paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
51e169df-7e23-491f-893c-affbddde8aad | improving-document-level-relation-extraction-1 | null | null | https://openreview.net/forum?id=wu3ADZaresn | https://openreview.net/pdf?id=wu3ADZaresn | Improving Document-level Relation Extraction via Context Guided Mention Integration and Inter-pair Reasoning | Document-level Relation Extraction (DRE) aims to recognize the relations between two entities.
The entity may correspond to multiple mentions that span beyond sentence boundary.
Few previous studies have investigated the mention integration, which may be problematic because coreferential mentions do not equally contrib... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['document-level-relation-extraction'] | ['natural-language-processing'] | [-1.60333980e-02 5.19819796e-01 -3.64306450e-01 -3.73796433e-01
-8.84410501e-01 -6.41304135e-01 5.47190964e-01 7.63654411e-01
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4.67498899e-02 3.69118154e-01 5.31412959e-01 -4.37181920... | [9.247620582580566, 8.716957092285156] |
008a3cda-e3b5-4d4f-9ccb-ac06248acbb2 | the-effect-of-metadata-on-scientific | 2302.03341 | null | https://arxiv.org/abs/2302.03341v1 | https://arxiv.org/pdf/2302.03341v1.pdf | The Effect of Metadata on Scientific Literature Tagging: A Cross-Field Cross-Model Study | Due to the exponential growth of scientific publications on the Web, there is a pressing need to tag each paper with fine-grained topics so that researchers can track their interested fields of study rather than drowning in the whole literature. Scientific literature tagging is beyond a pure multi-label text classifica... | ['Jiawei Han', 'Yu Meng', 'Qi Zhu', 'Bowen Jin', 'Yu Zhang'] | 2023-02-07 | null | null | null | null | ['multi-label-text-classification', 'multi-label-text-classification'] | ['methodology', 'natural-language-processing'] | [-3.13801229e-01 -3.12530279e-01 -6.52988553e-01 5.36665246e-02
-7.62786508e-01 -9.76977527e-01 7.77308166e-01 9.38550293e-01
-5.97622335e-01 9.16496575e-01 4.60125923e-01 -6.19984746e-01
-3.57232451e-01 -7.25858092e-01 -6.39102638e-01 -5.93855143e-01
3.35834771e-01 2.33722880e-01 9.06447843e-02 4.25939441... | [9.584386825561523, 8.153243064880371] |
d5d757a8-820d-4fd6-a7da-88aea97872bf | domain-and-task-adaptation-for-vaccinchatnl-a | null | null | https://aclanthology.org/2022.coling-1.312 | https://aclanthology.org/2022.coling-1.312.pdf | Domain- and Task-Adaptation for VaccinChatNL, a Dutch COVID-19 FAQ Answering Corpus and Classification Model | FAQs are important resources to find information. However, especially if a FAQ concerns many question-answer pairs, it can be a difficult and time-consuming job to find the answer you are looking for. A FAQ chatbot can ease this process by automatically retrieving the relevant answer to a user’s question. We present Va... | ['Walter Daelemans', 'Ehsan Lotfi', 'Maxime De Bruyn', 'Jeska Buhmann'] | null | null | null | null | coling-2022-10 | ['intent-classification'] | ['natural-language-processing'] | [-1.54978096e-01 7.13501498e-02 8.22197460e-03 -4.63120639e-01
-1.44803047e+00 -1.00445330e+00 2.26545110e-01 6.40533745e-01
-6.37888312e-01 7.48738647e-01 7.08415926e-01 -4.32040542e-01
-1.41162649e-01 -7.62736082e-01 -2.67090559e-01 -9.77829844e-03
4.37701911e-01 1.07763445e+00 7.94599473e-01 -6.66543305... | [11.297709465026855, 8.050749778747559] |
3192d2f4-3114-481c-9b8d-7fa1551e5572 | evolvemt-an-ensemble-mt-engine-improving | 2306.11823 | null | https://arxiv.org/abs/2306.11823v1 | https://arxiv.org/pdf/2306.11823v1.pdf | EvolveMT: an Ensemble MT Engine Improving Itself with Usage Only | This paper presents EvolveMT for efficiently combining multiple machine translation (MT) engines. The proposed system selects the output from a single engine for each segment by utilizing online learning techniques to predict the most suitable system for every translation request. A neural quality estimation metric sup... | ['Hassan Sawaf', 'Shreyas Sharma', 'Mohamed Al-Badrashiny', 'Ahmet Gunduz', 'Kamer Ali Yuksel'] | 2023-06-20 | null | null | null | null | ['machine-translation'] | ['natural-language-processing'] | [ 1.72212392e-01 -8.69819298e-02 -3.63683313e-01 -3.31365049e-01
-1.07268023e+00 -8.35721731e-01 5.38553059e-01 1.56583712e-01
-5.39750874e-01 7.07474887e-01 -2.75946707e-01 -5.13333559e-01
-4.93543223e-02 -3.62549901e-01 -6.06949568e-01 -3.94812495e-01
3.10839474e-01 1.18955123e+00 9.03846025e-02 -3.08058619... | [11.717975616455078, 10.206855773925781] |
891781b9-74a1-4c4d-9bc1-d66d8735eef1 | self-correction-for-human-parsing | 1910.09777 | null | https://arxiv.org/abs/1910.09777v1 | https://arxiv.org/pdf/1910.09777v1.pdf | Self-Correction for Human Parsing | Labeling pixel-level masks for fine-grained semantic segmentation tasks, e.g. human parsing, remains a challenging task. The ambiguous boundary between different semantic parts and those categories with similar appearance usually are confusing, leading to unexpected noises in ground truth masks. To tackle the problem o... | ['Yi Yang', 'Peike Li', 'Yunchao Wei', 'Yunqiu Xu'] | 2019-10-22 | null | null | null | null | ['human-part-segmentation', 'human-parsing'] | ['computer-vision', 'computer-vision'] | [ 3.78594846e-01 3.81786168e-01 -1.58072799e-01 -6.09292984e-01
-1.08621061e+00 -4.21127141e-01 1.02339327e-01 1.05709106e-01
-3.79035622e-01 7.37898827e-01 1.51934803e-01 1.18104763e-01
4.93414134e-01 -4.20628548e-01 -7.90669322e-01 -7.17100859e-01
5.83280146e-01 5.94781637e-01 4.61531669e-01 2.27070153... | [9.233514785766602, 0.39966195821762085] |
b3794d9f-2b9a-48dc-9fec-1237d66aff06 | an-internal-learning-approach-to-video | 1909.07957 | null | https://arxiv.org/abs/1909.07957v1 | https://arxiv.org/pdf/1909.07957v1.pdf | An Internal Learning Approach to Video Inpainting | We propose a novel video inpainting algorithm that simultaneously hallucinates missing appearance and motion (optical flow) information, building upon the recent 'Deep Image Prior' (DIP) that exploits convolutional network architectures to enforce plausible texture in static images. In extending DIP to video we make tw... | ['Long Mai', 'John Collomosse', 'Zhaowen Wang', 'Ning Xu', 'Haotian Zhang', 'Hailin Jin'] | 2019-09-17 | an-internal-learning-approach-to-video-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Zhang_An_Internal_Learning_Approach_to_Video_Inpainting_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Zhang_An_Internal_Learning_Approach_to_Video_Inpainting_ICCV_2019_paper.pdf | iccv-2019-10 | ['video-inpainting'] | ['computer-vision'] | [ 4.26244557e-01 2.98795104e-01 -3.39404028e-03 -1.53750420e-01
-8.45501661e-01 -5.59508562e-01 8.04499984e-01 -6.35318577e-01
-3.57824638e-02 7.55444109e-01 3.93684030e-01 -6.22562021e-02
1.21835545e-01 -3.15361977e-01 -1.28872418e+00 -4.64732438e-01
-1.05057778e-02 -3.37537527e-02 6.90385625e-02 -3.92483547... | [10.842686653137207, -0.9994127154350281] |
5f97b956-9322-4437-ae2d-fe2eb585449c | towards-segmenting-everything-that-moves | 1902.03715 | null | https://arxiv.org/abs/1902.03715v4 | https://arxiv.org/pdf/1902.03715v4.pdf | Towards Segmenting Anything That Moves | Detecting and segmenting individual objects, regardless of their category, is crucial for many applications such as action detection or robotic interaction. While this problem has been well-studied under the classic formulation of spatio-temporal grouping, state-of-the-art approaches do not make use of learning-based m... | ['Deva Ramanan', 'Pavel Tokmakov', 'Achal Dave'] | 2019-02-11 | null | null | null | null | ['moving-object-detection'] | ['computer-vision'] | [ 3.02117229e-01 -4.51509207e-01 -3.88452262e-01 -1.98242188e-01
-4.46542382e-01 -7.79385030e-01 7.25271881e-01 3.39265019e-01
