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2d5a9f57-3b7b-476b-a6de-b00e0c27bab0 | amr-parsing-with-action-pointer-transformer-1 | null | null | https://openreview.net/forum?id=X9KK-SCmKWn | https://openreview.net/pdf?id=X9KK-SCmKWn | AMR Parsing with Action-Pointer Transformer | Abstract Meaning Representation parsing belongs to a category of sentence-to-graph prediction tasks where the target graph is not explicitly linked to the sentence tokens. However, nodes or subgraphs are semantically related to subsets of the sentence tokens, and locality between words and related nodes is often preser... | ['Anonymous'] | 2020-11-24 | null | null | null | null | ['hard-attention'] | ['methodology'] | [ 3.83801460e-01 7.77489960e-01 -2.78778821e-01 -5.24666250e-01
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-3.92002054e-02 4.53717947e-01 3.16688776e-01 -4.23732489... | [10.403935432434082, 9.064424514770508] |
ea2011da-55cd-4e5c-99d0-2b2db96a7ab5 | mednext-transformer-driven-scaling-of | 2303.09975 | null | https://arxiv.org/abs/2303.09975v3 | https://arxiv.org/pdf/2303.09975v3.pdf | MedNeXt: Transformer-driven Scaling of ConvNets for Medical Image Segmentation | There has been exploding interest in embracing Transformer-based architectures for medical image segmentation. However, the lack of large-scale annotated medical datasets make achieving performances equivalent to those in natural images challenging. Convolutional networks, in contrast, have higher inductive biases and ... | ['Klaus Maier-Hein', 'Paul F. Jaeger', 'Fabian Isensee', 'Jens Petersen', 'Michael Baumgartner', 'Constantin Ulrich', 'Gregor Koehler', 'Saikat Roy'] | 2023-03-17 | null | null | null | null | ['volumetric-medical-image-segmentation'] | ['medical'] | [ 3.13262224e-01 3.19690466e-01 -1.51141644e-01 -5.93803406e-01
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863d7e6e-068d-4f69-95d5-015f8a1320bd | attention-based-multiple-instance-learning-2 | 2212.07724 | null | https://arxiv.org/abs/2212.07724v2 | https://arxiv.org/pdf/2212.07724v2.pdf | Attention-based Multiple Instance Learning for Survival Prediction on Lung Cancer Tissue Microarrays | Attention-based multiple instance learning (AMIL) algorithms have proven to be successful in utilizing gigapixel whole-slide images (WSIs) for a variety of different computational pathology tasks such as outcome prediction and cancer subtyping problems. We extended an AMIL approach to the task of survival prediction by... | ['Marc Aubreville', 'Katharina Breininger', 'Christian Schulz', 'Christoph Brochhausen-Delius', 'Tanja Niedermair', 'Jonathan Ganz', 'Lars-Henning Schmidt', 'Jonas Ammeling'] | 2022-12-15 | null | null | null | null | ['multiple-instance-learning'] | ['methodology'] | [ 4.88620251e-01 1.49184391e-01 -7.82989323e-01 -5.44939160e-01
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161e27df-8e5b-4b6e-9b36-dd959bcc1262 | low-resource-style-transfer-via-domain-1 | 2205.12475 | null | https://arxiv.org/abs/2205.12475v1 | https://arxiv.org/pdf/2205.12475v1.pdf | Low Resource Style Transfer via Domain Adaptive Meta Learning | Text style transfer (TST) without parallel data has achieved some practical success. However, most of the existing unsupervised text style transfer methods suffer from (i) requiring massive amounts of non-parallel data to guide transferring different text styles. (ii) colossal performance degradation when fine-tuning t... | ['Sujian Li', 'Yu Xia', 'Xiang Long', 'Xiangyang Li'] | 2022-05-25 | null | https://aclanthology.org/2022.naacl-main.220 | https://aclanthology.org/2022.naacl-main.220.pdf | naacl-2022-7 | ['general-knowledge', 'text-style-transfoer'] | ['miscellaneous', 'natural-language-processing'] | [ 6.41654670e-01 -3.01372409e-01 1.06894625e-02 -5.37805676e-01
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cb9cc02f-0eb7-45f8-8475-f3ed456b9e62 | analyzing-the-effect-of-global-learning-and | null | null | https://aclanthology.org/C12-2136 | https://aclanthology.org/C12-2136.pdf | Analyzing the Effect of Global Learning and Beam-Search on Transition-Based Dependency Parsing | null | ['Yue Zhang', 'Joakim Nivre'] | 2012-12-01 | analyzing-the-effect-of-global-learning-and-1 | https://aclanthology.org/C12-2136 | https://aclanthology.org/C12-2136.pdf | coling-2012-12 | ['transition-based-dependency-parsing'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.363021373748779, 3.708585262298584] |
76d77af5-f2ff-491c-82a2-45d582c8ad46 | effects-of-a-differentiating-therapy-on | 2303.04607 | null | https://arxiv.org/abs/2303.04607v1 | https://arxiv.org/pdf/2303.04607v1.pdf | Effects of a Differentiating Therapy on Cancer-Stem-Cell-Driven Tumors | The growth of many solid tumors has been found to be driven by chemo- and radiotherapy-resistant cancer stem cells (CSCs). A suitable therapeutic avenue in these cases may involve the use of a differentiating agent (DA) to force the differentiation of the CSCs and of conventional therapies to eliminate the remaining di... | ['Carlos A. Condat', 'Lucas Barberis', 'Jerónimo Fotinós'] | 2023-03-08 | null | null | null | null | ['culture'] | ['speech'] | [ 1.04056112e-01 -2.25788414e-01 -2.68101990e-01 5.34050167e-01
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1.33535787e-01 4.90683913e-01 5.43456376e-01 -4.30889070... | [5.876653671264648, 4.242072582244873] |
228a8b0e-aa5f-4a50-97b5-687c15c4f386 | cuet-nlp-tamilnlp-acl2022-multi-class-textual | null | null | https://aclanthology.org/2022.dravidianlangtech-1.31 | https://aclanthology.org/2022.dravidianlangtech-1.31.pdf | CUET-NLP@TamilNLP-ACL2022: Multi-Class Textual Emotion Detection from Social Media using Transformer | Recently, emotion analysis has gained increased attention by NLP researchers due to its various applications in opinion mining, e-commerce, comprehensive search, healthcare, personalized recommendations and online education. Developing an intelligent emotion analysis model is challenging in resource-constrained languag... | ['Mohammed Moshiul Hoque', 'Omar Sharif', 'Eftekhar Hossain', 'Golam Md. Mursalin', 'Rabeya Rabu', 'Nasehatul Mustakim'] | null | null | null | null | dravidianlangtech-acl-2022-5 | ['xlm-r'] | ['natural-language-processing'] | [-3.60729009e-01 -1.07809372e-01 -3.87064368e-02 -5.58948934e-01
-1.28345549e-01 -5.96481025e-01 5.61631203e-01 6.46867037e-01
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-1.27829671e-01 2.51647413e-01 -6.31265640e-01 -4.25608844... | [12.690899848937988, 6.202596664428711] |
aa434ece-5ae8-49c2-8260-61d83a3275af | improving-the-intra-class-long-tail-in-3d | 2210.08375 | null | https://arxiv.org/abs/2210.08375v1 | https://arxiv.org/pdf/2210.08375v1.pdf | Improving the Intra-class Long-tail in 3D Detection via Rare Example Mining | Continued improvements in deep learning architectures have steadily advanced the overall performance of 3D object detectors to levels on par with humans for certain tasks and datasets, where the overall performance is mostly driven by common examples. However, even the best performing models suffer from the most naive ... | ['Dragomir Anguelov', 'Yin Zhou', 'Charles R. Qi', 'Mahyar Najibi', 'Chiyu Max Jiang'] | 2022-10-15 | null | null | null | null | ['imbalanced-classification'] | ['miscellaneous'] | [-1.72252715e-01 -1.56843457e-02 -5.18281639e-01 -4.25650269e-01
-6.77958906e-01 -4.68995363e-01 3.44515324e-01 4.97863621e-01
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-1.82965308e-01 8.55911911e-01 2.49424741e-01 5.18156067... | [9.104073524475098, 1.214970350265503] |
62c06a0b-e2c4-432d-8c4e-2ec1df5b1569 | joint-learning-of-representations-for-web | null | null | https://aclanthology.org/2021.eacl-main.102 | https://aclanthology.org/2021.eacl-main.102.pdf | Joint Learning of Representations for Web-tables, Entities and Types using Graph Convolutional Network | Existing approaches for table annotation with entities and types either capture the structure of table using graphical models, or learn embeddings of table entries without accounting for the complete syntactic structure. We propose TabGCN, that uses Graph Convolutional Networks to capture the complete structure of tabl... | ['Indrajit Bhattacharya', 'Aniket Pramanick'] | 2021-04-01 | null | null | null | eacl-2021-2 | ['table-annotation', 'table-annotation'] | ['knowledge-base', 'natural-language-processing'] | [-3.26843798e-01 6.10943437e-01 -8.22755635e-01 -3.97247523e-01
-5.88639200e-01 -1.06353343e+00 2.90781319e-01 1.01239312e+00
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-2.76991069e-01 9.55758274e-01 2.07467720e-01 -2.90932387... | [9.527554512023926, 7.929538249969482] |
dc801d2e-d330-45c9-bea3-18e0f90e723f | encoder-decoder-with-multi-level-attention | 2109.02303 | null | https://arxiv.org/abs/2109.02303v1 | https://arxiv.org/pdf/2109.02303v1.pdf | Encoder-decoder with Multi-level Attention for 3D Human Shape and Pose Estimation | 3D human shape and pose estimation is the essential task for human motion analysis, which is widely used in many 3D applications. However, existing methods cannot simultaneously capture the relations at multiple levels, including spatial-temporal level and human joint level. Therefore they fail to make accurate predict... | ['Hongsheng Li', 'Shuai Yi', 'Jianbo Liu', 'Maoqing Tian', 'Zhengjia Li', 'Ziniu Wan'] | 2021-09-06 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Wan_Encoder-Decoder_With_Multi-Level_Attention_for_3D_Human_Shape_and_Pose_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Wan_Encoder-Decoder_With_Multi-Level_Attention_for_3D_Human_Shape_and_Pose_ICCV_2021_paper.pdf | iccv-2021-1 | ['3d-absolute-human-pose-estimation'] | ['computer-vision'] | [-4.27549064e-01 -1.06028125e-01 -1.17666461e-01 1.16639445e-02
-8.20854604e-01 3.17140035e-02 2.75547266e-01 -2.41717920e-01
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5.01824822e-03 6.65154934e-01 7.43789673e-01 -2.40134344... | [7.172220230102539, -0.6006494760513306] |
b659aee7-fcfe-453a-bff2-47e8db64df6d | multi-task-recurrent-neural-network-for-1 | 2003.04772 | null | https://arxiv.org/abs/2003.04772v1 | https://arxiv.org/pdf/2003.04772v1.pdf | Multi-Task Recurrent Neural Network for Surgical Gesture Recognition and Progress Prediction | Surgical gesture recognition is important for surgical data science and computer-aided intervention. Even with robotic kinematic information, automatically segmenting surgical steps presents numerous challenges because surgical demonstrations are characterized by high variability in style, duration and order of actions... | ['Matthew J. Clarkson', 'Danail Stoyanov', 'Beatrice van Amsterdam'] | 2020-03-10 | null | null | null | null | ['surgical-gesture-recognition'] | ['medical'] | [ 4.24162596e-01 -7.02248514e-02 -6.19892240e-01 -4.20911491e-01
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-4.64911088e-02 5.28806210e-01 -2.51639247e-01 2.65314803... | [14.057912826538086, -3.3562328815460205] |
cabe630e-3247-488b-b23e-fd6490144d91 | hiding-speaker-s-sex-in-speech-using-zero | 2211.16065 | null | https://arxiv.org/abs/2211.16065v2 | https://arxiv.org/pdf/2211.16065v2.pdf | Hiding speaker's sex in speech using zero-evidence speaker representation in an analysis/synthesis pipeline | The use of modern vocoders in an analysis/synthesis pipeline allows us to investigate high-quality voice conversion that can be used for privacy purposes. Here, we propose to transform the speaker embedding and the pitch in order to hide the sex of the speaker. ECAPA-TDNN-based speaker representation fed into a HiFiGAN... | ['Driss Matrouf', 'Jean-François Bonastre', 'Junichi Yamagishi', 'Xin Wang', 'Xiaoxiao Miao', 'Paul-Gauthier Noé'] | 2022-11-29 | null | null | null | null | ['voice-conversion', 'voice-conversion'] | ['audio', 'speech'] | [ 3.52909803e-01 5.57622910e-01 3.86221595e-02 -3.05968910e-01
-6.22288048e-01 -7.41644442e-01 5.24610043e-01 -9.69020277e-02
-4.00500000e-01 6.30712926e-01 3.28946471e-01 -3.43069017e-01
-4.52901149e-04 -4.82364893e-01 -5.97365201e-01 -9.14062619e-01
1.52427107e-01 -2.26002127e-01 -1.62635893e-01 4.56159934... | [14.028497695922852, 5.8758087158203125] |
8090067c-4b91-4410-afe0-896a65f51389 | riskyishness-and-pinocchio-s-search-for-a | 2103.03482 | null | https://arxiv.org/abs/2103.03482v2 | https://arxiv.org/pdf/2103.03482v2.pdf | Pilot Investigation for a Comprehensive Taxonomy of Autonomous Entities | This paper documents an exploratory pilot study to define the term Autonomous Entity, and any characteristics that are required to identify or classify an Autonomous Entity. Our solution builds on previous work with regard to philosophical and scientific classification methods but focuses on a novel Design Science Rese... | ['William Wagner', 'Joseph Santhosh', 'Clement Aladi', 'Anna Źakowska'] | 2021-03-05 | null | null | null | null | ['miscellaneous'] | ['miscellaneous'] | [-1.63126975e-01 3.99501055e-01 -3.20249528e-01 -1.91422626e-01
1.15683824e-02 -8.63269091e-01 9.31225836e-01 1.49977699e-01
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2.16674104e-01 3.00305516e-01 -2.41850153e-01 -1.31444693... | [9.045543670654297, 6.505204200744629] |
bd2e8792-767e-4ca3-85fe-4058baad6142 | weakly-supervised-action-localization-via | 2206.11011 | null | https://arxiv.org/abs/2206.11011v2 | https://arxiv.org/pdf/2206.11011v2.pdf | Weakly-Supervised Temporal Action Localization by Progressive Complementary Learning | Weakly Supervised Temporal Action Localization (WSTAL) aims to localize and classify action instances in long untrimmed videos with only video-level category labels. Due to the lack of snippet-level supervision for indicating action boundaries, previous methods typically assign pseudo labels for unlabeled snippets. How... | ['Ying Shan', 'Xiao-Ming Wu', 'Kun-Yu Lin', 'Jia-Run Du', 'Wei-Shi Zheng', 'Zhongang Qi', 'Fa-Ting Hong', 'Jia-Chang Feng'] | 2022-06-22 | null | null | null | null | ['weakly-supervised-action-localization', 'weakly-supervised-temporal-action', 'action-localization'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 0.54666483 -0.07386957 -0.6357929 -0.15473358 -0.54590076 -0.562608
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0.93828815 1.0048134 0.16... | [8.51686954498291, 0.6944706439971924] |
b126ef35-c5df-46c8-8598-b8aea432b839 | image-based-camera-localization-an-overview | 1610.03660 | null | http://arxiv.org/abs/1610.03660v4 | http://arxiv.org/pdf/1610.03660v4.pdf | Image Based Camera Localization: an Overview | Recently, virtual reality, augmented reality, robotics, autonomous driving et
al attract much attention of both academic and industrial community, in which
image based camera localization is a key task. However, there has not been a
complete review on image-based camera localization. It is urgent to map this
topic to h... | ['Fulin Tang', 'Heping Li', 'Yihong Wu'] | 2016-10-12 | null | null | null | null | ['camera-localization'] | ['computer-vision'] | [-1.33917242e-01 -5.50454855e-01 -3.63912821e-01 -2.68822819e-01
-3.56234848e-01 -8.27844977e-01 3.80270004e-01 3.26480865e-02
