -3.91448736e-01
-3.25311452e-01 -7.92082027e-02 3.93231362e-01 1.85932219e-01
1.47084132e-01 4.99904245e-01 1.07335486e-01 -7.93836825e-03
-3.26786757e-01 -9.44907248e-01 -7.10734010e-01 -5.35975993e-01
-1.43796146e-01 2.23483101e-01 1.91980138e-01 -1.37758121... | [7.461837291717529, 6.009706020355225] |
1946cd7d-a1e2-41e3-a686-6ad4f3083a88 | thy-friend-is-my-friend-iterative | null | null | http://papers.nips.cc/paper/7057-thy-friend-is-my-friend-iterative-collaborative-filtering-for-sparse-matrix-estimation | http://papers.nips.cc/paper/7057-thy-friend-is-my-friend-iterative-collaborative-filtering-for-sparse-matrix-estimation.pdf | Thy Friend is My Friend: Iterative Collaborative Filtering for Sparse Matrix Estimation | The sparse matrix estimation problem consists of estimating the distribution of an $n\times n$ matrix $Y$, from a sparsely observed single instance of this matrix where the entries of $Y$ are independent random variables. This captures a wide array of problems; special instances include matrix completion in the contex... | ['Christina E. Lee', 'Jennifer Chayes', 'Devavrat Shah', 'Christian Borgs'] | 2017-12-01 | null | null | null | neurips-2017-12 | ['graphon-estimation'] | ['graphs'] | [ 1.47600874e-01 1.23769656e-01 -4.78080735e-02 -4.76881489e-02
-9.80757713e-01 -6.01818204e-01 -1.39283642e-01 8.92224070e-03
-4.92033541e-01 5.74055493e-01 -1.59835339e-01 -3.95109832e-01
-7.86010981e-01 -1.00091660e+00 -8.77859831e-01 -8.59873354e-01
-9.09501553e-01 6.03220642e-01 -2.65366137e-01 -1.92413688... | [6.717716217041016, 4.7385430335998535] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.