-4.07262415e-01 2.36880317e-01 3.07964832e-01 -4.78928871e-02
-7.40874112e-02 -4.57719326e-01 -5.99987507e-01 -6.34973228e-01
-2.62681276e-01 1.70172423e-01 1.01200902e+00 -5.15506640... | [8.65615463256836, 0.14869238436222076] |
773973b6-e46b-4837-9b75-d4ec9a7c62db | the-interface-between-readability-and | null | null | https://aclanthology.org/W18-7001 | https://aclanthology.org/W18-7001.pdf | The Interface Between Readability and Automatic Text Simplification | null | ['Thomas Fran{\\c{c}}ois'] | 2018-11-01 | null | null | null | ws-2018-11 | ['complex-word-identification'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.382079124450684, 3.729156970977783] |
b05f6d49-fd48-49b3-a5cd-780e9289e3e1 | visual-tactile-fusion-for-transparent-object | 2211.16693 | null | https://arxiv.org/abs/2211.16693v1 | https://arxiv.org/pdf/2211.16693v1.pdf | Visual-tactile Fusion for Transparent Object Grasping in Complex Backgrounds | The accurate detection and grasping of transparent objects are challenging but of significance to robots. Here, a visual-tactile fusion framework for transparent object grasping under complex backgrounds and variant light conditions is proposed, including the grasping position detection, tactile calibration, and visual... | ['Xiao-Ping Zhang', 'Xueqian Wang', 'Chongkun Xia', 'Linqi Ye', 'Houde Liu', 'Wenbo Ding', 'Haixin Yu', 'Shoujie Li'] | 2022-11-30 | null | null | null | null | ['transparent-objects'] | ['computer-vision'] | [ 2.53181219e-01 -3.79358500e-01 3.70062768e-01 -2.68426210e-01
-2.37827256e-01 -5.99672198e-01 1.11072302e-01 6.12850748e-02
-4.62168813e-01 2.53830254e-01 -3.93749982e-01 1.74591452e-01
-1.47399038e-01 -8.37531030e-01 -6.06572509e-01 -1.09442019e+00
1.82175398e-01 1.14008084e-01 5.27675927e-01 3.25547606... | [5.843311786651611, -0.9121105074882507] |
28a07772-f57a-44c4-9675-a86c2c60a4d8 | event-based-motion-segmentation-by-cascaded | 2111.03483 | null | https://arxiv.org/abs/2111.03483v1 | https://arxiv.org/pdf/2111.03483v1.pdf | Event-based Motion Segmentation by Cascaded Two-Level Multi-Model Fitting | Among prerequisites for a synthetic agent to interact with dynamic scenes, the ability to identify independently moving objects is specifically important. From an application perspective, nevertheless, standard cameras may deteriorate remarkably under aggressive motion and challenging illumination conditions. In contra... | ['Shaojie Shen', 'Yi Zhou', 'Xiuyuan Lu'] | 2021-11-05 | null | null | null | null | ['motion-segmentation'] | ['computer-vision'] | [ 5.84760904e-01 -5.05119205e-01 1.78617284e-01 1.47191599e-01
-4.52383488e-01 -9.27051961e-01 6.70829713e-01 -7.76068419e-02
-5.60393572e-01 6.19350135e-01 -4.42149907e-01 2.14444980e-01
-2.28519484e-01 -4.47034717e-01 -6.70620084e-01 -9.45161939e-01
7.71761611e-02 3.90306592e-01 7.18842506e-01 3.00692022... | [8.667444229125977, -1.0910794734954834] |
63fc1c7a-99a6-4270-9ae7-b107a8298ba0 | effects-of-data-enrichment-with-image | 2306.07724 | null | https://arxiv.org/abs/2306.07724v1 | https://arxiv.org/pdf/2306.07724v1.pdf | Effects of Data Enrichment with Image Transformations on the Performance of Deep Networks | Images cannot always be expected to come in a certain standard format and orientation. Deep networks need to be trained to take into account unexpected variations in orientation or format. For this purpose, training data should be enriched to include different conditions. In this study, the effects of data enrichment o... | ['Hakan Temiz'] | 2023-06-13 | null | null | null | null | ['super-resolution'] | ['computer-vision'] | [ 3.13695580e-01 2.55161501e-03 2.07291573e-01 -5.28028607e-01
-1.34541437e-01 -3.41036439e-01 8.70531738e-01 -2.54577011e-01
-8.22001040e-01 7.79947460e-01 3.05729985e-01 7.76225328e-02
-1.84737563e-01 -7.16805935e-01 -8.91169727e-01 -6.18917465e-01
6.82777688e-02 4.67387378e-01 -3.31539661e-02 -3.61574262... | [10.72103214263916, -1.7071115970611572] |
33b1b072-939b-4d90-907c-63e403e6d123 | target-concept-guided-medical-concept | null | null | https://aclanthology.org/2020.deelio-1.8 | https://aclanthology.org/2020.deelio-1.8.pdf | Target Concept Guided Medical Concept Normalization in Noisy User-Generated Texts | Medical concept normalization (MCN) i.e., mapping of colloquial medical phrases to standard concepts is an essential step in analysis of medical social media text. The main drawback in existing state-of-the-art approach (Kalyan and Sangeetha, 2020b) is learning target concept vector representations from scratch which r... | ['Sivanesan Sangeetha', 'Katikapalli Subramanyam Kalyan'] | null | null | null | null | emnlp-deelio-2020-11 | ['medical-concept-normalization'] | ['medical'] | [ 6.08879685e-01 2.76024073e-01 -3.33514124e-01 -2.55530983e-01
-7.50163972e-01 -2.83147186e-01 6.72574401e-01 1.12867498e+00
-9.41379607e-01 6.98257446e-01 7.44205534e-01 5.12375869e-02
-2.86068857e-01 -1.00838685e+00 -1.23648219e-01 -2.94537544e-01
1.56029806e-01 5.09705901e-01 1.35354713e-01 -8.40157688... | [8.621116638183594, 8.550880432128906] |
261652b3-e9aa-4f3b-9a08-8d147027ff05 | application-of-deep-neural-networks-to-assess | 2003.02334 | null | https://arxiv.org/abs/2003.02334v1 | https://arxiv.org/pdf/2003.02334v1.pdf | Application of Deep Neural Networks to assess corporate Credit Rating | Recent literature implements machine learning techniques to assess corporate credit rating based on financial statement reports. In this work, we analyze the performance of four neural network architectures (MLP, CNN, CNN2D, LSTM) in predicting corporate credit rating as issued by Standard and Poor's. We analyze compan... | ['Parisa Golbayani', 'Ionut Florescu', 'Dan Wang'] | 2020-03-04 | null | null | null | null | ['holdout-set'] | ['computer-vision'] | [-0.17744206 -0.13011084 -0.23143615 -0.44358867 -0.20680556 -0.51451176
0.5500949 0.43066597 -0.37555954 0.7140915 0.4926606 -0.66244614
-0.42281765 -0.95624727 -0.17210175 -0.41908485 -0.06429069 0.2456824
-0.19549568 -0.17758976 1.0599074 0.71777993 -1.2586565 0.50586456
0.4768951 1.2533305 -0.2... | [4.543757915496826, 4.219448089599609] |
7b91d01e-5e8b-4564-a0fc-3d7c41eedfcc | socially-and-contextually-aware-human-motion | 2007.06843 | null | https://arxiv.org/abs/2007.06843v1 | https://arxiv.org/pdf/2007.06843v1.pdf | Socially and Contextually Aware Human Motion and Pose Forecasting | Smooth and seamless robot navigation while interacting with humans depends on predicting human movements. Forecasting such human dynamics often involves modeling human trajectories (global motion) or detailed body joint movements (local motion). Prior work typically tackled local and global human movements separately. ... | ['Vida Adeli', 'Juan Carlos Niebles', 'Ehsan Adeli', 'Ian Reid', 'Hamid Rezatofighi'] | 2020-07-14 | null | null | null | null | ['human-dynamics'] | ['computer-vision'] | [ 1.06959999e-01 1.21968538e-01 1.51656047e-01 -5.34465849e-01
-7.13435471e-01 7.14429561e-03 7.17213154e-01 -1.73225135e-01
-6.72333837e-01 6.21449828e-01 6.71982348e-01 3.63905668e-01
3.78736794e-01 -5.51304936e-01 -1.04657304e+00 -4.42868143e-01
-1.90724716e-01 3.74705642e-01 3.57209593e-01 -2.33730868... | [7.270692348480225, -0.32502493262290955] |
8d295b4c-ddc2-4b56-a8ff-c235da6206c3 | color-constancy-by-gans-an-experimental | 1812.03085 | null | http://arxiv.org/abs/1812.03085v1 | http://arxiv.org/pdf/1812.03085v1.pdf | Color Constancy by GANs: An Experimental Survey | In this paper, we formulate the color constancy task as an image-to-image
translation problem using GANs. By conducting a large set of experiments on
different datasets, an experimental survey is provided on the use of different
types of GANs to solve for color constancy i.e. CC-GANs (Color Constancy GANs).