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3.36394429e-01 -2.37747580e-02 4.74658966e-01 -2.31144905... | [7.5238213539123535, -2.0815114974975586] |
d850fcd7-c4fa-4cdb-b94a-3f523264a990 | metaslrcl-a-self-adaptive-learning-rate-and | null | null | https://aclanthology.org/2022.coling-1.180 | https://aclanthology.org/2022.coling-1.180.pdf | MetaSLRCL: A Self-Adaptive Learning Rate and Curriculum Learning Based Framework for Few-Shot Text Classification | Due to the lack of labeled data in many realistic scenarios, a number of few-shot learning methods for text classification have been proposed, among which the meta learning based ones have recently attracted much attention. Such methods usually consist of a learner as the classifier and a meta learner for specializing ... | ['Xueqi Cheng', 'Jiafeng Guo', 'Saiping Guan', 'Xiaolong Jin', 'Kailin Zhao'] | null | null | null | null | coling-2022-10 | ['few-shot-text-classification'] | ['natural-language-processing'] | [ 1.16765007e-01 -3.30636680e-01 -2.39295930e-01 -4.87698585e-01
-2.74225801e-01 -1.04109861e-01 3.03296953e-01 2.88542688e-01
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5.45861781e-01 2.04545707e-01 6.71482801e-01 -4.05916840... | [10.186965942382812, 3.4960720539093018] |
98f01e9a-d5fd-4d8e-88c3-c0c1dc8c8b49 | hyppo-a-surrogate-based-multi-level | 2110.01698 | null | https://arxiv.org/abs/2110.01698v1 | https://arxiv.org/pdf/2110.01698v1.pdf | HYPPO: A Surrogate-Based Multi-Level Parallelism Tool for Hyperparameter Optimization | We present a new software, HYPPO, that enables the automatic tuning of hyperparameters of various deep learning (DL) models. Unlike other hyperparameter optimization (HPO) methods, HYPPO uses adaptive surrogate models and directly accounts for uncertainty in model predictions to find accurate and reliable models that m... | ['Marc Day', 'Mariam Kiran', 'Talita Perciano', 'Juliane Mueller', 'Vidya Ganapati', 'Chelsea Jones', 'Anuradha Trivedi', 'Casey Garner', 'Vincent Dumont'] | 2021-10-04 | null | null | null | null | ['time-series-prediction'] | ['time-series'] | [-4.24102455e-01 1.21646501e-01 1.40621126e-01 -3.76977801e-01
-9.28862453e-01 -1.47137135e-01 3.37227792e-01 3.09239715e-01
-5.85712433e-01 8.46455276e-01 1.79897342e-02 -4.52661276e-01
-2.34442949e-01 -4.96761084e-01 -4.95725572e-01 -1.02055395e+00
-2.67854303e-01 7.90145934e-01 1.85896099e-01 1.57080129... | [7.991957187652588, 3.501676559448242] |
4be9cdd5-eb85-438e-a391-be8a707c083c | getting-the-roles-right-using-framenet-in-nlp | null | null | https://aclanthology.info/papers/N15-4006/n15-4006 | https://www.aclweb.org/anthology/N15-4006 | Getting the Roles Right: Using FrameNet in NLP | null | ['Miriam R. L. Petruck', 'Collin Baker', 'Michael Ellsworth', 'Nathan Schneider'] | 2015-05-01 | null | null | null | hlt-2015-5 | ['text-annotation'] | ['natural-language-processing'] | [-2.44508207e-01 3.89024585e-01 -2.65282035e-01 -2.15905145e-01
-8.60921741e-02 -7.76765764e-01 4.48510379e-01 -7.23253429e-01
-5.48377395e-01 1.31954515e+00 3.66348401e-02 -9.49533224e-01
-2.40340635e-01 -1.05564880e+00 -8.44053447e-01 -8.75781775e-01
-7.42435038e-01 6.86515033e-01 1.44298598e-01 -6.52004302... | [-1.5391589403152466, 15.86916446685791] |
e0edc839-7d87-45ba-b075-f5691b993f6a | flexvdw-a-machine-learning-approach-to | 2303.11494 | null | https://arxiv.org/abs/2303.11494v1 | https://arxiv.org/pdf/2303.11494v1.pdf | FlexVDW: A machine learning approach to account for protein flexibility in ligand docking | Most widely used ligand docking methods assume a rigid protein structure. This leads to problems when the structure of the target protein deforms upon ligand binding. In particular, the ligand's true binding pose is often scored very unfavorably due to apparent clashes between ligand and protein atoms, which lead to ex... | ['Ron O. Dror', 'Joseph M. Paggi', 'Patricia Suriana'] | 2023-03-20 | null | null | null | null | ['pose-prediction'] | ['computer-vision'] | [ 2.78598994e-01 1.18088506e-01 -9.24097002e-02 -5.08679211e-01
-7.18034983e-01 -6.79741085e-01 1.90527916e-01 3.72576386e-01
-5.70836902e-01 1.26435554e+00 1.92473829e-01 -4.11801100e-01
3.83177847e-01 -6.99503899e-01 -1.26862228e+00 -9.06148732e-01
3.91556561e-04 8.00144196e-01 1.83465585e-01 -2.70663768... | [4.846113681793213, 5.519539833068848] |
7052190a-1544-45fa-af6b-77e9233969ee | periodicity-in-cryptocurrency-volatility-and | 2109.12142 | null | https://arxiv.org/abs/2109.12142v2 | https://arxiv.org/pdf/2109.12142v2.pdf | Periodicity in Cryptocurrency Volatility and Liquidity | We study recurrent patterns in volatility and volume for major cryptocurrencies, Bitcoin and Ether, using data from two centralized exchanges (Coinbase Pro and Binance) and a decentralized exchange (Uniswap V2). We find systematic patterns in both volatility and volume across day-of-the-week, hour-of-the-day, and withi... | ['Wade Kimbrough', 'Chan Kim', 'Peter Reinhard Hansen'] | 2021-09-24 | null | null | null | null | ['algorithmic-trading'] | ['time-series'] | [-1.07605040e+00 4.67168465e-02 -1.56629980e-01 -2.14537188e-01
-3.49697292e-01 -1.27168858e+00 1.22248530e+00 1.68997690e-01
-1.80677563e-01 9.81711805e-01 6.34506643e-01 -5.60713172e-01
-2.54906833e-01 -8.72661531e-01 -2.57647157e-01 -4.41140413e-01
-5.64146936e-01 7.96911716e-01 1.72648594e-01 -4.68587905... | [4.697022438049316, 4.106358051300049] |
411c6616-fd81-400d-9ed8-01bd37bc9256 | comparing-reinforcement-learning-and-human | 2306.17766 | null | https://arxiv.org/abs/2306.17766v1 | https://arxiv.org/pdf/2306.17766v1.pdf | Comparing Reinforcement Learning and Human Learning using the Game of Hidden Rules | Reliable real-world deployment of reinforcement learning (RL) methods requires a nuanced understanding of their strengths and weaknesses and how they compare to those of humans. Human-machine systems are becoming more prevalent and the design of these systems relies on a task-oriented understanding of both human learni... | ['Vicki Bier', 'Paul Kantor', 'Yonatan Mintz', 'Vladimir Menkov', 'Eric Pulick'] | 2023-06-30 | null | null | null | null | ['reinforcement-learning-1'] | ['methodology'] | [ 3.27643454e-02 -4.81463075e-02 -2.30217174e-01 -2.55394638e-01
-3.46451700e-01 -6.77291155e-01 7.35932410e-01 2.55334884e-01
-8.03177476e-01 7.29258120e-01 3.68009172e-02 -4.29629743e-01
-7.57060666e-03 -4.10352111e-01 -4.00606990e-01 -3.87527764e-01
-3.73578429e-01 4.42616582e-01 2.42049530e-01 -4.55628872... | [4.155524730682373, 1.6281708478927612] |
995cff51-9342-4dfd-8aa2-bc6ab650fe21 | towards-practical-lipreading-with-distilled | 2007.06504 | null | https://arxiv.org/abs/2007.06504v3 | https://arxiv.org/pdf/2007.06504v3.pdf | Towards Practical Lipreading with Distilled and Efficient Models | Lipreading has witnessed a lot of progress due to the resurgence of neural networks. Recent works have placed emphasis on aspects such as improving performance by finding the optimal architecture or improving generalization. However, there is still a significant gap between the current methodologies and the requirement... | ['Pingchuan Ma', 'Maja Pantic', 'Stavros Petridis', 'Brais Martinez'] | 2020-07-13 | null | null | null | null | ['lipreading'] | ['computer-vision'] | [ 1.85825869e-01 9.73596945e-02 -3.81843776e-01 -5.71579412e-02
-1.10741401e+00 -2.71972120e-01 3.90617460e-01 -1.09842427e-01
-5.60385406e-01 5.74305594e-01 2.79918402e-01 -4.14963901e-01
-1.10167535e-02 -2.92847157e-01 -5.72545886e-01 -4.95239496e-01
1.12999327e-01 2.38617450e-01 4.09701407e-01 -2.14972556... | [14.31668472290039, 5.030989170074463] |
2b47ba79-94a1-4810-9cb9-8c052e687d6d | example-based-explainable-ai-and-its | 2302.01526 | null | https://arxiv.org/abs/2302.01526v1 | https://arxiv.org/pdf/2302.01526v1.pdf | Example-Based Explainable AI and its Application for Remote Sensing Image Classification | We present a method of explainable artificial intelligence (XAI), "What I Know (WIK)", to provide additional information to verify the reliability of a deep learning model by showing an example of an instance in a training dataset that is similar to the input data to be inferred and demonstrate it in a remote sensing i... | ['Masao Yasui', 'Taiki Ogihara', 'Peihsuan Lin', 'Kazunari Matsunaga', 'Yasunobu Uchiyama', 'Masato Taki', 'Masato Todo', 'Shin-nosuke Ishikawa'] | 2023-02-03 | null | null | null | null | ['remote-sensing-image-classification'] | ['miscellaneous'] | [ 3.92259657e-01 4.06505495e-01 -1.07212707e-01 -7.66635418e-01
-1.28603116e-01 -2.87924707e-01 6.96971357e-01 3.20109963e-01
-6.98883552e-03 8.33072186e-01 -2.62360722e-01 -8.16575527e-01
-6.12104774e-01 -1.02903664e+00 -1.07365918e+00 -7.16146231e-01
-8.64099860e-02 7.50529170e-01 -3.23887579e-02 -5.72285801... | [8.885770797729492, 5.762767314910889] |
88296edf-7f2c-4af1-9337-06de79208468 | improving-dialog-evaluation-with-a-multi | 2009.11321 | null | https://arxiv.org/abs/2009.11321v1 | https://arxiv.org/pdf/2009.11321v1.pdf | Improving Dialog Evaluation with a Multi-reference Adversarial Dataset and Large Scale Pretraining | There is an increasing focus on model-based dialog evaluation metrics such as ADEM, RUBER, and the more recent BERT-based metrics. These models aim to assign a high score to all relevant responses and a low score to all irrelevant responses. Ideally, such models should be trained using multiple relevant and irrelevant ... | ['Mitesh M. Khapra', 'Ananya B. Sai', 'Siddhartha Arora', 'Akash Kumar Mohankumar'] | 2020-09-23 | null | null | null | null | ['dialogue-evaluation'] | ['natural-language-processing'] | [ 4.80435370e-03 1.88896079e-02 6.84335157e-02 -6.96070671e-01
-1.01664460e+00 -1.02275014e+00 9.25554395e-01 1.05759449e-01
-8.03172350e-01 8.41817558e-01 3.86619061e-01 -3.07399809e-01
1.55905876e-02 -7.03867376e-01 -2.48939171e-02 -3.25561851e-01
2.36139894e-01 8.20885777e-01 4.18646663e-01 -7.09683597... | [12.70009708404541, 8.094905853271484] |
b5b403f8-d350-447d-96e7-947a14ec0795 | bayesian-inference-and-role-of-astrocytes-in | 2306.12520 | null | https://arxiv.org/abs/2306.12520v1 | https://arxiv.org/pdf/2306.12520v1.pdf | Bayesian inference and role of astrocytes in amyloid-beta dynamics with modelling of Alzheimer's disease using clinical data | Alzheimer's disease (AD) is a prominent, worldwide, age-related neurodegenerative disease that currently has no systemic treatment. Strong evidence suggests that permeable amyloid-beta peptide (Abeta) oligomers, astrogliosis and reactive astrocytosis cause neuronal damage in AD. A large amount of Abeta is secreted by a... | ["the Alzheimer's Disease Neuroimaging Initiative", 'Roderick Melnik', 'Hina Shaheen'] | 2023-06-21 | null | null | null | null | ['bayesian-inference'] | ['methodology'] | [-6.02751710e-02 -5.26576459e-01 2.40859404e-01 3.52048478e-03
-2.92969942e-01 -3.11738729e-01 6.58481956e-01 1.91903070e-01
-5.41716039e-01 1.17680836e+00 4.12854522e-01 -2.39471316e-01
1.11723796e-01 -8.77850056e-01 -4.05006170e-01 -9.57360685e-01
-3.33553404e-01 8.31451595e-01 5.42890906e-01 5.98257817... | [14.08312702178955, -1.797318935394287] |
f553aec9-95e0-406f-b8ef-ac1e1c7646fc | towards-fair-and-decentralized-privacy | 1906.01167 | null | https://arxiv.org/abs/1906.01167v3 | https://arxiv.org/pdf/1906.01167v3.pdf | Towards Fair and Privacy-Preserving Federated Deep Models | The current standalone deep learning framework tends to result in overfitting and low utility. This problem can be addressed by either a centralized framework that deploys a central server to train a global model on the joint data from all parties, or a distributed framework that leverages a parameter server to aggrega... | ['Lingjuan Lyu', 'Kee Siong Ng', 'Karthik Nandakumar', 'Han Yu', 'Yitong Li', 'Jiong Jin', 'Xingjun Ma', 'Jiangshan Yu'] | 2019-06-04 | null | null | null | null | ['privacy-preserving-deep-learning', 'privacy-preserving-deep-learning'] | ['methodology', 'natural-language-processing'] | [-6.85684383e-01 4.68814634e-02 -2.48023197e-01 -7.86277115e-01
-9.94660556e-01 -8.14753771e-01 5.89435577e-01 9.43533238e-03
-5.29777825e-01 8.04444492e-01 9.11540166e-02 -3.01280081e-01
5.38852438e-03 -9.08661127e-01 -6.16965771e-01 -8.26761544e-01
2.31163681e-01 1.30585551e-01 -1.74338788e-01 2.53245592... | [5.855238437652588, 6.56024694442749] |
94d9e05a-b5f4-4108-af19-953034e4c67f | how-do-we-answer-complex-questions-discourse-1 | 2203.11048 | null | https://arxiv.org/abs/2203.11048v1 | https://arxiv.org/pdf/2203.11048v1.pdf | How Do We Answer Complex Questions: Discourse Structure of Long-form Answers | Long-form answers, consisting of multiple sentences, can provide nuanced and comprehensive answers to a broader set of questions. To better understand this complex and understudied task, we study the functional structure of long-form answers collected from three datasets, ELI5, WebGPT and Natural Questions. Our main go... | ['Eunsol Choi', 'Junyi Jessy Li', 'Fangyuan Xu'] | 2022-03-21 | null | https://aclanthology.org/2022.acl-long.249 | https://aclanthology.org/2022.acl-long.249.pdf | acl-2022-5 | ['natural-questions'] | ['miscellaneous'] | [ 4.55443561e-02 8.78790617e-01 -2.08090231e-01 -4.94714409e-01
-1.23144829e+00 -1.09131896e+00 7.13032067e-01 4.34179336e-01
-1.91005871e-01 1.08205700e+00 1.04972255e+00 -7.11674869e-01
-1.78327188e-01 -6.31859541e-01 -4.55605537e-01 -3.25518511e-02
3.49106878e-01 9.17325675e-01 7.12898195e-01 -7.47958481... | [11.668240547180176, 8.16215991973877] |
640e7b07-a440-4ab6-8389-2d26a2972d74 | efficient-determination-of-safety | 2307.01371 | null | https://arxiv.org/abs/2307.01371v1 | https://arxiv.org/pdf/2307.01371v1.pdf | Efficient Determination of Safety Requirements for Perception Systems | Perception systems operate as a subcomponent of the general autonomy stack, and perception system designers often need to optimize performance characteristics while maintaining safety with respect to the overall closed-loop system. For this reason, it is useful to distill high-level safety requirements into component-l... | ['Mykel J. Kochenderfer', 'Esen Yel', 'Anthony L. Corso', 'Sydney M. Katz'] | 2023-07-03 | null | null | null | null | ['gaussian-processes'] | ['methodology'] | [ 3.98479924e-02 1.82313666e-01 -8.99778977e-02 -2.13614196e-01
-6.73640370e-01 -8.59890103e-01 3.94626886e-01 1.46573499e-01
-2.48871401e-01 3.80471647e-01 -2.30039030e-01 -7.93578267e-01
-3.55689406e-01 -5.89941382e-01 -5.82200527e-01 -4.42376703e-01
-2.07196966e-01 3.10020119e-01 4.59660947e-01 -1.69626296... | [4.732146263122559, 2.0705275535583496] |
63a40134-0718-438b-b232-a5c7d1fd04dd | predicting-risk-of-dementia-with-survival | 2306.10330 | null | https://arxiv.org/abs/2306.10330v1 | https://arxiv.org/pdf/2306.10330v1.pdf | Predicting Risk of Dementia with Survival Machine Learning and Statistical Methods: Results on the English Longitudinal Study of Ageing Cohort | Machine learning models that aim to predict dementia onset usually follow the classification methodology ignoring the time until an event happens. This study presents an alternative, using survival analysis within the context of machine learning techniques. Two survival method extensions based on machine learning algor... | ['Daniel Stahl', 'Olesya Ajnakina', 'Henry Musto', 'Daniel Stamate'] | 2023-06-17 | null | null | null | null | ['survival-analysis'] | ['miscellaneous'] | [ 3.32917452e-01 8.03378969e-03 -5.13838232e-01 -4.32429641e-01