Based on th... | ['Theo Gevers', 'Sezer Karaoglu', 'Anil S. Baslamisli', 'Yang Liu', 'Partha Das'] | 2018-12-07 | null | null | null | null | ['color-constancy'] | ['computer-vision'] | [ 4.60655123e-01 -6.40711561e-02 -1.01836964e-01 -4.79965478e-01
-5.70907652e-01 -5.29641867e-01 5.16010761e-01 -9.99468923e-01
3.23466733e-02 7.48722911e-01 -5.76074347e-02 -2.75378704e-01
4.32765067e-01 -4.70566481e-01 -4.38255221e-01 -7.65681863e-01
6.08803034e-01 -8.04907307e-02 -3.90549541e-01 -2.09058106... | [11.350112915039062, -1.1297941207885742] |
9e2061c0-cd85-4891-aacf-862768505beb | modeling-and-design-of-heterogeneous | 2304.05137 | null | https://arxiv.org/abs/2304.05137v1 | https://arxiv.org/pdf/2304.05137v1.pdf | Modeling and design of heterogeneous hierarchical bioinspired spider web structures using generative deep learning and additive manufacturing | Spider webs are incredible biological structures, comprising thin but strong silk filament and arranged into complex hierarchical architectures with striking mechanical properties (e.g., lightweight but high strength, achieving diverse mechanical responses). While simple 2D orb webs can easily be mimicked, the modeling... | ['Markus J. Buehler', 'Nic A. Lee', 'Wei Lu'] | 2023-04-11 | null | null | null | null | ['graph-construction'] | ['graphs'] | [ 9.75814834e-02 2.73395777e-01 1.75248742e-01 2.47679889e-01
1.22743584e-01 -1.00462449e+00 6.70642257e-01 -4.04176652e-01
5.14301062e-01 6.93152905e-01 3.19193453e-01 -2.83281118e-01
-3.98018122e-01 -1.26490748e+00 -9.53199565e-01 -8.23690891e-01
-6.27863407e-01 7.13018417e-01 3.72176409e-01 -5.35093129... | [5.170474529266357, 5.403843879699707] |
00526faa-e6a9-4daf-8490-2a869c7e5241 | hts-at-a-hierarchical-token-semantic-audio | 2202.00874 | null | https://arxiv.org/abs/2202.00874v1 | https://arxiv.org/pdf/2202.00874v1.pdf | HTS-AT: A Hierarchical Token-Semantic Audio Transformer for Sound Classification and Detection | Audio classification is an important task of mapping audio samples into their corresponding labels. Recently, the transformer model with self-attention mechanisms has been adopted in this field. However, existing audio transformers require large GPU memories and long training time, meanwhile relying on pretrained visio... | ['Shlomo Dubnov', 'Taylor Berg-Kirkpatrick', 'Zejun Ma', 'Bilei Zhu', 'Xingjian Du', 'Ke Chen'] | 2022-02-02 | null | null | null | null | ['sound-classification', 'keyword-spotting'] | ['audio', 'speech'] | [-1.84678316e-01 -4.67558831e-01 1.78544685e-01 -2.03575715e-01
-1.03037190e+00 -3.12150449e-01 2.40229905e-01 2.51553595e-01
-5.68365395e-01 2.05275193e-02 1.49884552e-01 6.49914443e-02
4.01522964e-01 -8.88864577e-01 -6.42412663e-01 -5.27176082e-01
-6.82238787e-02 3.84919912e-01 7.46644795e-01 1.10704035... | [15.107979774475098, 5.225547790527344] |
1ca91ad1-f516-4c52-a047-6b385c44ba99 | gpu-based-computation-of-2d-least-median-of | 1510.01041 | null | http://arxiv.org/abs/1510.01041v1 | http://arxiv.org/pdf/1510.01041v1.pdf | GPU-Based Computation of 2D Least Median of Squares with Applications to Fast and Robust Line Detection | The 2D Least Median of Squares (LMS) is a popular tool in robust regression
because of its high breakdown point: up to half of the input data can be
contaminated with outliers without affecting the accuracy of the LMS estimator.
The complexity of 2D LMS estimation has been shown to be $\Omega(n^2)$ where
$n$ is the tot... | ['Gil Shapira', 'Tal Hassner'] | 2015-10-05 | null | null | null | null | ['line-detection'] | ['computer-vision'] | [ 1.28302827e-01 -4.22030360e-01 5.21528542e-01 -1.27398759e-01
-1.00890791e+00 -3.93177181e-01 1.80206880e-01 2.47475952e-01
-5.28216958e-01 5.94148397e-01 -4.63845044e-01 -4.28690195e-01
1.32412568e-01 -6.34542048e-01 -6.97464883e-01 -7.32048988e-01
-1.12919994e-01 3.16772938e-01 6.93171799e-01 -4.34093662... | [8.223753929138184, -1.743933916091919] |
10b6b639-a332-4e24-80a0-67fa68dc0d31 | collaborative-static-and-dynamic-vision | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Lin_Collaborative_Static_and_Dynamic_Vision-Language_Streams_for_Spatio-Temporal_Video_Grounding_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Lin_Collaborative_Static_and_Dynamic_Vision-Language_Streams_for_Spatio-Temporal_Video_Grounding_CVPR_2023_paper.pdf | Collaborative Static and Dynamic Vision-Language Streams for Spatio-Temporal Video Grounding | Spatio-Temporal Video Grounding (STVG) aims to localize the target object spatially and temporally according to the given language query. It is a challenging task in which the model should well understand dynamic visual cues (e.g., motions) and static visual cues (e.g., object appearances) in the language descripti... | ['Wei-Shi Zheng', 'Tiancai Ye', 'Zhi Jin', 'Jian-Fang Hu', 'Chaolei Tan', 'Zihang Lin'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['video-grounding', 'spatio-temporal-video-grounding'] | ['computer-vision', 'computer-vision'] | [-1.70216486e-01 -3.69945258e-01 -3.10027361e-01 -4.69668031e-01
-6.35253489e-01 -5.75777948e-01 6.61552787e-01 4.76096384e-02
-3.45944822e-01 1.60563529e-01 3.40299606e-01 8.10941830e-02
7.99307302e-02 -4.95350957e-01 -8.72608423e-01 -5.79888225e-01
1.45654723e-01 2.16316015e-01 8.44540298e-01 -1.33696795... | [9.738816261291504, 0.7029266357421875] |
c4028d89-ebaf-4616-a5e6-b5e47ca78ee9 | taxonomy-expansion-for-named-entity | 2305.13191 | null | https://arxiv.org/abs/2305.13191v1 | https://arxiv.org/pdf/2305.13191v1.pdf | Taxonomy Expansion for Named Entity Recognition | Training a Named Entity Recognition (NER) model often involves fixing a taxonomy of entity types. However, requirements evolve and we might need the NER model to recognize additional entity types. A simple approach is to re-annotate entire dataset with both existing and additional entity types and then train the model ... | ['Miguel Ballesteros', 'Dan Roth', 'Vittorio Castelli', 'Yassine Benajiba', 'Shuai Wang', 'Neha Anna John', 'Giovanni Paolini', 'Jie Ma', 'Yogarshi Vyas', 'Karthikeyan K'] | 2023-05-22 | null | null | null | null | ['named-entity-recognition-ner', 'taxonomy-expansion'] | ['natural-language-processing', 'natural-language-processing'] | [ 5.50596090e-03 2.83746243e-01 -2.45148227e-01 -4.68868732e-01
-7.27964222e-01 -1.23734510e+00 2.75642872e-01 3.86751533e-01
-8.73990953e-01 9.70913172e-01 2.49900714e-01 -3.72930050e-01
-3.54968058e-03 -6.86406195e-01 -5.49561024e-01 -1.38565004e-02
2.49541223e-01 8.25187802e-01 2.54338741e-01 -3.99364643... | [9.577610969543457, 9.059968948364258] |
11ca6d82-95ae-4417-b775-e8b11f2bfe2b | advanced-semantics-for-commonsense-knowledge | 2011.00905 | null | https://arxiv.org/abs/2011.00905v4 | https://arxiv.org/pdf/2011.00905v4.pdf | Advanced Semantics for Commonsense Knowledge Extraction | Commonsense knowledge (CSK) about concepts and their properties is useful for AI applications such as robust chatbots. Prior works like ConceptNet, TupleKB and others compiled large CSK collections, but are restricted in their expressiveness to subject-predicate-object (SPO) triples with simple concepts for S and monol... | ['Gerhard Weikum', 'Simon Razniewski', 'Tuan-Phong Nguyen'] | 2020-11-02 | null | null | null | null | ['commonsense-knowledge-base-construction'] | ['knowledge-base'] | [-4.34728354e-01 4.67438757e-01 -4.35022295e-01 -3.42252135e-01
-5.19005954e-01 -8.47449064e-01 8.46976936e-01 6.30252779e-01
-2.19187438e-01 1.15717196e+00 3.93445730e-01 -1.95707366e-01
-6.74002528e-01 -9.08848703e-01 -4.16850090e-01 -1.10634007e-01
-1.70543805e-01 8.14195454e-01 6.52924061e-01 -6.53560936... | [9.62797737121582, 8.244457244873047] |
d07afb81-bcd9-4c48-a382-8a7c67936631 | impossible-triangle-what-s-next-for-pre | 2204.06130 | null | https://arxiv.org/abs/2204.06130v2 | https://arxiv.org/pdf/2204.06130v2.pdf | Impossible Triangle: What's Next for Pre-trained Language Models? | Recent development of large-scale pre-trained language models (PLM) have significantly improved the capability of models in various NLP tasks, in terms of performance after task-specific fine-tuning and zero-shot / few-shot learning. However, many of such models come with a dauntingly huge size that few institutions ca... | ['Michael Zeng', 'Chenguang Zhu'] | 2022-04-13 | null | null | null | null | ['generalized-few-shot-learning'] | ['methodology'] | [ 7.51347421e-03 1.21127188e-01 -5.39270699e-01 -1.24012701e-01
-6.88251436e-01 -2.64206052e-01 8.70031774e-01 -4.73272391e-02
-4.64920044e-01 7.97957838e-01 3.30224782e-01 -3.52696240e-01
-2.82712489e-01 -8.05848062e-01 -3.55803639e-01 -3.87591690e-01
1.45358741e-01 6.13572598e-01 4.56079394e-01 -5.06966054... | [10.711631774902344, 7.877350330352783] |
d3bc3fc4-fb3c-4247-9243-70483708894e | llama-open-and-efficient-foundation-language-1 | 2302.13971 | null | https://arxiv.org/abs/2302.13971v1 | https://arxiv.org/pdf/2302.13971v1.pdf | LLaMA: Open and Efficient Foundation Language Models | We introduce LLaMA, a collection of foundation language models ranging from 7B to 65B parameters. We train our models on trillions of tokens, and show that it is possible to train state-of-the-art models using publicly available datasets exclusively, without resorting to proprietary and inaccessible datasets. In partic... | ['Guillaume Lample', 'Edouard Grave', 'Armand Joulin', 'Aurelien Rodriguez', 'Faisal Azhar', 'Eric Hambro', 'Naman Goyal', 'Baptiste Rozière', 'Timothée Lacroix', 'Marie-Anne Lachaux', 'Xavier Martinet', 'Gautier Izacard', 'Thibaut Lavril', 'Hugo Touvron'] | 2023-02-27 | llama-open-and-efficient-foundation-language | https://research.facebook.com/publications/llama-open-and-efficient-foundation-language-models/ | https://research.facebook.com/file/1574548786327032/LLaMA--Open-and-Efficient-Foundation-Language-Models.pdf | arxiv-2023-2 | ['math-word-problem-solving', 'multi-task-language-understanding', 'math-word-problem-solving', 'math-word-problem-solving'] | ['knowledge-base', 'methodology', 'reasoning', 'time-series'] | [-5.05362093e-01 -3.18572074e-01 -6.82029605e-01 -1.43722504e-01
-1.11361825e+00 -8.55218232e-01 7.20798612e-01 2.51047872e-02
-7.22899675e-01 8.47400188e-01 1.25288248e-01 -1.00343156e+00
8.93973261e-02 -5.62861800e-01 -7.80420482e-01 -3.23101103e-01
-3.65423441e-01 6.99202657e-01 3.30355108e-01 -5.12900829... | [10.797876358032227, 8.41159725189209] |
793d8276-9881-49e1-b9f7-3611d23225d7 | object-segmentation-by-mining-cross-modal | 2305.10469 | null | https://arxiv.org/abs/2305.10469v2 | https://arxiv.org/pdf/2305.10469v2.pdf | Object Segmentation by Mining Cross-Modal Semantics | Multi-sensor clues have shown promise for object segmentation, but inherent noise in each sensor, as well as the calibration error in practice, may bias the segmentation accuracy. In this paper, we propose a novel approach by mining the Cross-Modal Semantics to guide the fusion and decoding of multimodal features, with... | ['Radu Timofte', 'Guolei Sun', 'Cédric Demonceaux', 'Qiuping Jiang', 'Zhaochong An', 'Zhuyun Zhou', 'Jingjing Wang', 'Zongwei Wu'] | 2023-05-17 | null | null | null | null | ['specificity'] | ['natural-language-processing'] | [ 4.77672338e-01 -7.78267384e-02 -2.87272632e-01 -7.12949753e-01
-8.56342196e-01 -6.92314625e-01 4.86492395e-01 2.65537828e-01
-3.55437040e-01 3.37753147e-01 2.64012158e-01 1.20013170e-01
-3.11495155e-01 -6.02994263e-01 -7.85347819e-01 -9.57929730e-01
3.06401044e-01 2.80982792e-01 3.87427002e-01 -2.49627173... | [9.696731567382812, -0.7546918392181396] |
990eba05-e171-41b5-9f6f-d70d61726ba2 | truncated-affinity-maximization-one-class | 2306.00006 | null | https://arxiv.org/abs/2306.00006v2 | https://arxiv.org/pdf/2306.00006v2.pdf | Truncated Affinity Maximization: One-class Homophily Modeling for Graph Anomaly Detection | One prevalent property we find empirically in real-world graph anomaly detection (GAD) datasets is a one-class homophily, i.e., normal nodes tend to have strong connection/affinity with each other, while the homophily in abnormal nodes is significantly weaker than normal nodes. However, this anomaly-discriminative prop... | ['Guansong Pang', 'Hezhe Qiao'] | 2023-05-29 | null | null | null | null | ['graph-anomaly-detection'] | ['graphs'] | [-4.28111963e-02 4.95297343e-01 -2.49019906e-01 -4.98523861e-01
-1.23021461e-01 -3.24489325e-01 3.46763819e-01 5.71623862e-01
1.33133322e-01 3.24428588e-01 5.92966489e-02 -1.85105175e-01
-1.48813546e-01 -1.04963529e+00 -5.41085303e-01 -8.03354323e-01
-5.99083781e-01 6.27041936e-01 2.73845166e-01 -1.51142180... | [6.671387195587158, 5.809799671173096] |
7888f508-0d01-4880-8f7d-9e0dc4a6010e | vgr-net-a-view-invariant-gait-recognition | 1710.04803 | null | http://arxiv.org/abs/1710.04803v1 | http://arxiv.org/pdf/1710.04803v1.pdf | VGR-Net: A View Invariant Gait Recognition Network | Biometric identification systems have become immensely popular and important
because of their high reliability and efficiency. However person identification
at a distance, still remains a challenging problem. Gait can be seen as an
essential biometric feature for human recognition and identification. It can be
easily a... | ['Divyansh Aggarwal', 'Daksh Thapar', 'Aditya Nigam', 'Punjal Agarwal'] | 2017-10-13 | null | null | null | null | ['person-identification'] | ['computer-vision'] | [-7.62093952e-03 -8.59063447e-01 2.01779306e-01 -3.99010956e-01
-9.86311063e-02 -4.02201325e-01 4.33553904e-01 -3.29490937e-02
-8.23650002e-01 5.28173149e-01 -2.35099450e-01 1.08022965e-01
-1.09139886e-02 -7.96823800e-01 -2.48008654e-01 -8.06578934e-01
-7.37648532e-02 5.22830427e-01 1.02779068e-01 -4.69065383... | [14.16619873046875, 1.33912992477417] |
355194fe-d617-455f-a77a-73fb8f598c51 | label-semantic-knowledge-distillation-for | 2208.03763 | null | https://arxiv.org/abs/2208.03763v1 | https://arxiv.org/pdf/2208.03763v1.pdf | Label Semantic Knowledge Distillation for Unbiased Scene Graph Generation | The Scene Graph Generation (SGG) task aims to detect all the objects and their pairwise visual relationships in a given image. Although SGG has achieved remarkable progress over the last few years, almost all existing SGG models follow the same training paradigm: they treat both object and predicate classification in S... | ['Jun Xiao', 'Yi Yang', 'Jian Shao', 'Wenxiao Wang', 'Hanrong Shi', 'Long Chen', 'Lin Li'] | 2022-08-07 | null | null | null | null | ['scene-graph-generation', 'unbiased-scene-graph-generation'] | ['computer-vision', 'computer-vision'] | [ 4.80993390e-01 4.49607223e-01 -4.19759125e-01 -6.10854506e-01
-6.81685925e-01 -5.13084054e-01 7.19959021e-01 5.88496439e-02
3.36573347e-02 6.84355199e-01 -3.35785709e-02 -4.90635112e-02
1.04290983e-02 -6.41843736e-01 -6.93391860e-01 -7.93216765e-01
1.85245737e-01 6.12516999e-01 4.01024938e-01 2.31471702... | [10.234505653381348, 1.8156640529632568] |
9feff4dc-f308-40c5-b229-212b58c06eba | uncovering-energy-efficient-practices-in-deep | 2303.13972 | null | https://arxiv.org/abs/2303.13972v1 | https://arxiv.org/pdf/2303.13972v1.pdf | Uncovering Energy-Efficient Practices in Deep Learning Training: Preliminary Steps Towards Green AI | Modern AI practices all strive towards the same goal: better results. In the context of deep learning, the term "results" often refers to the achieved accuracy on a competitive problem set. In this paper, we adopt an idea from the emerging field of Green AI to consider energy consumption as a metric of equal importance... | ['Arie van Deursen', 'June Sallou', 'Daniel Feitosa', 'Luís Cruz', 'Tim Yarally'] | 2023-03-24 | null | null | null | null | ['bayesian-optimisation'] | ['methodology'] | [ 1.34087905e-01 5.93737401e-02 -2.26895228e-01 -1.07735731e-01