-5.65249383e-01 7.52793178e-02 5.86032867e-01 5.63889384e-01
-1.06151938e+00 1.22646976e+00 3.33860815e-01 -9.75383162e-01
-7.25647569e-01 -6.64054215e-01 -5.63728623e-02 -6.38705969e-01
-6.54624820e-01 7.82508314e-01 2.29458809e-01 -5.90518070... | [7.929276466369629, 5.346313953399658] |
da9b0ed7-05fd-48f9-b44d-50ea4024a244 | single-image-super-resolution-using-1 | null | null | http://openaccess.thecvf.com/content_cvpr_2014/html/Zhu_Single_Image_Super-resolution_2014_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2014/papers/Zhu_Single_Image_Super-resolution_2014_CVPR_paper.pdf | Single Image Super-resolution using Deformable Patches | We proposed a deformable patches based method for single image super-resolution. By the concept of deformation, a patch is not regarded as a fixed vector but a flexible deformation flow. Via deformable patches, the dictionary can cover more patterns that do not appear, thus becoming more expressive. We present the ener... | ['Alan L. Yuille', 'Yanning Zhang', 'Yu Zhu'] | 2014-06-01 | null | null | null | cvpr-2014-6 | ['patch-matching'] | ['computer-vision'] | [ 2.35198587e-01 -6.80798218e-02 6.75301105e-02 -1.22228920e-01
-5.83385468e-01 -4.90815610e-01 2.53378302e-01 -5.42283893e-01
1.53406888e-01 6.72400594e-01 4.91388500e-01 8.46530139e-01
-1.10030502e-01 -1.01219988e+00 -6.26886904e-01 -8.72258902e-01
1.93114921e-01 1.52653545e-01 6.88659370e-01 -4.74941373... | [10.997608184814453, -2.0835530757904053] |
39c8ae10-5daa-4d49-b0f9-4c05a821e2d9 | surfnet-generating-3d-shape-surfaces-using | 1703.04079 | null | http://arxiv.org/abs/1703.04079v1 | http://arxiv.org/pdf/1703.04079v1.pdf | SurfNet: Generating 3D shape surfaces using deep residual networks | 3D shape models are naturally parameterized using vertices and faces, \ie,
composed of polygons forming a surface. However, current 3D learning paradigms
for predictive and generative tasks using convolutional neural networks focus
on a voxelized representation of the object. Lifting convolution operators from
the trad... | ['Qi-Xing Huang', 'Karthik Ramani', 'Ayan Sinha', 'Asim Unmesh'] | 2017-03-12 | surfnet-generating-3d-shape-surfaces-using-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Sinha_SurfNet_Generating_3D_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Sinha_SurfNet_Generating_3D_CVPR_2017_paper.pdf | cvpr-2017-7 | ['3d-shape-generation'] | ['computer-vision'] | [ 3.16208661e-01 6.55728221e-01 3.38931620e-01 -5.59230745e-01
-5.67183137e-01 -8.31453681e-01 8.85217726e-01 -4.14476991e-01
3.36524218e-01 3.28072995e-01 5.65234423e-02 -2.11373582e-01
1.52694598e-01 -1.39799809e+00 -1.25362003e+00 -4.03596729e-01
-4.21987119e-04 1.19562829e+00 4.32212930e-03 -2.26497069... | [8.829946517944336, -3.6461172103881836] |
150354c4-3973-42dd-9d03-5f996cfd4d9b | probabilistic-learning-of-multivariate-time | 2306.09147 | null | https://arxiv.org/abs/2306.09147v2 | https://arxiv.org/pdf/2306.09147v2.pdf | Probabilistic Learning of Multivariate Time Series with Temporal Irregularity | Multivariate sequential data collected in practice often exhibit temporal irregularities, including nonuniform time intervals and component misalignment. However, if uneven spacing and asynchrony are endogenous characteristics of the data rather than a result of insufficient observation, the information content of thes... | ['Qi Wu', 'Cheuk Hang Leung', 'Yijun Li'] | 2023-06-15 | null | null | null | null | ['imputation', 'imputation', 'imputation'] | ['computer-vision', 'miscellaneous', 'time-series'] | [ 7.02872351e-02 -4.68016714e-01 -5.02241492e-01 -3.70288521e-01
-3.58537644e-01 -6.95124805e-01 7.14133263e-01 7.30919987e-02
-1.54934879e-02 8.61371934e-01 3.85414839e-01 -4.89077061e-01
-8.19561005e-01 -6.44805789e-01 -5.48641622e-01 -8.92700195e-01
-6.38147891e-01 5.89055181e-01 -1.28976136e-01 4.14320290... | [7.026886463165283, 3.9831247329711914] |
4746e249-814f-4c2f-8ba9-3ed89680174c | learning-regional-attention-over-multi | 2104.07240 | null | https://arxiv.org/abs/2104.07240v1 | https://arxiv.org/pdf/2104.07240v1.pdf | Learning Regional Attention over Multi-resolution Deep Convolutional Features for Trademark Retrieval | Large-scale trademark retrieval is an important content-based image retrieval task. A recent study shows that off-the-shelf deep features aggregated with Regional-Maximum Activation of Convolutions (R-MAC) achieve state-of-the-art results. However, R-MAC suffers in the presence of background clutter/trivial regions and... | ['Clinton Fookes', 'Sridha Sridharan', 'Simon Denman', 'Osman Tursun'] | 2021-04-15 | null | null | null | null | ['trademark-retrieval', 'content-based-image-retrieval'] | ['computer-vision', 'computer-vision'] | [-7.01617682e-04 -6.45354450e-01 2.43575454e-01 -3.85618865e-01
-1.26126242e+00 -7.31209397e-01 8.75778675e-01 2.44054452e-01
-7.09752083e-01 4.02247131e-01 2.70322412e-01 1.37365103e-01
-3.11191350e-01 -7.38726020e-01 -7.42896020e-01 -5.09186566e-01
-1.94250435e-01 -1.71374589e-01 7.81392097e-01 -3.09364855... | [10.681291580200195, 0.5900668501853943] |
4f247350-32a8-440f-a586-00b5e42d37ed | superdisco-super-class-discovery-improves | 2304.00101 | null | https://arxiv.org/abs/2304.00101v1 | https://arxiv.org/pdf/2304.00101v1.pdf | SuperDisco: Super-Class Discovery Improves Visual Recognition for the Long-Tail | Modern image classifiers perform well on populated classes, while degrading considerably on tail classes with only a few instances. Humans, by contrast, effortlessly handle the long-tailed recognition challenge, since they can learn the tail representation based on different levels of semantic abstraction, making the l... | ['Cees G. M. Snoek', 'XianTong Zhen', 'Jiayi Shen', 'Yingjun Du'] | 2023-03-31 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Du_SuperDisco_Super-Class_Discovery_Improves_Visual_Recognition_for_the_Long-Tail_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Du_SuperDisco_Super-Class_Discovery_Improves_Visual_Recognition_for_the_Long-Tail_CVPR_2023_paper.pdf | cvpr-2023-1 | ['semantic-textual-similarity', 'semantic-similarity'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.01477169e-01 1.46585718e-01 -5.04157126e-01 -8.42512131e-01
-5.99301696e-01 -3.62822950e-01 6.67511523e-01 3.91526520e-01
-1.20597944e-01 5.11180043e-01 8.42691213e-02 8.30098987e-02
-3.97988141e-01 -7.93698311e-01 -8.13005090e-01 -7.32328653e-01
3.22431587e-02 8.23019087e-01 2.09138736e-01 -1.32372394... | [9.695009231567383, 2.8592171669006348] |
cfe41fe7-ca30-4313-9b5d-3e2cf5e8f6f3 | continuous-risk-measures-for-driving-support | 2303.08007 | null | https://arxiv.org/abs/2303.08007v1 | https://arxiv.org/pdf/2303.08007v1.pdf | Continuous Risk Measures for Driving Support | In this paper, we compare three different model-based risk measures by evaluating their stengths and weaknesses qualitatively and testing them quantitatively on a set of real longitudinal and intersection scenarios. We start with the traditional heuristic Time-To-Collision (TTC), which we extend towards 2D operation an... | ['Tim Puphal', 'Julian Eggert'] | 2023-03-14 | null | null | null | null | ['survival-analysis'] | ['miscellaneous'] | [-3.61740142e-01 3.86451115e-03 3.89701165e-02 -1.30482838e-01
-9.11233842e-01 -3.29442263e-01 8.75033498e-01 6.81061924e-01
-5.91703832e-01 8.70458663e-01 1.54850096e-01 -6.55912995e-01
-8.78304005e-01 -9.22873795e-01 -4.69678551e-01 -8.09759736e-01
-6.43472075e-01 8.20310354e-01 7.18127728e-01 -2.81711400... | [5.724159240722656, 1.2862011194229126] |
52c949c9-33e3-4e74-bec9-64cad64194af | carpal-confidence-aware-intent-recognition | 2003.08003 | null | https://arxiv.org/abs/2003.08003v2 | https://arxiv.org/pdf/2003.08003v2.pdf | CARPAL: Confidence-Aware Intent Recognition for Parallel Autonomy | Predicting driver intentions is a difficult and crucial task for advanced driver assistance systems. Traditional confidence measures on predictions often ignore the way predicted trajectories affect downstream decisions for safe driving. In this paper, we propose a novel multi-task intent recognition neural network tha... | ['John J. Leonard', 'Luke Fletcher', 'Jonathan A. DeCastro', 'Stephen G. McGill', 'Guy Rosman', 'Brian C. Williams', 'Xin Huang'] | 2020-03-18 | null | null | null | null | ['intent-recognition'] | ['natural-language-processing'] | [-2.41799988e-02 3.76102030e-01 -4.38329428e-01 -9.74383414e-01
-8.16740513e-01 -3.64810169e-01 7.86418378e-01 2.35741958e-01
-6.73194408e-01 6.20195985e-01 5.36352694e-01 -1.13645339e+00
-2.51975089e-01 -6.43009543e-01 -4.68113065e-01 -1.68587700e-01
1.24550782e-01 2.73252845e-01 3.81493628e-01 -3.33209515... | [5.832157135009766, 0.9467912912368774] |
12e6256b-df35-434b-adf5-55c4cb928c03 | stability-via-adversarial-training-of-neural | 2210.00874 | null | https://arxiv.org/abs/2210.00874v1 | https://arxiv.org/pdf/2210.00874v1.pdf | Stability Via Adversarial Training of Neural Network Stochastic Control of Mean-Field Type | In this paper, we present an approach to neural network mean-field-type control and its stochastic stability analysis by means of adversarial inputs (aka adversarial attacks). This is a class of data-driven mean-field-type control where the distribution of the variables such as the system states and control inputs are ... | ['Boualem Djehiche', 'Salah Eddine Choutri', 'Julian Barreiro-Gomez'] | 2022-09-27 | null | null | null | null | ['type'] | ['speech'] | [ 2.78116226e-01 3.61694485e-01 -1.93489358e-01 1.55465305e-01
-3.48283350e-01 -6.07744932e-01 5.70046067e-01 -6.67555109e-02
-4.20117944e-01 1.37120664e+00 -4.47914511e-01 -5.17383575e-01
-5.83259404e-01 -7.53198028e-01 -1.06929481e+00 -1.13014901e+00
-2.68268257e-01 -7.16848597e-02 -1.94731012e-01 -5.48815250... | [5.3349103927612305, 2.6077287197113037] |
330a1dce-cf34-4e39-ad7e-2a59374a2f3f | optimal-algorithms-for-stochastic-bilevel | 2306.12067 | null | https://arxiv.org/abs/2306.12067v1 | https://arxiv.org/pdf/2306.12067v1.pdf | Optimal Algorithms for Stochastic Bilevel Optimization under Relaxed Smoothness Conditions | Stochastic Bilevel optimization usually involves minimizing an upper-level (UL) function that is dependent on the arg-min of a strongly-convex lower-level (LL) function. Several algorithms utilize Neumann series to approximate certain matrix inverses involved in estimating the implicit gradient of the UL function (hype... | ['Krishnakumar Balasubramanian', 'Tesi Xiao', 'Xuxing Chen'] | 2023-06-21 | null | null | null | null | ['bilevel-optimization'] | ['methodology'] | [-2.05303878e-01 2.62431484e-02 -7.89076760e-02 -2.23512486e-01
-1.38605142e+00 -6.25731707e-01 1.39487088e-01 1.15192167e-01
-2.54308075e-01 8.27614784e-01 -8.55248123e-02 -4.82489139e-01
-4.54636365e-01 -5.10814488e-01 -1.06118691e+00 -1.01911342e+00
-1.73976257e-01 2.50907123e-01 -2.06778824e-01 -1.94132239... | [6.809924602508545, 4.358095645904541] |
8c71e037-8b56-4642-bc3f-42d85850200e | comprehensive-multi-modal-interactions-for | 2104.10412 | null | https://arxiv.org/abs/2104.10412v4 | https://arxiv.org/pdf/2104.10412v4.pdf | Comprehensive Multi-Modal Interactions for Referring Image Segmentation | We investigate Referring Image Segmentation (RIS), which outputs a segmentation map corresponding to the natural language description. Addressing RIS efficiently requires considering the interactions happening across visual and linguistic modalities and the interactions within each modality. Existing methods are limite... | ['Vineet Gandhi', 'Kanishk Jain'] | 2021-04-21 | comprehensive-multi-modal-interactions-for-1 | https://aclanthology.org/2022.findings-acl.270 | https://aclanthology.org/2022.findings-acl.270.pdf | findings-acl-2022-5 | ['referring-expression-segmentation'] | ['computer-vision'] | [ 4.64028656e-01 2.17904657e-01 -1.42099962e-01 -3.69885951e-01
-1.03033471e+00 -7.31569171e-01 8.10082793e-01 2.76876867e-01
-4.44628030e-01 3.83280098e-01 3.19499940e-01 -1.89332485e-01
1.33301154e-01 -5.71490765e-01 -4.54169661e-01 -3.46581340e-01
2.70591140e-01 3.45722347e-01 5.51786244e-01 -3.25026661... | [10.38514518737793, 1.261517882347107] |
a5b2f9b3-956e-46a8-b97c-eae802741f66 | automatic-prompt-augmentation-and-selection | 2302.12822 | null | https://arxiv.org/abs/2302.12822v1 | https://arxiv.org/pdf/2302.12822v1.pdf | Automatic Prompt Augmentation and Selection with Chain-of-Thought from Labeled Data | Chain-of-thought prompting (CoT) advances the reasoning abilities of large language models (LLMs) and achieves superior performance in arithmetic, commonsense, and symbolic reasoning tasks. However, most CoT studies rely on carefully designed human-annotated rational chains to prompt the language model, which poses cha... | ['Tong Zhang', 'Shizhe Diao', 'Kashun Shum'] | 2023-02-24 | null | null | null | null | ['arithmetic-reasoning'] | ['reasoning'] | [ 1.71229541e-01 3.44009697e-01 -3.32894444e-01 -2.92923450e-01
-1.05693686e+00 -5.98695874e-01 5.36257386e-01 2.15471029e-01
-5.54510295e-01 5.95032096e-01 1.89519569e-01 -6.98368549e-01
-2.57987697e-02 -7.05983698e-01 -6.18227422e-01 -2.21079409e-01
2.22468272e-01 7.50858247e-01 7.43833035e-02 -4.66281474... | [9.76326847076416, 7.465367317199707] |
e1bfa615-eb8f-4584-9619-b8c17578877b | ifqa-a-dataset-for-open-domain-question | 2305.14010 | null | https://arxiv.org/abs/2305.14010v1 | https://arxiv.org/pdf/2305.14010v1.pdf | IfQA: A Dataset for Open-domain Question Answering under Counterfactual Presuppositions | Although counterfactual reasoning is a fundamental aspect of intelligence, the lack of large-scale counterfactual open-domain question-answering (QA) benchmarks makes it difficult to evaluate and improve models on this ability. To address this void, we introduce the first such dataset, named IfQA, where each question i... | ['Ashish Sabharwal', 'Peter Clark', 'Meng Jiang', 'Wenhao Yu'] | 2023-05-23 | null | null | null | null | ['open-domain-question-answering'] | ['natural-language-processing'] | [ 7.53253624e-02 6.77094579e-01 -2.78050959e-01 -3.07162732e-01
-1.47959101e+00 -1.05838192e+00 1.00247264e+00 2.58309007e-01
-3.93687755e-01 1.24831021e+00 1.06898022e+00 -9.50578094e-01
-4.02614862e-01 -9.21945810e-01 -1.09058177e+00 -8.51482674e-02
2.12133229e-01 9.46427941e-01 6.71455786e-02 -7.04564035... | [10.752242088317871, 7.929214000701904] |
2c5189e4-049d-4ed9-a9f9-39fd453e4421 | deconvolving-convolution-neural-network-for | 1806.06970 | null | http://arxiv.org/abs/1806.06970v1 | http://arxiv.org/pdf/1806.06970v1.pdf | Deconvolving convolution neural network for cell detection | Automatic cell detection in histology images is a challenging task due to
varying size, shape and features of cells and stain variations across a large
cohort. Conventional deep learning methods regress the probability of each
pixel belonging to the centre of a cell followed by detection of local maxima.