-4.12349373e-01 -6.13094807e-01 5.48803568e-01 3.97970248e-03
-9.62265790e-01 3.81091297e-01 -6.44034147e-02 -3.79868716e-01
-5.02978921e-01 -7.46412933e-01 -6.45206273e-01 -1.01903069e+00
2.11835802e-01 8.45831558e-02 -1.48310080e-01 3.35505493... | [8.426118850708008, 3.2479357719421387] |
c3556a8c-3e12-4efc-bac9-ef8ccbb1cc8a | boosting-multi-label-image-classification | 2205.10986 | null | https://arxiv.org/abs/2205.10986v1 | https://arxiv.org/pdf/2205.10986v1.pdf | Boosting Multi-Label Image Classification with Complementary Parallel Self-Distillation | Multi-Label Image Classification (MLIC) approaches usually exploit label correlations to achieve good performance. However, emphasizing correlation like co-occurrence may overlook discriminative features of the target itself and lead to model overfitting, thus undermining the performance. In this study, we propose a ge... | ['Bo Liu', 'Daniel Zeng', 'Luwen Huangfu', 'Fengtao Zhou', 'Sheng Huang', 'Jiazhi Xu'] | 2022-05-23 | null | null | null | null | ['multi-label-image-classification'] | ['computer-vision'] | [ 2.68123001e-01 -6.01941906e-03 -6.15165412e-01 -4.94272023e-01
-8.77013743e-01 -5.57429075e-01 5.94922483e-01 3.72137100e-01
7.40031227e-02 6.25989795e-01 -9.80641246e-02 -2.40783766e-01
-6.51331898e-03 -3.57843220e-01 -6.52427256e-01 -9.87175882e-01
1.40372247e-01 4.60500866e-01 -5.42743318e-02 2.09403589... | [9.710086822509766, 3.8230438232421875] |
af464d59-bc54-49ff-9e61-9e038018df7d | nafssr-stereo-image-super-resolution-using | 2204.08714 | null | https://arxiv.org/abs/2204.08714v2 | https://arxiv.org/pdf/2204.08714v2.pdf | NAFSSR: Stereo Image Super-Resolution Using NAFNet | Stereo image super-resolution aims at enhancing the quality of super-resolution results by utilizing the complementary information provided by binocular systems. To obtain reasonable performance, most methods focus on finely designing modules, loss functions, and etc. to exploit information from another viewpoint. This... | ['Wenqing Yu', 'Liangyu Chen', 'Xiaojie Chu'] | 2022-04-19 | null | null | null | null | ['stereo-image-super-resolution'] | ['computer-vision'] | [ 2.18269452e-01 -2.12127090e-01 -1.39320627e-01 -3.96332681e-01
-8.81425738e-01 -1.99707091e-01 6.14937425e-01 -6.74834490e-01
-1.35057092e-01 8.07879865e-01 6.60921812e-01 2.31511220e-01
3.10441740e-02 -5.88205218e-01 -7.79888570e-01 -5.17518878e-01
2.57611245e-01 -2.49191701e-01 2.11330041e-01 -5.45630455... | [10.884246826171875, -2.096611738204956] |
355a3c2f-f0e8-46fb-a1d7-ccd8c3301091 | pliers-a-popularity-based-recommender-system | 2307.02865 | null | https://arxiv.org/abs/2307.02865v1 | https://arxiv.org/pdf/2307.02865v1.pdf | PLIERS: a Popularity-Based Recommender System for Content Dissemination in Online Social Networks | In this paper, we propose a novel tag-based recommender system called PLIERS, which relies on the assumption that users are mainly interested in items and tags with similar popularity to those they already own. PLIERS is aimed at reaching a good tradeoff between algorithmic complexity and the level of personalization o... | ['Elena Pagani', 'Franca Delmastro', 'Mattia Giovanni Campana', 'Valerio Arnaboldi'] | 2023-07-06 | null | null | null | null | ['recommendation-systems'] | ['miscellaneous'] | [-4.23433632e-01 -1.89418003e-01 -5.12288749e-01 -2.21538007e-01
-1.19835407e-01 -5.01950324e-01 3.68426770e-01 5.26707947e-01
-5.48444510e-01 4.93648559e-01 1.90656051e-01 -1.08534433e-01
-5.58872163e-01 -8.09338987e-01 -2.98906296e-01 -3.55889201e-01
-4.86928821e-01 5.90023339e-01 8.49358916e-01 -5.37295699... | [9.94359016418457, 5.698623180389404] |
461a069b-bf7f-421a-812e-80a70cc02e7a | multi-scale-progressive-fusion-network-for | 2003.10985 | null | https://arxiv.org/abs/2003.10985v2 | https://arxiv.org/pdf/2003.10985v2.pdf | Multi-Scale Progressive Fusion Network for Single Image Deraining | Rain streaks in the air appear in various blurring degrees and resolutions due to different distances from their positions to the camera. Similar rain patterns are visible in a rain image as well as its multi-scale (or multi-resolution) versions, which makes it possible to exploit such complementary information for rai... | ['Chen Chen', 'Kui Jiang', 'Junjun Jiang', 'Zhongyuan Wang', 'Jiayi Ma', 'Baojin Huang', 'Yimin Luo', 'Peng Yi'] | 2020-03-24 | multi-scale-progressive-fusion-network-for-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Jiang_Multi-Scale_Progressive_Fusion_Network_for_Single_Image_Deraining_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Jiang_Multi-Scale_Progressive_Fusion_Network_for_Single_Image_Deraining_CVPR_2020_paper.pdf | cvpr-2020-6 | ['single-image-deraining'] | ['computer-vision'] | [ 6.61821365e-02 -7.04121172e-01 3.14156681e-01 -4.03379887e-01
-5.74129760e-01 -3.38023603e-01 8.46316516e-02 -2.62103319e-01
-2.33214289e-01 6.53675795e-01 1.50683105e-01 3.38384435e-02
-5.66808954e-02 -8.30219150e-01 -5.28822780e-01 -1.18082404e+00
2.24566668e-01 -2.82138258e-01 4.15902644e-01 -3.44655931... | [10.897415161132812, -3.247929811477661] |
6e99d627-f7c0-4435-a612-a2cd81b0f087 | 3d-face-arbitrary-style-transfer | 2303.07709 | null | https://arxiv.org/abs/2303.07709v1 | https://arxiv.org/pdf/2303.07709v1.pdf | 3D Face Arbitrary Style Transfer | Style transfer of 3D faces has gained more and more attention. However, previous methods mainly use images of artistic faces for style transfer while ignoring arbitrary style images such as abstract paintings. To solve this problem, we propose a novel method, namely Face-guided Dual Style Transfer (FDST). To begin with... | ['Haoqian Wang', 'Jiawei Zhou', 'Yuxiao Liu', 'Zhifang Liu', 'Yang Liu', 'Chendong Zhao', 'Yuanhao Cai', 'Yingshuang Zou', 'Xiangwen Deng'] | 2023-03-14 | null | null | null | null | ['face-reconstruction'] | ['computer-vision'] | [ 3.20166528e-01 -3.64519656e-03 9.19819325e-02 -5.73004067e-01
-6.28201783e-01 -4.93652850e-01 4.65137064e-01 -8.16708624e-01
1.25198379e-01 6.79184854e-01 2.17342108e-01 1.31474882e-01
2.47368574e-01 -9.50349808e-01 -6.21350169e-01 -8.10277760e-01
8.93090248e-01 4.21021491e-01 5.83395846e-02 -3.52059901... | [12.606134414672852, -0.19414867460727692] |
58a2ce9a-d7bf-4993-beda-60a64aa83f94 | team-pku-wict-mipl-pic-makeup-temporal-video | 2207.02687 | null | https://arxiv.org/abs/2207.02687v1 | https://arxiv.org/pdf/2207.02687v1.pdf | Team PKU-WICT-MIPL PIC Makeup Temporal Video Grounding Challenge 2022 Technical Report | In this technical report, we briefly introduce the solutions of our team `PKU-WICT-MIPL' for the PIC Makeup Temporal Video Grounding (MTVG) Challenge in ACM-MM 2022. Given an untrimmed makeup video and a step query, the MTVG aims to localize a temporal moment of the target makeup step in the video. To tackle this task,... | ['Yang Liu', 'Yuxin Peng', 'Ting Lei', 'Zhongjie Ye', 'Dejie Yang', 'Minghang Zheng'] | 2022-07-06 | null | null | null | null | ['video-grounding'] | ['computer-vision'] | [-1.07337888e-02 -1.49034873e-01 -5.47836423e-01 -1.79628760e-01
-1.01784551e+00 -6.08789384e-01 1.82053089e-01 6.62368685e-02
-4.81436014e-01 6.29510462e-01 3.71343702e-01 -1.31933063e-01
-2.74227887e-01 -3.98515105e-01 -9.14056540e-01 -2.32707724e-01
-3.85287911e-01 1.51518106e-01 6.31541610e-01 -7.69833401... | [9.798755645751953, 0.6093029379844666] |