We present dec... | ['Mariam Jamal-Hanjani', 'Khalid AbdulJabbar', 'John Le Quesne', 'Charles Swanton', 'Yinyin Yuan', 'Shan E Ahmed Raza', 'Selvaraju Veeriah'] | 2018-06-18 | null | null | null | null | ['cell-detection'] | ['computer-vision'] | [ 3.67325217e-01 7.47150183e-02 4.35748309e-01 -1.84343442e-01
-8.05528045e-01 -5.78146398e-01 4.80294019e-01 5.14500201e-01
-1.03215265e+00 9.82729077e-01 -2.13336021e-01 1.28191605e-01
3.89949530e-01 -7.06611395e-01 -7.52067864e-01 -1.19212639e+00
5.90123534e-02 4.47178900e-01 5.50348520e-01 1.51998341... | [14.685647010803223, -3.1431689262390137] |
3970d909-6d33-40ac-a191-878b73294342 | real-time-low-cost-multi-person-3d-pose | 2110.11414 | null | https://arxiv.org/abs/2110.11414v3 | https://arxiv.org/pdf/2110.11414v3.pdf | Real-time, low-cost multi-person 3D pose estimation | The process of tracking human anatomy in computer vision is referred to pose estimation, and it is used in fields ranging from gaming to surveillance. Three-dimensional pose estimation traditionally requires advanced equipment, such as multiple linked intensity cameras or high-resolution time-of-flight cameras to produ... | ['Jonathan Leach', 'Abderrahim Halimi', 'Steve McLaughlin', 'Brent Hearn', 'Istvan Gyongy', 'Feng Zhu', 'Stirling Scholes', 'Germán Mora Martín', 'Max Tyler', 'Alice Ruget'] | 2021-10-11 | null | null | null | null | ['3d-pose-estimation', '3d-multi-person-pose-estimation'] | ['computer-vision', 'computer-vision'] | [ 5.10979712e-01 2.49876827e-01 2.86947250e-01 -1.93620816e-01
-5.80452025e-01 -4.73466367e-01 -1.34025678e-01 -7.47271534e-03
-1.06586623e+00 5.47266364e-01 -5.60235023e-01 -1.05149075e-01
3.70332859e-02 -6.11460686e-01 -5.29758453e-01 -3.59355986e-01
-1.49963319e-01 4.84544128e-01 6.43398821e-01 4.73275185... | [8.820425033569336, -2.2967913150787354] |
7a91787a-d44b-4d18-925f-09942b813105 | an-approach-to-improving-sound-based-vehicle | 2204.05082 | null | https://arxiv.org/abs/2204.05082v1 | https://arxiv.org/pdf/2204.05082v1.pdf | An approach to improving sound-based vehicle speed estimation | We consider improving the performance of a recently proposed sound-based vehicle speed estimation method. In the original method, an intermediate feature, referred to as the modified attenuation (MA), has been proposed for both vehicle detection and speed estimation. The MA feature maximizes at the instant of the vehic... | ['Slobodan Djukanovic', 'Nikola Bulatovic'] | 2022-04-08 | null | null | null | null | ['vehicle-speed-estimation'] | ['computer-vision'] | [ 1.50716916e-01 -1.37346417e-01 -4.51141298e-01 -3.70048374e-01
-9.12328899e-01 -4.55477834e-01 2.98897207e-01 2.34518066e-01
-4.40600365e-01 6.11566365e-01 -4.23480541e-01 -4.32765603e-01
-3.62299234e-02 -7.69057453e-01 -5.84918082e-01 -6.03935242e-01
-2.41085604e-01 -1.24742612e-02 7.00217783e-01 2.02222049... | [7.913299083709717, -0.9655061364173889] |
5b2438e3-8098-4c1e-8c2a-b3978e70b621 | latent-complete-row-space-recovery-for-multi | 1912.07248 | null | https://arxiv.org/abs/1912.07248v1 | https://arxiv.org/pdf/1912.07248v1.pdf | Latent Complete Row Space Recovery for Multi-view Subspace Clustering | Multi-view subspace clustering has been applied to applications such as image processing and video surveillance, and has attracted increasing attention. Most existing methods learn view-specific self-representation matrices, and construct a combined affinity matrix from multiple views. The affinity construction process... | ['Chenping Hou', 'Hong Tao', 'Jubo Zhu', 'Dongyun Yi', 'Yuhua Qian'] | 2019-12-16 | null | null | null | null | ['multi-view-subspace-clustering'] | ['computer-vision'] | [ 4.31602336e-02 -5.43441176e-01 -1.64370567e-01 -1.03504909e-02
-5.94021499e-01 -6.39547348e-01 4.02070493e-01 -3.82469654e-01
-4.31704447e-02 2.95946181e-01 3.31420213e-01 1.63526554e-02
-2.91954577e-01 -4.77035463e-01 -3.13057423e-01 -1.17534339e+00
3.43786776e-01 4.50040668e-01 5.28319143e-02 1.75790768... | [8.16383171081543, 4.573819637298584] |
0d0d7e58-e20d-45c2-8635-4ed4f2a4fd0a | user-engagement-prediction-for-clarification | 2102.04163 | null | https://arxiv.org/abs/2102.04163v1 | https://arxiv.org/pdf/2102.04163v1.pdf | User Engagement Prediction for Clarification in Search | Clarification is increasingly becoming a vital factor in various topics of information retrieval, such as conversational search and modern Web search engines. Prompting the user for clarification in a search session can be very beneficial to the system as the user's explicit feedback helps the system improve retrieval ... | ['Fabio Crestani', 'Mohammad Aliannejadi', 'Ivan Sekulić'] | 2021-02-08 | null | null | null | null | ['conversational-search'] | ['natural-language-processing'] | [ 1.03294030e-01 4.80583534e-02 -3.18785667e-01 -2.57995129e-01
-7.61756957e-01 -5.97640216e-01 1.05411696e+00 4.07683581e-01
-7.38611639e-01 6.03504419e-01 4.62870032e-01 -6.89948976e-01
-3.74165982e-01 -1.49885088e-01 -5.72167039e-02 -2.66071528e-01
6.83419883e-01 5.43581784e-01 1.38397962e-01 -6.20112598... | [12.145340919494629, 7.805956840515137] |
0ed24109-5cc7-49bc-b42b-6e9401d3bb54 | boosting-video-captioning-with-dynamic-loss | 2107.11707 | null | https://arxiv.org/abs/2107.11707v3 | https://arxiv.org/pdf/2107.11707v3.pdf | Boosting Video Captioning with Dynamic Loss Network | Video captioning is one of the challenging problems at the intersection of vision and language, having many real-life applications in video retrieval, video surveillance, assisting visually challenged people, Human-machine interface, and many more. Recent deep learning based methods have shown promising results but are... | ['Nasib Ullah', 'Partha Pratim Mohanta'] | 2021-07-25 | null | null | null | null | ['video-description'] | ['computer-vision'] | [ 9.71658379e-02 -1.10239111e-01 -3.98180664e-01 -4.34244394e-01
-8.46152127e-01 -3.05520922e-01 5.98638892e-01 1.23218283e-01
-7.00094759e-01 9.26069200e-01 2.17950806e-01 -2.22824156e-01
5.09475544e-02 -2.75292814e-01 -7.25144088e-01 -5.20277739e-01
9.22846198e-02 3.58390242e-01 3.19218844e-01 -9.92350131... | [10.727278709411621, 0.7078536748886108] |
c1f08c01-6d5f-46a3-b9ae-67b6bce946ef | a-fast-machine-learning-model-for-ecg-based | null | null | https://doi.org/10.3389/fphy.2019.00103 | https://www.frontiersin.org/articles/10.3389/fphy.2019.00103/pdf | A Fast Machine Learning Model for ECG-Based Heartbeat Classification and Arrhythmia Detection | We present a fully automatic and fast ECG arrhythmia classifier based on a simple brain-inspired machine learning approach known as Echo State Networks. Our classifier has a low-demanding feature processing that only requires a single ECG lead. Its training and validation follows an inter-patient procedure. Our approac... | ['Silvia Ortín', 'Miguel C. Soriano', 'Miquel Alfaras'] | 2019-07-18 | null | null | null | frontiers-in-physics-2019-7 | ['arrhythmia-detection', 'heartbeat-classification', 'electrocardiography-ecg'] | ['medical', 'medical', 'methodology'] | [ 3.37694615e-01 8.31100419e-02 1.21532187e-01 -4.90943730e-01
-3.98478687e-01 -4.43804324e-01 2.36641448e-02 5.26165962e-01
-6.21009946e-01 7.75218546e-01 -4.30146515e-01 -2.92712271e-01
-4.32135493e-01 -4.62226748e-01 8.71438086e-02 -7.23465204e-01
-4.97081310e-01 5.47166646e-01 1.02908716e-01 -8.67956579... | [14.273333549499512, 3.267589569091797] |
e16d6e0f-c1ff-41a1-ac92-55c6ff78a0a5 | automated-detection-of-equine-facial-action | 2102.08983 | null | https://arxiv.org/abs/2102.08983v2 | https://arxiv.org/pdf/2102.08983v2.pdf | Automated Detection of Equine Facial Action Units | The recently developed Equine Facial Action Coding System (EquiFACS) provides a precise and exhaustive, but laborious, manual labelling method of facial action units of the horse. To automate parts of this process, we propose a Deep Learning-based method to detect EquiFACS units automatically from images. We use a casc... | ['Hedvig Kjellström', 'Pia Haubro Andersen', 'Sofia Broomé', 'Zhenghong Li'] | 2021-02-17 | null | null | null | null | ['facial-action-unit-detection'] | ['computer-vision'] | [ 4.89494950e-01 5.69212377e-01 -1.06514893e-01 -5.31268656e-01
-8.13915730e-02 -2.58447796e-01 5.60891509e-01 -7.47264326e-01
-5.35257339e-01 4.16636884e-01 2.86071952e-02 2.03127384e-01
3.80923092e-01 -5.30814171e-01 -4.53546494e-01 -5.78052282e-01
5.35441115e-02 2.28736296e-01 4.82082427e-01 -3.63351017... | [13.57342529296875, 1.7604817152023315] |
3cca82eb-6fe3-44e8-bafc-ea3f824dccc4 | capsule-network-based-contrastive-learning-of | 2209.11276 | null | https://arxiv.org/abs/2209.11276v1 | https://arxiv.org/pdf/2209.11276v1.pdf | Capsule Network based Contrastive Learning of Unsupervised Visual Representations | Capsule Networks have shown tremendous advancement in the past decade, outperforming the traditional CNNs in various task due to it's equivariant properties. With the use of vector I/O which provides information of both magnitude and direction of an object or it's part, there lies an enormous possibility of using Capsu... | ['Ioannis Patras', 'Harsh Panwar'] | 2022-09-22 | null | null | null | null | ['unsupervised-image-classification'] | ['computer-vision'] | [-3.61808002e-01 -4.47704382e-02 -4.91390526e-02 -2.35865429e-01
-2.38032088e-01 -6.26123846e-01 2.85200864e-01 1.11174611e-02
-6.24466777e-01 7.32144237e-01 2.06388682e-01 2.60646129e-03
-1.55781209e-01 -4.92324114e-01 -7.32193172e-01 -5.94461918e-01
-5.93484104e-01 3.05924445e-01 3.20177644e-01 -2.50932761... | [14.881304740905762, -2.624868392944336] |
a6de49d2-f147-4fd7-8d46-3e0f95d60a41 | best-vision-technologies-submission-to | 1806.09278 | null | http://arxiv.org/abs/1806.09278v1 | http://arxiv.org/pdf/1806.09278v1.pdf | Best Vision Technologies Submission to ActivityNet Challenge 2018-Task: Dense-Captioning Events in Videos | This note describes the details of our solution to the dense-captioning
events in videos task of ActivityNet Challenge 2018. Specifically, we solve
this problem with a two-stage way, i.e., first temporal event proposal and then
sentence generation. For temporal event proposal, we directly leverage the
three-stage workf... | ['Yuan Liu', 'Moyini Yao'] | 2018-06-25 | null | null | null | null | ['dense-captioning'] | ['computer-vision'] | [ 4.75291580e-01 1.11192860e-01 3.25161181e-02 -3.19641948e-01
-8.16746891e-01 -2.69967109e-01 8.63065481e-01 -3.30578655e-01
-5.39194822e-01 9.56567466e-01 6.68755174e-01 -1.56625658e-01
5.07885277e-01 -5.20916343e-01 -1.04589093e+00 -5.47643006e-01
1.93047926e-01 -1.88678175e-01 1.35470137e-01 2.67063528... | [10.496476173400879, 0.6951568126678467] |
29c2caa6-fa40-4bb8-a57c-d8d27f6402b1 | mlp-3d-a-mlp-like-3d-architecture-with-1 | 2206.06292 | null | https://arxiv.org/abs/2206.06292v1 | https://arxiv.org/pdf/2206.06292v1.pdf | MLP-3D: A MLP-like 3D Architecture with Grouped Time Mixing | Convolutional Neural Networks (CNNs) have been regarded as the go-to models for visual recognition. More recently, convolution-free networks, based on multi-head self-attention (MSA) or multi-layer perceptrons (MLPs), become more and more popular. Nevertheless, it is not trivial when utilizing these newly-minted networ... | ['Tao Mei', 'Chong-Wah Ngo', 'Ting Yao', 'Zhaofan Qiu'] | 2022-06-13 | mlp-3d-a-mlp-like-3d-architecture-with | http://openaccess.thecvf.com//content/CVPR2022/html/Qiu_MLP-3D_A_MLP-Like_3D_Architecture_With_Grouped_Time_Mixing_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Qiu_MLP-3D_A_MLP-Like_3D_Architecture_With_Grouped_Time_Mixing_CVPR_2022_paper.pdf | cvpr-2022-1 | ['action-classification'] | ['computer-vision'] | [-1.04200609e-01 -4.03508872e-01 -2.94022828e-01 -1.23894513e-01
-2.28906006e-01 -3.02915871e-01 6.94001555e-01 -4.33700860e-01
-5.00221193e-01 2.13162556e-01 3.58560532e-02 -5.09186387e-01
1.53561234e-01 -5.34639955e-01 -9.19224203e-01 -9.25412714e-01
-1.79519877e-01 -6.28269017e-02 2.14262232e-01 3.19237113... | [9.044527053833008, 0.3770999014377594] |
bc281609-c0fc-4303-905f-5e692a037c13 | neural-exploitation-and-exploration-of | 2305.03784 | null | https://arxiv.org/abs/2305.03784v1 | https://arxiv.org/pdf/2305.03784v1.pdf | Neural Exploitation and Exploration of Contextual Bandits | In this paper, we study utilizing neural networks for the exploitation and exploration of contextual multi-armed bandits. Contextual multi-armed bandits have been studied for decades with various applications. To solve the exploitation-exploration trade-off in bandits, there are three main techniques: epsilon-greedy, T... | ['Jingrui He', 'Arindam Banerjee', 'Yuchen Yan', 'Yikun Ban'] | 2023-05-05 | null | null | null | null | ['thompson-sampling', 'multi-armed-bandits'] | ['methodology', 'miscellaneous'] | [ 8.39848220e-02 5.37735084e-03 -1.02289355e+00 -3.97800595e-01
-1.27741361e+00 -5.03583372e-01 1.25696838e-01 -6.95203766e-02
-6.70913458e-01 1.44601822e+00 3.43874772e-03 -9.94104564e-01
-6.65893555e-01 -7.73967564e-01 -1.16670573e+00 -8.59681845e-01
-2.87892729e-01 6.66719198e-01 -1.67221844e-01 6.19785972... | [4.529580116271973, 3.261258363723755] |
05462e49-8286-4def-a217-04585dabf2ef | a-multivariate-semi-parametric-portfolio-risk | 2207.04595 | null | https://arxiv.org/abs/2207.04595v2 | https://arxiv.org/pdf/2207.04595v2.pdf | A multivariate semi-parametric portfolio risk optimization and forecasting framework | We develop a novel multivariate semi-parametric modelling approach to portfolio Value-at-Risk (VaR) and Expected Shortfall (ES) forecasting. Differently from existing univariate semi-parametric approaches, the proposed framework involves explicit modelling of the dependence structure among portfolio asset returns throu... | ['Chao Wang', 'Giuseppe Storti'] | 2022-07-11 | null | null | null | null | ['portfolio-optimization'] | ['time-series'] | [-3.65133211e-02 -2.99994648e-02 1.69986859e-01 -4.83704209e-01
-6.92618966e-01 -7.64395595e-01 8.58400583e-01 5.81389517e-02
-1.32299006e-01 7.45301545e-01 7.29803741e-02 -8.39498401e-01
-9.60155904e-01 -1.01583898e+00 -2.33239189e-01 -7.30128825e-01
-1.27109915e-01 6.64490759e-01 -1.61705852e-01 9.82721969... | [4.991524696350098, 4.027007102966309] |
e823fa2b-db8a-4eb2-92bd-0b2f2a8808dd | word-level-loss-extensions-for-neural | 1808.02374 | null | http://arxiv.org/abs/1808.02374v1 | http://arxiv.org/pdf/1808.02374v1.pdf | Word-Level Loss Extensions for Neural Temporal Relation Classification | Unsupervised pre-trained word embeddings are used effectively for many tasks
in natural language processing to leverage unlabeled textual data. Often these
embeddings are either used as initializations or as fixed word representations
for task-specific classification models. In this work, we extend our
classification m... | ['Marie-Francine Moens', 'Artuur Leeuwenberg'] | 2018-08-07 | word-level-loss-extensions-for-neural-1 | https://aclanthology.org/C18-1291 | https://aclanthology.org/C18-1291.pdf | coling-2018-8 | ['temporal-relation-extraction', 'temporal-relation-classification'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.15784389e-01 5.36588311e-01 -7.51656234e-01 -5.62386990e-01
-8.74739707e-01 -3.38604569e-01 7.22281098e-01 9.59265172e-01
-1.04522526e+00 5.94614446e-01 7.25797713e-01 -2.98773378e-01
-6.80828169e-02 -5.50106466e-01 -2.44091094e-01 -6.56609476e-01
-3.71951193e-01 8.45053017e-01 1.27093479e-01 -6.20854199... | [8.561084747314453, 8.624773025512695] |
73b63fa1-c468-4421-b1ed-acf9802b5d51 | deep-collective-knowledge-distillation | 2304.08878 | null | https://arxiv.org/abs/2304.08878v1 | https://arxiv.org/pdf/2304.08878v1.pdf | Deep Collective Knowledge Distillation | Many existing studies on knowledge distillation have focused on methods in which a student model mimics a teacher model well. Simply imitating the teacher's knowledge, however, is not sufficient for the student to surpass that of the teacher. We explore a method to harness the knowledge of other students to complement ... | ['Sungwoo Cho', 'Yongkeun Yun', 'Chanho Min', 'Kyusam Oh', 'Jihyeon Seo'] | 2023-04-18 | null | null | null | null | ['model-compression'] | ['methodology'] | [-3.68307494e-02 1.87776044e-01 -1.15491465e-01 -2.27008104e-01
-3.00856471e-01 -6.36385441e-01 4.16884780e-01 1.97239473e-01
-6.90029144e-01 1.01992857e+00 -7.46408254e-02 -2.18628049e-01
-1.64730370e-01 -1.24668789e+00 -1.02467597e+00 -7.34692037e-01
3.26673359e-01 5.61994493e-01 6.53335869e-01 -2.67401993... | [9.517457008361816, 3.3515501022338867] |
84600a21-90e5-4b81-90e0-fa5daab139c7 | deepsurv-personalized-treatment-recommender | 1606.00931 | null | http://arxiv.org/abs/1606.00931v3 | http://arxiv.org/pdf/1606.00931v3.pdf | DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural Network | Medical practitioners use survival models to explore and understand the
relationships between patients' covariates (e.g. clinical and genetic features)
and the effectiveness of various treatment options. Standard survival models
like the linear Cox proportional hazards model require extensive feature
engineering or pri... | ['Jared Katzman', 'Tingting Jiang', 'Jonathan Bates', 'Alexander Cloninger', 'Yuval Kluger', 'Uri Shaham'] | 2016-06-02 | null | null | null | null | ['predicting-patient-outcomes'] | ['medical'] | [-1.05344042e-01 -5.64642400e-02 -7.76520073e-01 -6.85276568e-01
-4.72712398e-01 -1.36007234e-01 2.82260925e-01 4.78707075e-01
-1.04310866e-02 7.12817490e-01 8.36549222e-01 -8.46284211e-01
-4.33475256e-01 -9.14016008e-01 -2.26766482e-01 -5.23645222e-01
-6.44720435e-01 8.04460466e-01 -2.51810521e-01 -2.66426027... | [7.897073268890381, 5.6831769943237305] |
60a4aea2-1afc-4e3d-9c04-b54145309ac5 | don-t-generate-discriminate-a-proposal-for | 2212.09736 | null | https://arxiv.org/abs/2212.09736v2 | https://arxiv.org/pdf/2212.09736v2.pdf | Don't Generate, Discriminate: A Proposal for Grounding Language Models to Real-World Environments | A key missing capacity of current language models (LMs) is grounding to real-world environments. Most existing work for grounded language understanding uses LMs to directly generate plans that can be executed in the environment to achieve the desired effects. It thereby casts the burden of ensuring grammaticality, fait... | ['Yu Su', 'Xiang Deng', 'Yu Gu'] | 2022-12-19 | null | null | null | null | ['knowledge-base-question-answering'] | ['natural-language-processing'] | [ 1.01063326e-01 5.78716934e-01 -5.71480878e-02 -2.93792397e-01
-1.29514110e+00 -7.05127895e-01 5.66210628e-01 1.69139728e-01
-9.87698957e-02 6.47684038e-01 4.66515720e-01 -5.56307554e-01
-1.33838192e-01 -1.03368866e+00 -8.95731628e-01 -2.59643286e-01
-8.78061131e-02 8.28855991e-01 1.93210945e-01 -6.98021352... | [9.304044723510742, 7.277329444885254] |
6801fa53-6597-45cc-9948-82522a4be626 | sg-fcn-a-motion-and-memory-based-deep | 1809.07988 | null | http://arxiv.org/abs/1809.07988v1 | http://arxiv.org/pdf/1809.07988v1.pdf | SG-FCN: A Motion and Memory-Based Deep Learning Model for Video Saliency Detection | Data-driven saliency detection has attracted strong interest as a result of
applying convolutional neural networks to the detection of eye fixations.
Although a number of imagebased salient object and fixation detection models
have been proposed, video fixation detection still requires more exploration.
Different from ... | ['Meijun Sun', 'Ziqi Zhou', 'Zheng Wang', 'QinGhua Hu', 'Jianmin Jiang'] | 2018-09-21 | null | null | null | null | ['video-saliency-detection'] | ['computer-vision'] | [ 2.73549378e-01 -7.82524824e-01 -3.25295389e-01 -8.36117789e-02
5.82012162e-02 9.08388495e-02 2.12282360e-01 -1.50040453e-02
-5.45678616e-01 4.92261529e-01 1.93297192e-01 -5.08837327e-02
7.99013376e-02 -4.82325852e-01 -5.65356195e-01 -8.10747445e-01
1.53545856e-01 -6.07966185e-01 8.82166147e-01 -1.02945857... | [9.761494636535645, -0.33818700909614563] |
46dcc3b3-e5ee-4484-8cc6-41bdf001760d | a-spatio-temporal-network-for-video-semantic | 2306.11052 | null | https://arxiv.org/abs/2306.11052v1 | https://arxiv.org/pdf/2306.11052v1.pdf | A spatio-temporal network for video semantic segmentation in surgical videos | Semantic segmentation in surgical videos has applications in intra-operative guidance, post-operative analytics and surgical education. Segmentation models need to provide accurate and consistent predictions since temporally inconsistent identification of anatomical structures can impair usability and hinder patient sa... | ['Imanol Luengo', 'Danail Stoyanov', 'Karen Kerr', 'Lucy Culshaw', 'David Owen', 'Felix Bragman', 'Ricardo Sanchez-Matilla', 'Maria Grammatikopoulou'] | 2023-06-19 | null | null | null | null | ['video-semantic-segmentation'] | ['computer-vision'] | [ 1.24084800e-01 3.70008618e-01 -2.98146039e-01 -3.93279493e-01
-4.14470792e-01 -5.55854559e-01 3.02958131e-01 2.08793685e-01
-4.04024720e-01 2.23904416e-01 2.94960320e-01 -3.38555723e-01
-3.71528804e-01 -3.74681771e-01 -5.61394036e-01 -3.53100538e-01
-3.05978656e-01 1.75596669e-01 5.76451600e-01 1.62996575... | [14.045479774475098, -3.330519914627075] |
aa85c68c-7fd2-4fe2-96d8-a59cc870aed2 | relevance-guided-supervision-for-openqa-with | 2007.00814 | null | https://arxiv.org/abs/2007.00814v2 | https://arxiv.org/pdf/2007.00814v2.pdf | Relevance-guided Supervision for OpenQA with ColBERT | Systems for Open-Domain Question Answering (OpenQA) generally depend on a retriever for finding candidate passages in a large corpus and a reader for extracting answers from those passages. In much recent work, the retriever is a learned component that uses coarse-grained vector representations of questions and passage... | ['Christopher Potts', 'Omar Khattab', 'Matei Zaharia'] | 2020-07-01 | null | null | null | null | ['triviaqa'] | ['miscellaneous'] | [-1.30332291e-01 2.06083804e-01 1.31226014e-02 -1.57178283e-01
-1.83541501e+00 -1.05619299e+00 7.64457166e-01 3.67867708e-01
-5.13399482e-01 7.93785512e-01 5.79152644e-01 -4.82515842e-01
-3.85814548e-01 -9.82308984e-01 -7.96007872e-01 -8.06235522e-02
2.01023310e-01 1.18935752e+00 5.94305694e-01 -1.00229800... | [11.336771965026855, 7.892935752868652] |
ef9915b0-e61e-4b83-ad20-f82086e4d58a | a-pov-based-highway-vehicle-trajectory | 2303.06202 | null | https://arxiv.org/abs/2303.06202v1 | https://arxiv.org/pdf/2303.06202v1.pdf | A POV-based Highway Vehicle Trajectory Dataset and Prediction Architecture | Vehicle Trajectory datasets that provide multiple point-of-views (POVs) can be valuable for various traffic safety and management applications. Despite the abundance of trajectory datasets, few offer a comprehensive and diverse range of driving scenes, capturing multiple viewpoints of various highway layouts, merging l... | ['Hamed Tabkhi', 'Armin Danesh Pazho', 'Ghazal Alinezhad Noghre', 'Vinit Katariya'] | 2023-03-10 | null | null | null | null | ['trajectory-prediction'] | ['computer-vision'] | [-5.17638624e-01 -3.98857504e-01 -1.60111129e-01 -3.21173608e-01
-7.43628502e-01 -6.04054928e-01 4.59272146e-01 -2.86085635e-01
-1.78000852e-01 4.26402837e-01 5.12349121e-02 -8.22911739e-01
-1.72344521e-01 -9.93634701e-01 -8.99536312e-01 -5.10529757e-01
-1.89781681e-01 5.62171917e-03 3.58715683e-01 -4.74036276... | [6.073992729187012, 0.8754727244377136] |
a6678ab6-f327-4b96-8a9d-5313b986dc9d | towards-rich-feature-discovery-with-class | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Yang_Towards_Rich_Feature_Discovery_With_Class_Activation_Maps_Augmentation_for_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Yang_Towards_Rich_Feature_Discovery_With_Class_Activation_Maps_Augmentation_for_CVPR_2019_paper.pdf | Towards Rich Feature Discovery With Class Activation Maps Augmentation for Person Re-Identification | The fundamental challenge of small inter-person variation requires Person Re-Identification (Re-ID) models to capture sufficient fine-grained information. This paper proposes to discover diverse discriminative visual cues without extra assistance, e.g., pose estimation, human parsing. Specifically, a Class Activation M... | [' Shu Zhang', ' Kaiqi Huang', ' Xiaotang Chen', ' Zhang Zhang', ' Houjing Huang', 'Wenjie Yang'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['human-parsing'] | ['computer-vision'] | [ 3.50490175e-02 1.87115639e-01 2.75411755e-02 -4.31091756e-01
-4.49578352e-02 -4.92403030e-01 6.87346637e-01 1.20435156e-01
-5.04990220e-01 7.06115901e-01 4.85096484e-01 2.48024046e-01
7.49166403e-03 -4.86584574e-01 -4.68261033e-01 -5.05101979e-01
3.71312425e-02 4.52383608e-01 2.21733645e-01 -1.25215784... | [14.679221153259277, 0.8973012566566467] |
17616cab-58d6-4ce5-88e1-c7d2a6e41449 | personalizing-lexical-simplification | null | null | https://aclanthology.org/C18-1019 | https://aclanthology.org/C18-1019.pdf | Personalizing Lexical Simplification | A lexical simplification (LS) system aims to substitute complex words with simple words in a text, while preserving its meaning and grammaticality. Despite individual users{'} differences in vocabulary knowledge, current systems do not consider these variations; rather, they are trained to find one optimal substitution... | ['John Lee', 'Chak Yan Yeung'] | 2018-08-01 | personalizing-lexical-simplification-1 | https://aclanthology.org/C18-1019 | https://aclanthology.org/C18-1019.pdf | coling-2018-8 | ['complex-word-identification'] | ['natural-language-processing'] | [ 1.09145194e-01 1.06708616e-01 -2.55742580e-01 -3.08104277e-01
-3.07705611e-01 -6.72493219e-01 2.32308760e-01 3.80897522e-01
-9.83177304e-01 5.85978508e-01 4.15471584e-01 -6.44639194e-01
-7.49591812e-02 -6.51713252e-01 -3.94269228e-01 7.72728398e-02
7.05300331e-01 6.24768436e-01 2.86732972e-01 -1.03997743... | [10.882538795471191, 10.343581199645996] |
165c47fd-bd1a-4615-96aa-4ac864c57ab1 | time-efficient-training-of-progressive | 2202.12337 | null | https://arxiv.org/abs/2202.12337v1 | https://arxiv.org/pdf/2202.12337v1.pdf | Time Efficient Training of Progressive Generative Adversarial Network using Depthwise Separable Convolution and Super Resolution Generative Adversarial Network | Generative Adversarial Networks have been employed successfully to generate high-resolution augmented images of size 1024^2. Although the augmented images generated are unprecedented, the training time of the model is exceptionally high. Conventional GAN requires training of both Discriminator as well as the Generator.... | ['Soham Kamble', 'Akshay Joshi', 'Tejas Kolhe', 'Pranesh Kulkarni', 'Atharva Karwande'] | 2022-02-24 | null | null | null | null | ['image-augmentation'] | ['computer-vision'] | [ 7.66635001e-01 4.39569026e-01 3.57163161e-01 -5.37387468e-02