cd5993d0-4e63-4773-a10e-da6931c6b83a | interactive-text-ranking-with-bayesian | 1911.10183 | null | https://arxiv.org/abs/1911.10183v3 | https://arxiv.org/pdf/1911.10183v3.pdf | Interactive Text Ranking with Bayesian Optimisation: A Case Study on Community QA and Summarisation | For many NLP applications, such as question answering and summarisation, the goal is to select the best solution from a large space of candidates to meet a particular user's needs. To address the lack of user-specific training data, we propose an interactive text ranking approach that actively selects pairs of candidat... | ['Yang Gao', 'Iryna Gurevych', 'Edwin Simpson'] | 2019-11-22 | null | null | null | null | ['small-data'] | ['computer-vision'] | [ 6.64301455e-01 6.26569152e-01 -2.12314561e-01 -3.29039395e-01
-1.61349332e+00 -6.09288275e-01 6.03863418e-01 8.00953925e-01
-5.67059636e-01 8.53678226e-01 7.39241719e-01 -1.93813384e-01
-3.90969753e-01 -6.45906985e-01 -4.29974139e-01 -2.77904481e-01
1.02977179e-01 1.04115653e+00 5.19981027e-01 -4.07876074... | [12.125195503234863, 8.581280708312988] |
49b1ae72-71e8-4719-a18f-626a8abaf8f8 | an-ensemble-based-approach-by-fine-tuning-the | 2011.05543 | null | https://arxiv.org/abs/2011.05543v1 | https://arxiv.org/pdf/2011.05543v1.pdf | An ensemble-based approach by fine-tuning the deep transfer learning models to classify pneumonia from chest X-ray images | Pneumonia is caused by viruses, bacteria, or fungi that infect the lungs, which, if not diagnosed, can be fatal and lead to respiratory failure. More than 250,000 individuals in the United States, mainly adults, are diagnosed with pneumonia each year, and 50,000 die from the disease. Chest Radiography (X-ray) is widely... | ['Sagar Kora Venu'] | 2020-11-11 | null | null | null | null | ['pneumonia-detection', 'respiratory-failure'] | ['medical', 'medical'] | [ 8.87079071e-03 -3.88698518e-01 -1.00182720e-01 2.00674519e-01
-3.57683659e-01 -2.91673064e-01 1.47086561e-01 1.96200311e-01
-6.88348532e-01 8.53540182e-01 -5.16183525e-02 -4.63155806e-01
-1.22514538e-01 -9.87521291e-01 -2.71160603e-01 -8.06005597e-01
2.19504163e-01 7.18913615e-01 4.66954857e-01 5.31980395... | [15.544670104980469, -1.7471439838409424] |
89b9db9e-4d80-4762-a21c-f26497492cf3 | ratio-preserving-half-cylindrical-warps-for | 1803.06655 | null | http://arxiv.org/abs/1803.06655v1 | http://arxiv.org/pdf/1803.06655v1.pdf | Ratio-Preserving Half-Cylindrical Warps for Natural Image Stitching | A novel warp for natural image stitching is proposed that utilizes the
property of cylindrical warp and a horizontal pixel selection strategy. The
proposed ratio-preserving half-cylindrical warp is a combination of homography
and cylindrical warps which guarantees alignment by homography and possesses
less projective d... | ['Tianli Liao', 'Yifang Xu', 'Jing Chen'] | 2018-03-18 | null | null | null | null | ['image-stitching'] | ['computer-vision'] | [ 5.89314938e-01 -3.71446609e-02 -1.74361065e-01 2.37966135e-01
-3.28239888e-01 -8.40808868e-01 5.30399561e-01 -3.85815680e-01
-2.14425594e-01 5.98910749e-01 4.74128634e-01 -1.18907012e-01
1.21290609e-01 -8.29054058e-01 -5.81303596e-01 -1.10367084e+00
2.51136124e-01 1.98206380e-01 7.84295440e-01 -3.32102418... | [9.385509490966797, -2.3630526065826416] |
3057658c-912c-4c1c-8495-511b1b42b0c3 | tcr-short-video-title-generation-and-cover | 2304.12561 | null | https://arxiv.org/abs/2304.12561v1 | https://arxiv.org/pdf/2304.12561v1.pdf | TCR: Short Video Title Generation and Cover Selection with Attention Refinement | With the widespread popularity of user-generated short videos, it becomes increasingly challenging for content creators to promote their content to potential viewers. Automatically generating appealing titles and covers for short videos can help grab viewers' attention. Existing studies on video captioning mostly focus... | ['Di Niu', 'Yu Xu', 'Hui Liu', 'Weidong Guo', 'Jiuding Yang', 'Yakun Yu'] | 2023-04-25 | null | null | null | null | ['video-captioning'] | ['computer-vision'] | [ 6.32938087e-01 -2.91261747e-02 -4.83397424e-01 -2.46273011e-01
-1.20703804e+00 -4.48592633e-01 4.76726592e-01 -1.13538496e-01
-4.76139449e-02 8.88905108e-01 7.61249483e-01 1.29821479e-01
3.29077274e-01 -3.40615869e-01 -1.03408039e+00 -3.86951029e-01
1.17444796e-02 1.66405454e-01 2.29414985e-01 -1.22409917... | [10.586666107177734, 0.5596999526023865] |
c42122c8-71cd-4000-9830-9ffb4d2cc86a | usb-a-unified-semi-supervised-learning | 2208.07204 | null | https://arxiv.org/abs/2208.07204v2 | https://arxiv.org/pdf/2208.07204v2.pdf | USB: A Unified Semi-supervised Learning Benchmark for Classification | Semi-supervised learning (SSL) improves model generalization by leveraging massive unlabeled data to augment limited labeled samples. However, currently, popular SSL evaluation protocols are often constrained to computer vision (CV) tasks. In addition, previous work typically trains deep neural networks from scratch, w... | ['Yue Zhang', 'Xing Xie', 'Jindong Wang', 'Bernt Schiele', 'Takahiro Shinozaki', 'Bhiksha Raj', 'Marios Savvides', 'Wei Ye', 'Satoshi Nakamura', 'Yu-Feng Li', 'Zhen Wu', 'Heli Qi', 'Lan-Zhe Guo', 'Zhi Zhou', 'Linyi Yang', 'RenJie Wang', 'Wenxin Hou', 'Ran Tao', 'Wang Sun', 'Yue Fan', 'Hao Chen', 'Yidong Wang'] | 2022-08-12 | null | null | null | null | ['classification'] | ['methodology'] | [-3.61225903e-02 -4.89627272e-01 -2.28537619e-01 -6.52099133e-01
-1.15049398e+00 -7.17050731e-01 4.09257352e-01 7.18453899e-02
-7.34087288e-01 4.83937025e-01 -2.52421290e-01 -5.69305539e-01
4.45669204e-01 -4.65526372e-01 -9.73572433e-01 -4.32525396e-01
1.20687939e-01 2.88604558e-01 1.29239753e-01 1.16327114... | [9.410334587097168, 2.2041139602661133] |
9ddaf78e-ab3b-49c0-babe-4ade700a25d9 | luminous-indoor-scene-generation-for-embodied | 2111.05527 | null | https://arxiv.org/abs/2111.05527v1 | https://arxiv.org/pdf/2111.05527v1.pdf | LUMINOUS: Indoor Scene Generation for Embodied AI Challenges | Learning-based methods for training embodied agents typically require a large number of high-quality scenes that contain realistic layouts and support meaningful interactions. However, current simulators for Embodied AI (EAI) challenges only provide simulated indoor scenes with a limited number of layouts. This paper p... | ['Gaurav S. Sukhatme', 'Jesse Thomason', 'Govind Thattai', 'Qiaozi Gao', 'Zhiwei Jia', 'Kaixiang Lin', 'Yizhou Zhao'] | 2021-11-10 | null | null | null | null | ['scene-generation', 'indoor-scene-synthesis'] | ['computer-vision', 'computer-vision'] | [ 2.99750328e-01 -1.15167670e-01 8.54268253e-01 -3.99403691e-01
-6.17173731e-01 -6.49654388e-01 8.49636078e-01 -2.84704775e-01
-3.28160405e-01 8.59781921e-01 2.15154007e-01 -1.45366535e-01
9.37474426e-03 -8.33169520e-01 -1.11036754e+00 -3.58739793e-01
-4.09806699e-01 5.37222922e-01 -2.41930097e-01 -5.16053438... | [4.433853626251221, 0.6369262933731079] |
c90bda37-07ab-4897-8e0a-9620ece1cc7a | linear-to-multi-linear-algebra-and-systems | 2304.10658 | null | https://arxiv.org/abs/2304.10658v1 | https://arxiv.org/pdf/2304.10658v1.pdf | Linear to multi-linear algebra and systems using tensors | In past few decades, tensor algebra also known as multi-linear algebra has been developed and customized as a tool to be used for various engineering applications. In particular, with the help of a special form of tensor contracted product, known as the Einstein Product and its properties, many of the known concepts fr... | ['Harry Leib', 'Adithya Venugopal', 'Divyanshu Pandey'] | 2023-04-20 | null | null | null | null | ['tensor-networks'] | ['methodology'] | [-7.53593147e-02 -2.75788426e-01 3.44963014e-01 -2.85040718e-02
8.04580227e-02 -6.48290098e-01 5.10920346e-01 -2.84419239e-01
-2.26862058e-02 4.45841104e-01 -1.60223171e-02 -3.56664687e-01
-8.22538853e-01 -5.54459929e-01 -5.08258529e-02 -9.33938801e-01
-7.86155343e-01 -5.67851290e-02 -3.56172383e-01 -7.73664296... | [7.445896148681641, 4.24371862411499] |