-1.14453530e+00 -5.21669209e-01 6.36172056e-01 -6.47338212e-01
-2.07724750e-01 1.15625119e+00 -1.34754956e-01 -2.03437403e-01
5.76399148e-01 -1.02751923e+00 -6.69218242e-01 -7.59071171e-01
2.62895286e-01 6.39807463e-01 1.23892598e-01 -3.03589970... | [11.560070991516113, -0.577451765537262] |
4ac47a2f-0d21-4ef8-88dd-ec2662ceacd0 | jcdnet-joint-of-common-and-definite-phases | 2303.17294 | null | https://arxiv.org/abs/2303.17294v1 | https://arxiv.org/pdf/2303.17294v1.pdf | JCDNet: Joint of Common and Definite phases Network for Weakly Supervised Temporal Action Localization | Weakly-supervised temporal action localization aims to localize action instances in untrimmed videos with only video-level supervision. We witness that different actions record common phases, e.g., the run-up in the HighJump and LongJump. These different actions are defined as conjoint actions, whose rest parts are def... | ['Wei Zhou', 'Zhiling Luo', 'Xiaoxia Li', 'Yifu Liu'] | 2023-03-30 | null | null | null | null | ['weakly-supervised-temporal-action', 'action-localization', 'multiple-instance-learning'] | ['computer-vision', 'computer-vision', 'methodology'] | [ 5.58997355e-02 -3.64498287e-01 -7.36682892e-01 -6.57226294e-02
-5.31920671e-01 -3.37006688e-01 7.49600649e-01 -3.07257324e-01
-2.51004517e-01 3.91077965e-01 4.99781966e-01 3.87647241e-01
-2.29668185e-01 -2.74645150e-01 -7.68806934e-01 -1.10422146e+00
-1.83156431e-01 1.46557912e-01 7.77623832e-01 -5.05046993... | [8.511635780334473, 0.6539793610572815] |
df6bb72a-3399-40f0-b305-045f2d9af8a9 | representation-learning-for-weakly-supervised | 2105.00815 | null | https://arxiv.org/abs/2105.00815v1 | https://arxiv.org/pdf/2105.00815v1.pdf | Representation Learning for Weakly Supervised Relation Extraction | Recent years have seen rapid development in Information Extraction, as well as its subtask, Relation Extraction. Relation Extraction is able to detect semantic relations between entities in sentences. Currently, many efficient approaches have been applied to relation extraction tasks. Supervised learning approaches esp... | ['Zhuang Li'] | 2021-04-10 | null | null | null | null | ['unsupervised-pre-training'] | ['methodology'] | [ 2.07413778e-01 3.05418670e-01 -4.30630684e-01 -6.25571787e-01
-4.74129289e-01 -1.13478996e-01 4.84435707e-01 4.48376596e-01
-3.85802537e-01 1.05181026e+00 -1.06695469e-03 -2.52033472e-01
-3.11203867e-01 -1.01405764e+00 -2.89089024e-01 -6.58562601e-01
-1.58106182e-02 4.89766121e-01 7.34887943e-02 -4.55083489... | [9.32558822631836, 8.674972534179688] |
bd77063c-2213-45fc-80fc-b68118d4a376 | towards-discriminative-representation | 2108.03439 | null | https://arxiv.org/abs/2108.03439v2 | https://arxiv.org/pdf/2108.03439v2.pdf | Towards Discriminative Representation Learning for Unsupervised Person Re-identification | In this work, we address the problem of unsupervised domain adaptation for person re-ID where annotations are available for the source domain but not for target. Previous methods typically follow a two-stage optimization pipeline, where the network is first pre-trained on source and then fine-tuned on target with pseud... | ['Shengjin Wang', 'Yi Shan', 'Weihua Chen', 'Lu Tian', 'Dong Li', 'Takashi Isobe'] | 2021-08-07 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Isobe_Towards_Discriminative_Representation_Learning_for_Unsupervised_Person_Re-Identification_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Isobe_Towards_Discriminative_Representation_Learning_for_Unsupervised_Person_Re-Identification_ICCV_2021_paper.pdf | iccv-2021-1 | ['unsupervised-person-re-identification'] | ['computer-vision'] | [ 2.04033986e-01 -2.17836261e-01 -1.24713384e-01 -4.24021780e-01
-8.81559491e-01 -5.35869300e-01 7.21086919e-01 6.48133317e-03
-5.39559066e-01 7.46992826e-01 3.13202381e-01 2.67035753e-01
-3.35754633e-01 -5.71142972e-01 -3.91346484e-01 -6.13824427e-01
1.10437505e-01 5.91658711e-01 3.61757055e-02 -2.03979537... | [14.789250373840332, 1.1314051151275635] |
4e05a794-15d0-4ae0-9979-b4ccd0113807 | dibb-distributing-black-box-optimization | null | null | https://openreview.net/forum?id=WYDzDksK5b | https://openreview.net/pdf?id=WYDzDksK5b | DiBB: Distributing Black-Box Optimization | We present a novel framework for Distributing Black-Box Optimization (DiBB). DiBB can encapsulate any Black Box Optimization (BBO) method, making it of particular interest for scaling and distributing modern Evolution Strategies (ES), such as CMA-ES and its variants, which maintain a sampling covariance matrix througho... | ['Tobias Glasmachers', 'Philippe Cudre-Mauroux', 'Fabien Vorpe', 'Luca Sven Rolshoven', 'Giuseppe Cuccu'] | 2021-09-29 | null | null | null | null | ['problem-decomposition'] | ['miscellaneous'] | [-2.69720316e-01 1.25590339e-01 -1.12774096e-01 4.04776931e-01
-3.71537924e-01 -5.70614815e-01 1.97258353e-01 3.24782789e-01
-8.21070433e-01 1.08751714e+00 -5.21657825e-01 -3.51226658e-01
-5.67097902e-01 -6.42309129e-01 -7.36784458e-01 -1.39127958e+00
-4.56527561e-01 7.64784515e-01 1.51228115e-01 -2.59425730... | [4.164682388305664, 2.2858359813690186] |
8375332f-03f1-4d9d-af81-7c8cda880ac3 | bique-biquaternionic-embeddings-of-knowledge | 2109.14401 | null | https://arxiv.org/abs/2109.14401v1 | https://arxiv.org/pdf/2109.14401v1.pdf | BiQUE: Biquaternionic Embeddings of Knowledge Graphs | Knowledge graph embeddings (KGEs) compactly encode multi-relational knowledge graphs (KGs). Existing KGE models rely on geometric operations to model relational patterns. Euclidean (circular) rotation is useful for modeling patterns such as symmetry, but cannot represent hierarchical semantics. In contrast, hyperbolic ... | ['Stanley Kok', 'Jia Guo'] | 2021-09-29 | null | https://aclanthology.org/2021.emnlp-main.657 | https://aclanthology.org/2021.emnlp-main.657.pdf | emnlp-2021-11 | ['knowledge-graph-embeddings', 'knowledge-graph-embeddings'] | ['graphs', 'methodology'] | [-3.81985009e-01 1.80737332e-01 -4.27427500e-01 -1.57513976e-01
1.05615653e-01 -5.44698894e-01 5.32469094e-01 5.28085232e-01
-2.21361164e-02 1.99652240e-01 3.07063192e-01 -4.00187045e-01
-7.65993893e-01 -1.23020065e+00 -5.36153615e-01 -5.18831193e-01
-2.51116753e-01 6.37133002e-01 5.44130683e-01 -5.11098385... | [8.703500747680664, 7.750192165374756] |
7d6d5cd8-0564-4512-a830-4571f4d60b18 | semantic-instance-segmentation-with-a | 1708.02551 | null | http://arxiv.org/abs/1708.02551v1 | http://arxiv.org/pdf/1708.02551v1.pdf | Semantic Instance Segmentation with a Discriminative Loss Function | Semantic instance segmentation remains a challenging task. In this work we
propose to tackle the problem with a discriminative loss function, operating at
the pixel level, that encourages a convolutional network to produce a
representation of the image that can easily be clustered into instances with a
simple post-proc... | ['Luc van Gool', 'Bert De Brabandere', 'Davy Neven'] | 2017-08-08 | null | null | null | null | ['multi-human-parsing'] | ['computer-vision'] | [ 4.39543039e-01 4.83967960e-01 -9.30913091e-02 -5.19501388e-01
-8.97530496e-01 -8.14138114e-01 6.11275136e-01 3.61479402e-01
-5.36782086e-01 4.89389360e-01 -3.65677625e-01 -1.05836891e-01
-2.17268184e-01 -6.53730929e-01 -8.90014112e-01 -7.26294279e-01
2.57437229e-01 5.80878913e-01 7.20339119e-01 -6.30757883... | [9.469398498535156, 0.44310125708580017] |
4a9079cf-ea00-4f25-b0b8-33741820346b | local-hdp-interactive-open-ended-3d-object | 2009.01152 | null | https://arxiv.org/abs/2009.01152v3 | https://arxiv.org/pdf/2009.01152v3.pdf | Local-HDP: Interactive Open-Ended 3D Object Categorization in Real-Time Robotic Scenarios | We introduce a non-parametric hierarchical Bayesian approach for open-ended 3D object categorization, named the Local Hierarchical Dirichlet Process (Local-HDP). This method allows an agent to learn independent topics for each category incrementally and to adapt to the environment in time. Hierarchical Bayesian approac... | ['H. Ayoobi', 'R. Verbrugge', 'B. Verheij', 'H. Kasaei', 'M. Cao'] | 2020-09-02 | null | null | null | null | ['object-categorization'] | ['computer-vision'] | [-6.05939269e-01 7.77549669e-02 -1.03601657e-01 -4.16898131e-01
-5.18887281e-01 -2.33134627e-01 7.63748407e-01 7.74549767e-02
-2.12354302e-01 4.03909922e-01 -8.37195143e-02 1.92666605e-01
-1.95714325e-01 -8.66366982e-01 -4.05894041e-01 -9.96821761e-01
-1.37984112e-01 1.10349059e+00 6.16224349e-01 3.33321512... | [7.6413373947143555, -1.4779554605484009] |
8af85ade-7131-47fe-b216-dfd68e6b181c | a-multi-oriented-chinese-keyword-spotter | 2001.00722 | null | https://arxiv.org/abs/2001.00722v2 | https://arxiv.org/pdf/2001.00722v2.pdf | A Multi-oriented Chinese Keyword Spotter Guided by Text Line Detection | Chinese keyword spotting is a challenging task as there is no visual blank for Chinese words. Different from English words which are split naturally by visual blanks, Chinese words are generally split only by semantic information. In this paper, we propose a new Chinese keyword spotter for natural images, which is insp... | ['Hao Song', 'Hongzhen Wang', 'Pei Xu', 'Shen Huang', 'Qi Ju', 'Shan Huang'] | 2020-01-03 | null | null | null | null | ['line-detection'] | ['computer-vision'] | [ 3.26349288e-01 -1.45310432e-01 -2.28279516e-01 -3.53616178e-01
-3.77705097e-01 -5.03487229e-01 6.74549580e-01 -2.89113879e-01
-7.95210361e-01 4.26112831e-01 2.84187645e-01 -2.88042158e-01
5.07475376e-01 -5.48962116e-01 -7.11874604e-01 -2.93363333e-01
6.86725020e-01 3.61474127e-01 7.12481618e-01 -1.13505200... | [12.008094787597656, 2.230943202972412] |
0d0c0b87-079b-4e22-bd1b-af87959305da | smooth-trajectron-augmenting-the-trajectron | 2305.19678 | null | https://arxiv.org/abs/2305.19678v2 | https://arxiv.org/pdf/2305.19678v2.pdf | Smooth-Trajectron++: Augmenting the Trajectron++ behaviour prediction model with smooth attention | Understanding traffic participants' behaviour is crucial for predicting their future trajectories, aiding in developing safe and reliable planning systems for autonomous vehicles. Integrating cognitive processes and machine learning models has shown promise in other domains but is lacking in the trajectory forecasting ... | ['Arkady Zgonnikov', 'Julian F. Schumann', 'Frederik S. B. Westerhout'] | 2023-05-31 | null | null | null | null | ['trajectory-prediction', 'autonomous-vehicles', 'trajectory-forecasting'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-1.89381376e-01 2.70466864e-01 -3.34494293e-01 -3.95638555e-01
-2.01645121e-01 -2.90489733e-01 1.18057990e+00 1.12219751e-01
-5.49951434e-01 3.80105168e-01 6.15955889e-01 -8.71641219e-01
-3.99522096e-01 -5.76843381e-01 -5.41044712e-01 -3.85112643e-01
-2.04004243e-01 7.62979090e-01 6.20342135e-01 -4.79050934... | [6.075520038604736, 0.7870343923568726] |
2cd0e77d-72c3-40ca-b55d-2909370c7282 | double-dip-unsupervised-image-decomposition | 1812.00467 | null | http://arxiv.org/abs/1812.00467v2 | http://arxiv.org/pdf/1812.00467v2.pdf | "Double-DIP": Unsupervised Image Decomposition via Coupled Deep-Image-Priors | Many seemingly unrelated computer vision tasks can be viewed as a special
case of image decomposition into separate layers. For example, image
segmentation (separation into foreground and background layers); transparent
layer separation (into reflection and transmission layers); Image dehazing
(separation into a clear ... | ['Assaf Shocher', 'Michal Irani', 'Yossi Gandelsman'] | 2018-12-02 | double-dip-unsupervised-image-decomposition-2 | null | null | computer-vision-foundation-2018-12 | ['transparency-separation', 'unsupervised-image-decomposition'] | ['computer-vision', 'computer-vision'] | [ 7.92421877e-01 1.99248940e-01 2.91501909e-01 4.78897523e-03
-1.85480520e-01 -4.70166892e-01 6.18142366e-01 6.80251792e-02
-2.62753665e-01 3.80809546e-01 -4.94736917e-02 -2.61248738e-01
-2.62243003e-02 -6.60464168e-01 -5.99799633e-01 -1.40860677e+00
-1.09561950e-01 6.81390539e-02 6.86318874e-01 -1.66689530... | [10.946657180786133, -2.743053674697876] |
f63462ec-253c-403f-b8a8-26fa6cf1afca | elastic-numerical-reasoning-with-adaptive | 2210.10105 | null | https://arxiv.org/abs/2210.10105v2 | https://arxiv.org/pdf/2210.10105v2.pdf | ELASTIC: Numerical Reasoning with Adaptive Symbolic Compiler | Numerical reasoning over text is a challenging task of Artificial Intelligence (AI), requiring reading comprehension and numerical reasoning abilities. Previous approaches use numerical reasoning programs to represent the reasoning process. However, most works do not separate the generation of operators and operands, w... | ['Yashar Moshfeghi', 'Jiaxin Zhang'] | 2022-10-18 | null | null | null | null | ['math-word-problem-solving', 'math-word-problem-solving', 'math-word-problem-solving'] | ['knowledge-base', 'reasoning', 'time-series'] | [-1.47336066e-01 5.59457876e-02 -2.45472774e-01 -3.72743130e-01
-2.91339070e-01 -6.31999493e-01 4.40536112e-01 2.40892932e-01
-1.65681049e-01 4.02972192e-01 -5.07880561e-02 -1.04235005e+00
1.26178175e-01 -1.40765917e+00 -7.08172917e-01 -1.34371325e-01
2.68286347e-01 5.61642587e-01 2.30292633e-01 -6.43356144... | [9.48022174835205, 7.422053813934326] |
92069f73-bf2a-438f-9c55-8f6855ba0d49 | efficientsrface-an-efficient-network-with | 2306.02277 | null | https://arxiv.org/abs/2306.02277v1 | https://arxiv.org/pdf/2306.02277v1.pdf | EfficientSRFace: An Efficient Network with Super-Resolution Enhancement for Accurate Face Detection | In face detection, low-resolution faces, such as numerous small faces of a human group in a crowded scene, are common in dense face prediction tasks. They usually contain limited visual clues and make small faces less distinguishable from the other small objects, which poses great challenge to accurate face detection. ... | ['Bo Yang', 'Jianhua Xu', 'Jie Xie', 'Jun Li', 'Guangtao Wang'] | 2023-06-04 | null | null | null | null | ['image-super-resolution', 'face-detection', 'super-resolution'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-4.04924378e-02 -1.89733833e-01 -2.14537550e-02 -4.58244383e-01
-4.26899552e-01 3.66146117e-02 4.21928316e-01 -5.37639558e-01
-1.91878468e-01 3.86331022e-01 7.05621988e-02 2.73538232e-01
2.78941602e-01 -9.44922984e-01 -6.95718884e-01 -7.13366866e-01
2.68991813e-02 3.02401602e-01 3.43593687e-01 -1.91771150... | [13.420143127441406, 0.5493342280387878] |
f8f9956f-bdbf-4e3b-954c-27cd2baefc11 | web-scale-language-independent-cataloging-of | null | null | https://aclanthology.org/E17-1091 | https://aclanthology.org/E17-1091.pdf | Web-Scale Language-Independent Cataloging of Noisy Product Listings for E-Commerce | The cataloging of product listings through taxonomy categorization is a fundamental problem for any e-commerce marketplace, with applications ranging from personalized search recommendations to query understanding. However, manual and rule based approaches to categorization are not scalable. In this paper, we compare s... | ['Giuseppe Di Fabbrizio', 'i', 'Pradipto Das', 'Y Xia', 'Aaron Levine', 'Ankur Datta'] | 2017-04-01 | null | null | null | eacl-2017-4 | ['product-categorization'] | ['miscellaneous'] | [-3.58942330e-01 -2.68086314e-01 -6.42351270e-01 -8.20399523e-01
-6.18337274e-01 -8.33788335e-01 4.25582290e-01 3.87567580e-01
-4.48039562e-01 8.05977806e-02 2.91395128e-01 -6.95625186e-01
-4.86989498e-01 -9.83211279e-01 -3.70028913e-01 -1.46493077e-01
1.23386703e-01 9.76311505e-01 6.81657810e-03 -3.90719771... | [9.921346664428711, 6.209068775177002] |
b540997d-3511-4918-a313-425d7586e6b3 | improving-fairness-in-deepfake-detection | 2306.16635 | null | https://arxiv.org/abs/2306.16635v1 | https://arxiv.org/pdf/2306.16635v1.pdf | Improving Fairness in Deepfake Detection | Despite the development of effective deepfake detection models in recent years, several recent studies have demonstrated that biases in the training data utilized to develop deepfake detection models can lead to unfair performance for demographic groups of different races and/or genders. Such can result in these groups... | ['Siwei Lyu', 'George H. Chen', 'Shan Jia', 'Shu Hu', 'Yan Ju'] | 2023-06-29 | null | null | null | null | ['deepfake-detection', 'face-swapping', 'fairness', 'fairness'] | ['computer-vision', 'computer-vision', 'computer-vision', 'miscellaneous'] | [-4.04311031e-01 9.94937196e-02 -3.73622388e-01 -8.70724440e-01
-1.88719049e-01 -3.77905488e-01 7.28117585e-01 9.89031941e-02
-6.75307155e-01 8.05171788e-01 2.97771811e-01 -3.65525752e-01
-5.54666519e-02 -8.69229019e-01 -3.49377781e-01 -3.52390945e-01
1.56372070e-01 3.18798155e-01 -1.54848427e-01 1.05870761... | [8.942110061645508, 5.239869117736816] |
b96a0b48-c29b-406f-a278-cc8bb4f67e12 | pbsm-backdoor-attack-against-keyword-spotting | 2211.08697 | null | https://arxiv.org/abs/2211.08697v1 | https://arxiv.org/pdf/2211.08697v1.pdf | PBSM: Backdoor attack against Keyword spotting based on pitch boosting and sound masking | Keyword spotting (KWS) has been widely used in various speech control scenarios. The training of KWS is usually based on deep neural networks and requires a large amount of data. Manufacturers often use third-party data to train KWS. However, deep neural networks are not sufficiently interpretable to manufacturers, and... | ['Shunhui Ji', 'Yan Xiao', 'Hai Dong', 'Pengcheng Zhang', 'Hanbo Cai'] | 2022-11-16 | null | null | null | null | ['keyword-spotting'] | ['speech'] | [-4.95997742e-02 -5.80146238e-02 -2.93415248e-01 -1.91015691e-01
-5.92708707e-01 -8.60839009e-01 2.78707575e-02 -1.70920655e-01
-2.67531574e-01 1.83722571e-01 -4.12964284e-01 -1.14896584e+00
2.28057459e-01 -8.20372999e-01 -8.69721711e-01 -5.23752987e-01
1.40082181e-01 -2.91677892e-01 2.94801891e-01 -1.78006724... | [13.965699195861816, 5.811590671539307] |
97994f02-8e23-4519-b183-95a5399e7964 | efficient-and-deterministic-search-strategy | 2305.11716 | null | https://arxiv.org/abs/2305.11716v1 | https://arxiv.org/pdf/2305.11716v1.pdf | Efficient and Deterministic Search Strategy Based on Residual Projections for Point Cloud Registration | Estimating the rigid transformation between two LiDAR scans through putative 3D correspondences is a typical point cloud registration paradigm. Current 3D feature matching approaches commonly lead to numerous outlier correspondences, making outlier-robust registration techniques indispensable. Many recent studies have ... | ['Alois Knoll', 'Feihu Zhang', 'Xueli Liu', 'Hu Cao', 'Yinlong Liu', 'Xinyi Li'] | 2023-05-19 | null | null | null | null | ['3d-feature-matching', 'point-cloud-registration'] | ['computer-vision', 'computer-vision'] | [ 3.76067944e-02 -5.14740288e-01 -4.40076105e-02 -7.00113252e-02
-1.01310742e+00 -4.71303165e-01 3.55008781e-01 1.23519190e-01
-4.34471309e-01 2.58921266e-01 -3.84680837e-01 -1.86029345e-01
-3.77370119e-01 -7.09990025e-01 -7.08866656e-01 -6.80448651e-01
2.50460893e-01 7.80605435e-01 3.33194047e-01 -2.89150514... | [7.679967403411865, -2.892148971557617] |
47f965c5-f221-4724-91e7-13c148424fc1 | geneface-generalized-and-stable-real-time | 2305.00787 | null | https://arxiv.org/abs/2305.00787v1 | https://arxiv.org/pdf/2305.00787v1.pdf | GeneFace++: Generalized and Stable Real-Time Audio-Driven 3D Talking Face Generation | Generating talking person portraits with arbitrary speech audio is a crucial problem in the field of digital human and metaverse. A modern talking face generation method is expected to achieve the goals of generalized audio-lip synchronization, good video quality, and high system efficiency. Recently, neural radiance f... | ['Zhou Zhao', 'Zejun Ma', 'Xiang Yin', 'Yi Ren', 'Jinglin Liu', 'Jiawei Huang', 'Rongjie Huang', 'Ziyue Jiang', 'Jinzheng He', 'Zhenhui Ye'] | 2023-05-01 | null | null | null | null | ['motion-prediction', 'talking-face-generation', 'face-generation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-6.14954494e-02 -4.02304471e-01 -1.24265373e-01 -1.01392850e-01
-1.00425136e+00 -1.66969523e-01 3.51499915e-01 -6.51316881e-01
3.67836133e-02 4.99409437e-01 3.48666549e-01 1.10913219e-03
1.69832155e-01 -4.98768747e-01 -6.08909488e-01 -8.85906398e-01
1.91909745e-01 -1.59827620e-01 5.33956885e-02 -1.62199512... | [13.226578712463379, -0.4177965223789215] |
d1f6f474-e7c4-4ac3-af2b-c864e7cfa8c2 | eamdrift-an-interpretable-self-retrain-model | 2305.19837 | null | https://arxiv.org/abs/2305.19837v1 | https://arxiv.org/pdf/2305.19837v1.pdf | EAMDrift: An interpretable self retrain model for time series | The use of machine learning for time series prediction has become increasingly popular across various industries thanks to the availability of time series data and advancements in machine learning algorithms. However, traditional methods for time series forecasting rely on pre-optimized models that are ill-equipped to ... | ['António Rodrigues', 'João Leitão', 'Cláudia Soares', 'Gonçalo Mateus'] | 2023-05-31 | null | null | null | null | ['time-series-prediction'] | ['time-series'] | [ 6.16037659e-02 1.67835932e-02 -2.28171304e-01 -5.06622255e-01
1.25364840e-01 -7.02775896e-01 4.73696679e-01 3.30254078e-01
9.75802094e-02 4.99240816e-01 1.15880743e-02 -4.17551339e-01
-4.86492336e-01 -5.98119557e-01 -4.45857435e-01 -5.28308034e-01
-5.61604202e-01 8.02806914e-01 -1.10531777e-01 -3.86398494... | [7.143691062927246, 3.131134271621704] |
3f077786-4b9e-4067-adce-f11cda41d4d7 | flight-mode-on-a-feather-light-network-for | 2305.10889 | null | https://arxiv.org/abs/2305.10889v1 | https://arxiv.org/pdf/2305.10889v1.pdf | FLIGHT Mode On: A Feather-Light Network for Low-Light Image Enhancement | Low-light image enhancement (LLIE) is an ill-posed inverse problem due to the lack of knowledge of the desired image which is obtained under ideal illumination conditions. Low-light conditions give rise to two main issues: a suppressed image histogram and inconsistent relative color distributions with low signal-to-noi... | ['Mustafa Ayazaoglu', 'Hamza Ergezer', 'Mustafa Ozcan'] | 2023-05-18 | null | null | null | null | ['image-enhancement', 'low-light-image-enhancement'] | ['computer-vision', 'computer-vision'] | [ 7.09941566e-01 -6.14758968e-01 3.78413230e-01 -3.92948538e-01
-5.68065047e-01 -1.83012828e-01 1.37160212e-01 -4.94847536e-01
-5.64152658e-01 7.94419527e-01 -5.23055978e-02 -1.58028245e-01
1.45559132e-01 -7.01229155e-01 -8.33875179e-01 -1.11820090e+00
5.31114638e-01 -4.65663552e-01 1.58639997e-01 -3.28863353... | [10.750223159790039, -2.4438793659210205] |
df620d28-bd45-4940-84dc-c43720fc2c20 | facial-micro-expression-spotting-and | 1902.03514 | null | http://arxiv.org/abs/1902.03514v2 | http://arxiv.org/pdf/1902.03514v2.pdf | Facial Micro-Expression Spotting and Recognition using Time Contrasted Feature with Visual Memory | Facial micro-expressions are sudden involuntary minute muscle movements which
reveal true emotions that people try to conceal. Spotting a micro-expression
and recognizing it is a major challenge owing to its short duration and
intensity. Many works pursued traditional and deep learning based approaches to
solve this is... | ['Ayan Kumar Bhunia', 'Sauradip Nag', 'Aishik Konwer', 'Partha Pratim Roy'] | 2019-02-09 | null | null | null | null | ['micro-expression-spotting'] | ['computer-vision'] | [ 2.10528988e-02 -4.05357331e-01 -1.22141033e-01 -5.42986333e-01
-4.55315441e-01 -3.17900062e-01 4.39507127e-01 -6.14164889e-01
-4.40665573e-01 5.07418096e-01 -1.20152861e-01 4.54951853e-01
1.73309036e-02 -2.75667995e-01 -3.03812683e-01 -1.00879860e+00
-3.65199119e-01 -3.53806347e-01 -9.81111825e-02 -2.61031508... | [13.642889976501465, 1.8731462955474854] |
b80e8e1a-1fba-401b-83bb-62c39b06b812 | handling-cold-start-problem-in-review-spam | null | null | https://aclanthology.org/P17-1034 | https://aclanthology.org/P17-1034.pdf | Handling Cold-Start Problem in Review Spam Detection by Jointly Embedding Texts and Behaviors | Solving cold-start problem in review spam detection is an urgent and significant task. It can help the on-line review websites to relieve the damage of spammers in time, but has never been investigated by previous work. This paper proposes a novel neural network model to detect review spam for cold-start problem, by le... | ['Xuepeng Wang', 'Jun Zhao', 'Kang Liu'] | 2017-07-01 | null | null | null | acl-2017-7 | ['spam-detection'] | ['natural-language-processing'] | [-8.30482319e-02 -3.58520478e-01 -4.74393427e-01 -5.28470635e-01
-3.46886218e-01 -2.70712584e-01 5.21716654e-01 -1.64197251e-01
-4.31284368e-01 6.98964536e-01 -1.72123969e-01 -4.45074528e-01
-2.68489778e-01 -6.01536930e-01 -3.03908195e-02 -5.30732274e-01
5.13198912e-01 2.56617099e-01 4.71245140e-01 -4.56379324... | [7.867880344390869, 9.994648933410645] |
a0e5e73a-4e4e-41f9-b575-acbe725b6e6c | exploring-the-capacity-of-a-large-scale | 2108.12216 | null | https://arxiv.org/abs/2108.12216v1 | https://arxiv.org/pdf/2108.12216v1.pdf | Exploring the Capacity of a Large-scale Masked Language Model to Recognize Grammatical Errors | In this paper, we explore the capacity of a language model-based method for grammatical error detection in detail. We first show that 5 to 10% of training data are enough for a BERT-based error detection method to achieve performance equivalent to a non-language model-based method can achieve with the full training dat... | ['Kazuaki Hanawa', 'Manabu Kimura', 'Ryo Nagata'] | 2021-08-27 | null | https://aclanthology.org/2022.findings-acl.324 | https://aclanthology.org/2022.findings-acl.324.pdf | findings-acl-2022-5 | ['grammatical-error-detection'] | ['natural-language-processing'] | [-8.80579501e-02 5.38803756e-01 1.93636432e-01 -7.11457014e-01
-9.13822353e-01 -2.91458070e-01 1.91134755e-02 8.37348700e-01
-5.72776973e-01 6.63960636e-01 -2.18059331e-01 -9.18304026e-01
-1.61187932e-01 -8.72291028e-01 -7.86680520e-01 1.47915870e-01
3.30855064e-02 5.62778056e-01 4.44039911e-01 -6.99540854... | [10.91702651977539, 10.602365493774414] |
90c08039-2c3e-4aa8-a363-7f79719cf576 | asking-the-right-question-inferring-advice | 1904.01587 | null | http://arxiv.org/abs/1904.01587v1 | http://arxiv.org/pdf/1904.01587v1.pdf | Asking the Right Question: Inferring Advice-Seeking Intentions from Personal Narratives | People often share personal narratives in order to seek advice from others.