597e5503-5aa1-4b75-af1a-71757a9c22f9 | bert-based-multilingual-machine-comprehension | 2006.01432 | null | https://arxiv.org/abs/2006.01432v1 | https://arxiv.org/pdf/2006.01432v1.pdf | BERT Based Multilingual Machine Comprehension in English and Hindi | Multilingual Machine Comprehension (MMC) is a Question-Answering (QA) sub-task that involves quoting the answer for a question from a given snippet, where the question and the snippet can be in different languages. Recently released multilingual variant of BERT (m-BERT), pre-trained with 104 languages, has performed we... | ['Somil Gupta', 'Nilesh Khade'] | 2020-06-02 | null | null | null | null | ['multilingual-machine-comprehension'] | ['natural-language-processing'] | [-1.78229406e-01 -5.64758964e-02 2.49742791e-01 -5.71541190e-01
-2.03256178e+00 -9.61815953e-01 7.35869169e-01 1.68384731e-01
-7.58002758e-01 9.72369254e-01 4.22124773e-01 -8.78145576e-01
-1.20972566e-01 -4.53984618e-01 -9.04657304e-01 -2.34001532e-01
1.08616598e-01 1.19645858e+00 3.23473841e-01 -9.76486683... | [11.378890991210938, 8.307069778442383] |
39295f9c-ea6b-47c9-ba9a-8ef18a353e4c | rapid-training-of-quantum-recurrent-neural | 2207.00378 | null | https://arxiv.org/abs/2207.00378v2 | https://arxiv.org/pdf/2207.00378v2.pdf | Rapid training of quantum recurrent neural networks | Time series prediction is essential for human activities in diverse areas. A common approach to this task is to harness Recurrent Neural Networks (RNNs). However, while their predictions are quite accurate, their learning process is complex and, thus, time and energy consuming. Here, we propose to extend the concept of... | ['Bertrand Le Saux', 'Adam Buraczewski', 'Magdalena Stobińska', 'Michał Siemaszko'] | 2022-07-01 | null | null | null | null | ['time-series-prediction'] | ['time-series'] | [ 2.55139917e-01 4.28796634e-02 4.63351011e-02 7.23276734e-02
-5.18929720e-01 -3.38151395e-01 3.53117555e-01 -3.94933850e-01
-4.37630087e-01 1.07914031e+00 -4.29921061e-01 -2.34059319e-01
-1.45688951e-01 -1.04768634e+00 -5.79237998e-01 -1.02526200e+00
1.19241655e-01 1.28028348e-01 1.65360674e-01 -4.99168992... | [5.578086853027344, 4.932310104370117] |
3d763c11-b6d7-4dd5-a047-f66cf607e9f3 | magnification-prior-a-self-supervised-method | 2203.07707 | null | https://arxiv.org/abs/2203.07707v2 | https://arxiv.org/pdf/2203.07707v2.pdf | Magnification Prior: A Self-Supervised Method for Learning Representations on Breast Cancer Histopathological Images | This work presents a novel self-supervised pre-training method to learn efficient representations without labels on histopathology medical images utilizing magnification factors. Other state-of-theart works mainly focus on fully supervised learning approaches that rely heavily on human annotations. However, the scarcit... | ['Marcus Liwicki', 'Seiichi Uchida', 'Rajkumar Saini', 'Gustav Grund Pihlgren', 'Richa Upadhyay', 'Prakash Chandra Chhipa'] | 2022-03-15 | null | null | null | null | ['breast-cancer-histology-image-classification-1', 'breast-cancer-histology-image-classification', 'classification-of-breast-cancer-histology'] | ['computer-vision', 'medical', 'medical'] | [ 6.52319968e-01 6.85488224e-01 -5.44699669e-01 -5.55913925e-01
-1.28039229e+00 -3.48296195e-01 4.06079680e-01 7.34410644e-01
-6.01618528e-01 5.89508474e-01 1.54883787e-01 -3.59553814e-01
-2.33542666e-01 -4.92442399e-01 -7.89208829e-01 -9.05758679e-01
-2.78495178e-02 4.77299541e-01 3.28228273e-03 -7.73434639... | [14.937230110168457, -2.5496785640716553] |
2e25f604-4305-4773-8b76-017b2f1d5314 | set-to-sequence-ranking-based-concept-aware | 2306.04234 | null | https://arxiv.org/abs/2306.04234v1 | https://arxiv.org/pdf/2306.04234v1.pdf | Set-to-Sequence Ranking-based Concept-aware Learning Path Recommendation | With the development of the online education system, personalized education recommendation has played an essential role. In this paper, we focus on developing path recommendation systems that aim to generating and recommending an entire learning path to the given user in each session. Noticing that existing approaches ... | ['Yong Yu', 'Dingyin Xia', 'Kai Dong', 'Ruiming Tang', 'Menghui Zhu', 'Weiwen Liu', 'Weinan Zhang', 'Yakun Song', 'Jiarui Jin', 'Wei Xia', 'Jian Shen', 'Xianyu Chen'] | 2023-06-07 | null | null | null | null | ['knowledge-tracing'] | ['miscellaneous'] | [ 2.35042274e-02 -8.67774263e-02 -3.53555471e-01 -5.35282731e-01
-4.00263280e-01 -4.70713109e-01 3.67769688e-01 -3.86218652e-02
-1.30636394e-01 5.28245330e-01 5.47810853e-01 -6.07384682e-01
-4.41089541e-01 -9.40898180e-01 -7.48383224e-01 -3.90399456e-01
1.34793594e-01 1.43658131e-01 2.04938501e-01 -5.27691185... | [10.235649108886719, 5.989293575286865] |
d9ec330d-20ef-432f-b11e-f02c9d021f1d | are-you-telling-me-to-put-glasses-on-the-dog | 2306.02377 | null | https://arxiv.org/abs/2306.02377v1 | https://arxiv.org/pdf/2306.02377v1.pdf | "Are you telling me to put glasses on the dog?'' Content-Grounded Annotation of Instruction Clarification Requests in the CoDraw Dataset | Instruction Clarification Requests are a mechanism to solve communication problems, which is very functional in instruction-following interactions. Recent work has argued that the CoDraw dataset is a valuable source of naturally occurring iCRs. Beyond identifying when iCRs should be made, dialogue models should also be... | ['David Schlangen', 'Brielen Madureira'] | 2023-06-04 | null | null | null | null | ['instruction-following'] | ['natural-language-processing'] | [ 2.95321010e-02 8.34445894e-01 -2.29866177e-01 -3.17555666e-01
-3.56554449e-01 -7.20092416e-01 8.69773030e-01 3.62120420e-01
-1.88416138e-01 7.95805752e-01 1.09744263e+00 -6.70553744e-01
-2.48379588e-01 -6.77057981e-01 -9.65397712e-03 2.42383718e-01
4.62766111e-01 7.41123617e-01 5.49683094e-01 -1.17933059... | [12.6732759475708, 8.054206848144531] |
8118c620-9e9f-4424-aa73-026c7499c04a | exact-bias-correction-and-covariance | null | null | http://openaccess.thecvf.com/content_cvpr_2015/html/Freundlich_Exact_Bias_Correction_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Freundlich_Exact_Bias_Correction_2015_CVPR_paper.pdf | Exact Bias Correction and Covariance Estimation for Stereo Vision | We present an approach for correcting the bias in 3D reconstruction of points imaged by a calibrated stereo rig. Our analysis is based on the observation that, due to quantization error, a 3D point reconstructed by triangulation essentially represents an entire region in space. The true location of the world point tha... | ['Michael Zavlanos', 'Philippos Mordohai', 'Charles Freundlich'] | 2015-06-01 | null | null | null | cvpr-2015-6 | ['camera-localization'] | ['computer-vision'] | [ 2.50992000e-01 9.56694335e-02 2.92384088e-01 1.68771707e-02
-4.63940740e-01 -6.72980905e-01 5.04149020e-01 -3.51315401e-02
-5.08666635e-01 6.46935701e-01 5.10078557e-02 -2.20304251e-01
1.57487229e-01 -8.21644187e-01 -9.59211767e-01 -7.10709810e-01
2.19126493e-01 7.91932881e-01 4.16711807e-01 -1.02094635... | [8.732643127441406, -2.561777353286743] |
f552134c-e4bd-45d0-bb41-3d826a4e9203 | on-the-cross-lingual-transferability-of | 1910.11856 | null | https://arxiv.org/abs/1910.11856v3 | https://arxiv.org/pdf/1910.11856v3.pdf | On the Cross-lingual Transferability of Monolingual Representations | State-of-the-art unsupervised multilingual models (e.g., multilingual BERT) have been shown to generalize in a zero-shot cross-lingual setting. This generalization ability has been attributed to the use of a shared subword vocabulary and joint training across multiple languages giving rise to deep multilingual abstract... | ['Mikel Artetxe', 'Sebastian Ruder', 'Dani Yogatama'] | 2019-10-25 | on-the-cross-lingual-transferability-of-1 | https://aclanthology.org/2020.acl-main.421 | https://aclanthology.org/2020.acl-main.421.pdf | acl-2020-6 | ['cross-lingual-question-answering'] | ['natural-language-processing'] | [-4.48884249e-01 1.11786880e-01 -2.64952034e-01 -4.54821616e-01