To properly infer the narrator's intention, one needs to apply a certain degree
of common sense and social intuition. To test the capabilities of NLP systems
to recover such intuition, we introduce the new task of inferring what is the
advice-s... | ['Cristian Danescu-Niculescu-Mizil', 'Liye Fu', 'Jonathan P. Chang'] | 2019-04-02 | asking-the-right-question-inferring-advice-1 | https://aclanthology.org/N19-1052 | https://aclanthology.org/N19-1052.pdf | naacl-2019-6 | ['cloze-test'] | ['natural-language-processing'] | [ 3.33319873e-01 5.03226936e-01 -1.44163206e-01 -5.74039280e-01
-9.26043808e-01 -9.25579190e-01 1.01562095e+00 5.75927734e-01
-3.79564404e-01 4.70960021e-01 1.05333304e+00 -2.58835346e-01
-2.25002527e-01 -6.49279773e-01 -1.60337716e-01 -2.58962158e-02
4.79113042e-01 5.71387470e-01 3.23514044e-02 -3.12218249... | [11.092905044555664, 8.906229019165039] |
541f48ba-f755-48a3-83af-360aaa96e9b5 | designing-efficient-pair-trading-strategies | 2211.07080 | null | https://arxiv.org/abs/2211.07080v1 | https://arxiv.org/pdf/2211.07080v1.pdf | Designing Efficient Pair-Trading Strategies Using Cointegration for the Indian Stock Market | A pair-trading strategy is an approach that utilizes the fluctuations between prices of a pair of stocks in a short-term time frame, while in the long-term the pair may exhibit a strong association and co-movement pattern. When the prices of the stocks exhibit significant divergence, the shares of the stock that gains ... | ['Jaydip Sen'] | 2022-11-14 | null | null | null | null | ['pair-trading'] | ['time-series'] | [-6.48257792e-01 -3.24327141e-01 -3.25690389e-01 3.98191303e-01
-3.94393802e-01 -9.77911413e-01 7.98091769e-01 -2.47038111e-01
-1.18260168e-01 9.10877168e-01 2.57316470e-01 -5.87719142e-01
-5.92152834e-01 -9.74438667e-01 -3.32643926e-01 -7.57360935e-01
-1.60436690e-01 2.09579527e-01 3.68979365e-01 -3.39305967... | [4.650191307067871, 4.112219333648682] |
8b87a091-cd59-47ab-89a5-dde51b4e8156 | spatiotemporally-consistent-hdr-indoor | 2305.04374 | null | https://arxiv.org/abs/2305.04374v1 | https://arxiv.org/pdf/2305.04374v1.pdf | Spatiotemporally Consistent HDR Indoor Lighting Estimation | We propose a physically-motivated deep learning framework to solve a general version of the challenging indoor lighting estimation problem. Given a single LDR image with a depth map, our method predicts spatially consistent lighting at any given image position. Particularly, when the input is an LDR video sequence, our... | ['Zhao Dong', 'Manmohan Chandraker', 'Mikhail Okunev', 'Li Yu', 'Zhengqin Li'] | 2023-05-07 | null | null | null | null | ['lighting-estimation'] | ['computer-vision'] | [ 7.87194222e-02 -7.75680020e-02 5.69100380e-01 -4.16911513e-01
-6.11829877e-01 -4.11618292e-01 4.90759909e-01 -5.41133642e-01
2.16020122e-02 4.26145077e-01 2.21130475e-01 -3.06004673e-01
5.15096486e-01 -1.02928472e+00 -1.23810768e+00 -4.64096099e-01
2.86700726e-01 3.89032096e-01 3.87533866e-02 -1.22169442... | [9.645514488220215, -3.062983989715576] |
6169c006-e44c-4e77-b3ce-366b14dedb62 | event-based-video-reconstruction-via | 2201.10943 | null | https://arxiv.org/abs/2201.10943v3 | https://arxiv.org/pdf/2201.10943v3.pdf | Event-based Video Reconstruction via Potential-assisted Spiking Neural Network | Neuromorphic vision sensor is a new bio-inspired imaging paradigm that reports asynchronous, continuously per-pixel brightness changes called `events' with high temporal resolution and high dynamic range. So far, the event-based image reconstruction methods are based on artificial neural networks (ANN) or hand-crafted ... | ['Yonghong Tian', 'Tiejun Huang', 'Jianing Li', 'Yi Chang', 'Xiao Wang', 'Lin Zhu'] | 2022-01-25 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Zhu_Event-Based_Video_Reconstruction_via_Potential-Assisted_Spiking_Neural_Network_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Zhu_Event-Based_Video_Reconstruction_via_Potential-Assisted_Spiking_Neural_Network_CVPR_2022_paper.pdf | cvpr-2022-1 | ['video-reconstruction'] | ['computer-vision'] | [ 5.54111183e-01 -6.78305030e-01 5.35201967e-01 -1.06074259e-01
-1.15679011e-01 -2.63556808e-01 3.41255099e-01 -2.94066519e-01
-8.80745053e-01 9.54422116e-01 -3.24960709e-01 1.18873440e-01
1.71713382e-01 -7.72660673e-01 -9.20775473e-01 -1.26410878e+00
5.68666449e-03 -5.88639796e-01 8.14932704e-01 7.44168460... | [8.223953247070312, 2.3663220405578613] |
6a26136d-60f9-43f8-a513-da49e7cd1c90 | weakly-supervised-crack-detection | 2206.06743 | null | https://arxiv.org/abs/2206.06743v2 | https://arxiv.org/pdf/2206.06743v2.pdf | Weakly-Supervised Crack Detection | Pixel-level crack segmentation is widely studied due to its high impact on building and road inspections. While recent studies have made significant improvements in accuracy, they typically heavily depend on pixel-level crack annotations, which are time-consuming to obtain. In earlier work, we proposed to reduce the an... | ['Hiroto Nagayoshi', 'Yuki Inoue'] | 2022-06-14 | null | null | null | null | ['crack-segmentation'] | ['computer-vision'] | [ 4.49676514e-01 2.00165525e-01 2.09103581e-02 -1.80477381e-01
-9.08272624e-01 -4.67992097e-01 1.11635461e-01 5.98245203e-01
-5.10074854e-01 5.19398332e-01 -3.93907368e-01 -2.49119356e-01
2.73846358e-01 -9.08706427e-01 -4.73634630e-01 -9.63060677e-01
3.23503971e-01 1.06261790e-01 1.01728499e+00 8.24619010... | [7.5852437019348145, 1.4528868198394775] |
6da2afed-1df6-43e4-8e59-d73d56571075 | lift-language-interfaced-fine-tuning-for-non | 2206.06565 | null | https://arxiv.org/abs/2206.06565v4 | https://arxiv.org/pdf/2206.06565v4.pdf | LIFT: Language-Interfaced Fine-Tuning for Non-Language Machine Learning Tasks | Fine-tuning pretrained language models (LMs) without making any architectural changes has become a norm for learning various language downstream tasks. However, for non-language downstream tasks, a common practice is to employ task-specific designs for input, output layers, and loss functions. For instance, it is possi... | ['Kangwook Lee', 'Dimitris Papailiopoulos', 'Jy-yong Sohn', 'Michael Gira', 'Shashank Rajput', 'Ziqian Lin', 'Ruisu Zhang', 'Yuchen Zeng', 'Tuan Dinh'] | 2022-06-14 | null | null | null | null | ['classification'] | ['methodology'] | [ 7.01055899e-02 -2.47361716e-02 -2.58062154e-01 -5.22975445e-01
-9.11138654e-01 -8.18079114e-01 5.38221359e-01 -9.63110179e-02
-7.29289472e-01 5.36205947e-01 2.80877709e-01 -7.76876628e-01
9.57390890e-02 -6.05498850e-01 -7.63721108e-01 -6.34617269e-01
2.43306160e-01 1.91685170e-01 -1.51755316e-02 -3.12272549... | [10.678863525390625, 8.491839408874512] |
268ba5d4-f716-4c4c-8554-167f38582fea | skip-connected-3d-densenet-for-volumetric | null | null | https://www.sciencedirect.com/science/article/abs/pii/S1746809419301946 | https://www.sciencedirect.com/science/article/abs/pii/S1746809419301946 | Skip-connected 3D DenseNet for volumetric infant brain MRI segmentation | Automatic 6-month infant brain tissue segmentation of magnetic resonance imaging (MRI) is still less accurate owing to the low intensity contrast among tissues. To tackle the problem, we introduce an accurate segmentation method for volumetric infant brain MRI built upon a densely connected network that achieves state-... | ['Taesup Moon', 'Jitae Shin', 'Toan Duc Bui'] | 2019-09-01 | null | null | null | biomedical-signal-processing-and-control-2019-1 | ['infant-brain-mri-segmentation'] | ['medical'] | [-8.70863572e-02 2.06990063e-01 1.06081627e-01 -5.53100228e-01
-4.69655007e-01 9.17658210e-03 6.34825230e-02 2.14165092e-01
-5.71482480e-01 5.25766551e-01 -1.27836421e-01 -1.99795827e-01
-1.72659047e-02 -6.15289271e-01 -6.49566412e-01 -7.76331663e-01
-5.31567276e-01 4.81565624e-01 5.03371537e-01 2.08778471... | [14.156196594238281, -2.35416316986084] |
7bdeca64-80be-4d0c-a25a-0aa2e4e78964 | improving-cross-modal-alignment-for-text | 2301.11362 | null | https://arxiv.org/abs/2301.11362v1 | https://arxiv.org/pdf/2301.11362v1.pdf | Improving Cross-modal Alignment for Text-Guided Image Inpainting | Text-guided image inpainting (TGII) aims to restore missing regions based on a given text in a damaged image. Existing methods are based on a strong vision encoder and a cross-modal fusion model to integrate cross-modal features. However, these methods allocate most of the computation to visual encoding, while light co... | ['Guodong Long', 'Yucheng Zhou'] | 2023-01-26 | null | null | null | null | ['image-inpainting'] | ['computer-vision'] | [ 4.56433564e-01 -1.38249949e-01 -1.10249333e-01 -2.13345349e-01
-1.22966635e+00 -2.57053047e-01 7.19451785e-01 -3.90675545e-01
-2.26390630e-01 5.48438847e-01 6.36770308e-01 -8.13086703e-02
3.86343360e-01 -6.67929709e-01 -1.02011287e+00 -8.87186944e-01
7.67440557e-01 2.04494983e-01 3.75126563e-02 -3.57138634... | [11.333903312683105, -1.0793229341506958] |
de7d241d-06b1-4b27-84ec-5a883ae56db1 | analysis-and-approximate-inference-of-large | 2306.08489 | null | https://arxiv.org/abs/2306.08489v1 | https://arxiv.org/pdf/2306.08489v1.pdf | Analysis and Approximate Inference of Large and Dense Random Kronecker Graphs | Random graph models are playing an increasingly important role in science and industry, and finds their applications in a variety of fields ranging from social and traffic networks, to recommendation systems and molecular genetics. In this paper, we perform an in-depth analysis of the random Kronecker graph model propo... | ['Yong Xiao', 'Chengmei Niu', 'Yuanqian Xia', 'Zhenyu Liao'] | 2023-06-14 | null | null | null | null | ['graph-classification'] | ['graphs'] | [ 4.07604605e-01 3.96324664e-01 6.88477047e-03 2.11757086e-02
-9.88588184e-02 -6.29093349e-01 3.89722973e-01 2.70397365e-01
-8.56034905e-02 7.42616177e-01 -3.01277161e-01 -5.85499287e-01
-7.46730566e-01 -7.63526082e-01 -7.26248384e-01 -9.73650932e-01
-7.30670035e-01 4.13862318e-01 -1.79499034e-02 -5.98200075... | [6.907944679260254, 5.094531536102295] |
10d73e62-44b5-4c3e-9d4e-4254b996dc6e | safer-data-efficient-and-safe-reinforcement-1 | 2202.04849 | null | https://arxiv.org/abs/2202.04849v2 | https://arxiv.org/pdf/2202.04849v2.pdf | SAFER: Data-Efficient and Safe Reinforcement Learning via Skill Acquisition | Methods that extract policy primitives from offline demonstrations using deep generative models have shown promise at accelerating reinforcement learning(RL) for new tasks. Intuitively, these methods should also help to trainsafeRLagents because they enforce useful skills. However, we identify these techniques are not ... | ['Nevan Wichers', 'Bo Dai', 'Yinlam Chow', 'Dylan Slack'] | 2022-02-10 | null | null | null | null | ['robotic-grasping'] | ['robots'] | [-8.19077622e-03 1.89415902e-01 -4.25043434e-01 -2.20173135e-01
-6.73638403e-01 -8.96564245e-01 9.58338618e-01 -1.22546986e-01
-9.02164936e-01 1.08826196e+00 2.54042953e-01 -2.86274046e-01
-2.40488663e-01 -7.25392461e-01 -1.14280283e+00 -8.46116126e-01
-3.78821999e-01 5.23351848e-01 1.04192570e-01 -3.90794784... | [4.288464069366455, 1.4219733476638794] |
ab2921a0-7ed4-4509-88f8-6f06d759d04f | parcorfull2-0-a-parallel-corpus-annotated | null | null | https://aclanthology.org/2022.lrec-1.85 | https://aclanthology.org/2022.lrec-1.85.pdf | ParCorFull2.0: a Parallel Corpus Annotated with Full Coreference | In this paper, we describe ParCorFull2.0, a parallel corpus annotated with full coreference chains for multiple languages, which is an extension of the existing corpus ParCorFull (Lapshinova-Koltunski et al., 2018). Similar to the previous version, this corpus has been created to address translation of coreference acro... | ['Christian Hardmeier', 'Elina Lartaud', 'Pedro Augusto Ferreira', 'Ekaterina Lapshinova-Koltunski'] | null | null | null | null | lrec-2022-6 | ['coreference-resolution'] | ['natural-language-processing'] | [-1.91749841e-01 3.02413166e-01 -5.84011018e-01 -1.76589880e-02
-9.38389003e-01 -1.20045233e+00 7.35421419e-01 3.32177877e-01
-5.74661016e-01 1.08004797e+00 7.66513050e-01 -4.25557375e-01
-1.28289744e-01 -3.61982644e-01 -3.79623979e-01 -2.20100880e-01
4.28421348e-01 1.18097949e+00 2.35014707e-01 -6.31362200... | [9.840896606445312, 9.655659675598145] |
6bc48046-8d8e-4945-a269-d24e34417d10 | rsfdm-net-real-time-spatial-and-frequency | 2302.12186 | null | https://arxiv.org/abs/2302.12186v1 | https://arxiv.org/pdf/2302.12186v1.pdf | RSFDM-Net: Real-time Spatial and Frequency Domains Modulation Network for Underwater Image Enhancement | Underwater images typically experience mixed degradations of brightness and structure caused by the absorption and scattering of light by suspended particles. To address this issue, we propose a Real-time Spatial and Frequency Domains Modulation Network (RSFDM-Net) for the efficient enhancement of colors and details in... | ['ErKang Chen', 'Tian Ye', 'Sixiang Chen', 'Junjie Yin', 'Yun Liu', 'Jinbin Bai', 'Jingxia Jiang'] | 2023-02-23 | null | null | null | null | ['image-enhancement'] | ['computer-vision'] | [ 4.72609609e-01 -2.92645931e-01 6.42608523e-01 -4.37243730e-01
-2.92770177e-01 -9.11571309e-02 2.47274801e-01 -2.29808077e-01
-5.92438042e-01 6.70749247e-01 1.50106832e-01 -2.44657453e-02
1.09626800e-01 -1.12911749e+00 -7.47055531e-01 -1.20036066e+00
-3.42154711e-01 -7.51319945e-01 4.65506345e-01 -3.49784970... | [10.705473899841309, -3.511286735534668] |
f51b97bc-c8c6-4afd-a7d2-6c565123666e | speech-denoising-by-parametric-resynthesis | 1904.01537 | null | http://arxiv.org/abs/1904.01537v1 | http://arxiv.org/pdf/1904.01537v1.pdf | Speech denoising by parametric resynthesis | This work proposes the use of clean speech vocoder parameters as the target
for a neural network performing speech enhancement. These parameters have been
designed for text-to-speech synthesis so that they both produce high-quality
resyntheses and also are straightforward to model with neural networks, but
have not bee... | ['Soumi Maiti', 'Michael I Mandel'] | 2019-04-02 | null | null | null | null | ['speech-denoising'] | ['speech'] | [ 4.51658934e-01 5.25213897e-01 3.80213588e-01 -3.33619535e-01
-1.14315903e+00 -2.69169033e-01 6.10536933e-01 -3.60057443e-01
-3.52707356e-01 6.46765292e-01 6.13622725e-01 -4.34835136e-01
1.26941249e-01 -4.09670025e-01 -4.59372789e-01 -9.48287904e-01
1.63362503e-01 6.65998608e-02 1.60625950e-01 -5.95621169... | [15.141275405883789, 6.093997001647949] |
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