-1.29383004e+00 -1.07180035e+00 8.32574248e-01 1.83334082e-01
-5.87019920e-01 8.40163291e-01 2.22963467e-01 -7.72974312e-01
1.71760976e-01 -7.13549137e-01 -1.12038493e+00 -2.38754213e-01
2.97521353e-01 7.97376812e-01 1.07781358e-01 -6.80141687... | [11.058006286621094, 9.893274307250977] |
ccbb3dda-10c0-401d-8be4-8b42244e2dcc | distantly-supervised-ner-with-partial | null | null | https://aclanthology.org/C18-1183 | https://aclanthology.org/C18-1183.pdf | Distantly Supervised NER with Partial Annotation Learning and Reinforcement Learning | A bottleneck problem with Chinese named entity recognition (NER) in new domains is the lack of annotated data. One solution is to utilize the method of distant supervision, which has been widely used in relation extraction, to automatically populate annotated training data without humancost. The distant supervision ass... | ['Yaosheng Yang', 'Zhengqiu He', 'Zhenghua Li', 'Wenliang Chen', 'Min Zhang'] | 2018-08-01 | distantly-supervised-ner-with-partial-1 | https://aclanthology.org/C18-1183 | https://aclanthology.org/C18-1183.pdf | coling-2018-8 | ['chinese-named-entity-recognition'] | ['natural-language-processing'] | [ 1.15450755e-01 2.47844428e-01 -8.41174647e-02 -4.53900635e-01
-7.53736913e-01 -5.08733332e-01 2.53521591e-01 1.23647295e-01
-7.05560386e-01 1.21826530e+00 3.47350121e-01 -1.84066534e-01
1.68997213e-01 -8.15207422e-01 -5.96177101e-01 -4.38638121e-01
3.44762057e-01 4.39117193e-01 4.74626482e-01 -1.44924000... | [9.713995933532715, 9.605951309204102] |
970910a5-be9f-4d23-b61a-c9182b83348e | new-method-for-optimization-of-license-plate | 1407.6510 | null | http://arxiv.org/abs/1407.6510v1 | http://arxiv.org/pdf/1407.6510v1.pdf | New Method for Optimization of License Plate Recognition system with Use of Edge Detection and Connected Component | License Plate recognition plays an important role on the traffic monitoring
and parking management systems. In this paper, a fast and real time method has
been proposed which has an appropriate application to find tilt and poor
quality plates. In the proposed method, at the beginning, the image is
converted into binary... | ['Hamid Reza Shayegh', 'Reza Azad'] | 2014-07-24 | null | null | null | null | ['license-plate-recognition'] | ['computer-vision'] | [ 8.70154181e-04 -5.64598560e-01 1.14064410e-01 -5.46627603e-02
-1.90250240e-02 -3.74918848e-01 3.36383998e-01 -9.83454287e-02
-6.51498795e-01 8.18603218e-01 -4.71629918e-01 -3.09295595e-01
9.06371102e-02 -8.23675811e-01 -1.87285647e-01 -7.91199803e-01
5.78655899e-01 6.10913277e-01 7.71291196e-01 -1.45610064... | [9.791257858276367, -4.970582485198975] |
dbc404d1-876d-4915-abb0-6cea0c39fa08 | lcdnet-deep-loop-closure-detection-for-lidar | 2103.05056 | null | https://arxiv.org/abs/2103.05056v4 | https://arxiv.org/pdf/2103.05056v4.pdf | LCDNet: Deep Loop Closure Detection and Point Cloud Registration for LiDAR SLAM | Loop closure detection is an essential component of Simultaneous Localization and Mapping (SLAM) systems, which reduces the drift accumulated over time. Over the years, several deep learning approaches have been proposed to address this task, however their performance has been subpar compared to handcrafted techniques,... | ['Abhinav Valada', 'Matteo Vaghi', 'Daniele Cattaneo'] | 2021-03-08 | null | null | null | null | ['loop-closure-detection'] | ['computer-vision'] | [-6.02082610e-02 -1.11416712e-01 -1.09820351e-01 -7.60195494e-01
-9.69771564e-01 -3.74658823e-01 7.63588488e-01 3.26633543e-01
-7.80728042e-01 3.51105571e-01 -4.54553187e-01 -2.03041807e-01
-1.97509021e-01 -8.61366570e-01 -1.21946037e+00 -2.54576147e-01
-1.99374571e-01 1.01753843e+00 5.37340522e-01 -3.74232113... | [7.452520847320557, -2.237605333328247] |
0584b942-fa84-4bb4-a317-8a415e667523 | glm-130b-an-open-bilingual-pre-trained-model | 2210.02414 | null | https://arxiv.org/abs/2210.02414v1 | https://arxiv.org/pdf/2210.02414v1.pdf | GLM-130B: An Open Bilingual Pre-trained Model | We introduce GLM-130B, a bilingual (English and Chinese) pre-trained language model with 130 billion parameters. It is an attempt to open-source a 100B-scale model at least as good as GPT-3 and unveil how models of such a scale can be successfully pre-trained. Over the course of this effort, we face numerous unexpected... | ['Jie Tang', 'Yuxiao Dong', 'Peng Zhang', 'WenGuang Chen', 'Jidong Zhai', 'Yufei Xue', 'Zixuan Ma', 'Weng Lam Tam', 'Xiao Xia', 'Wendi Zheng', 'Yifan Xu', 'Zhuoyi Yang', 'Ming Ding', 'Hanyu Lai', 'Zihan Wang', 'Zhengxiao Du', 'Xiao Liu', 'Aohan Zeng'] | 2022-10-05 | null | null | null | null | ['multi-task-language-understanding'] | ['methodology'] | [-3.35952997e-01 -2.42814660e-01 -4.27917838e-01 -3.23808223e-01
-1.36283875e+00 -4.60441321e-01 3.73356074e-01 9.52994823e-02
-5.81622064e-01 6.34038925e-01 9.71058309e-02 -1.10384333e+00
2.63400525e-01 -6.46987200e-01 -7.93000698e-01 -2.49061495e-01
-2.36137763e-01 4.85739172e-01 -3.46812583e-03 -3.29128236... | [8.676891326904297, 3.5060973167419434] |
8281a55b-e57e-4988-a602-b1409f276777 | learning-elimination-ordering-for-tree | null | null | https://openreview.net/forum?id=aZ7wAnYs9v1 | https://openreview.net/pdf?id=aZ7wAnYs9v1 | Learning Elimination Ordering for Tree Decomposition Problem | We propose a Reinforcement Learning-based approach to approximately solve the Tree Decomposition problem.
Recently, it was shown that learned heuristics could successfully solve combinatorial problems.
We establish that our approach successfully generalizes from small graphs, where an optimal Tree Decomposition can b... | ['Ivan Oseledets', 'Roman Schutski', 'Taras Khakhulin'] | 2020-10-17 | null | null | null | neurips-workshop-lmca-2020-12 | ['tree-decomposition'] | ['graphs'] | [-7.75721148e-02 6.57042861e-01 -4.23496008e-01 1.44534528e-01
-8.87215436e-01 -7.65395045e-01 1.28343642e-01 4.70453858e-01
-1.33211687e-01 1.38315642e+00 -3.39880347e-01 -4.38134551e-01
-6.25347733e-01 -1.17144537e+00 -7.92434394e-01 -7.27384627e-01
-4.76162583e-01 1.07212245e+00 3.94038528e-01 -2.27799922... | [5.162932872772217, 2.851140022277832] |
726e000f-9674-4032-b797-8915c256202a | forknet-multi-branch-volumetric-semantic | 1909.01106 | null | https://arxiv.org/abs/1909.01106v1 | https://arxiv.org/pdf/1909.01106v1.pdf | ForkNet: Multi-branch Volumetric Semantic Completion from a Single Depth Image | We propose a novel model for 3D semantic completion from a single depth image, based on a single encoder and three separate generators used to reconstruct different geometric and semantic representations of the original and completed scene, all sharing the same latent space. To transfer information between the geometri... | ['Federico Tombari', 'Yida Wang', 'David Joseph Tan', 'Nassir Navab'] | 2019-09-03 | forknet-multi-branch-volumetric-semantic-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Wang_ForkNet_Multi-Branch_Volumetric_Semantic_Completion_From_a_Single_Depth_Image_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Wang_ForkNet_Multi-Branch_Volumetric_Semantic_Completion_From_a_Single_Depth_Image_ICCV_2019_paper.pdf | iccv-2019-10 | ['3d-semantic-scene-completion'] | ['computer-vision'] | [ 3.34355026e-01 4.75385875e-01 2.13609576e-01 -4.50928360e-01
-7.82087803e-01 -3.99032563e-01 6.63881838e-01 -1.40967697e-01
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1.18197411e-01 -1.15296972e+00 -1.07752573e+00 -4.38983947e-01
1.35116667e-01 5.96802592e-01 3.91588397e-02 8.94106328... | [8.928068161010742, -3.2912886142730713] |
c2fe8ca1-4f58-4e9d-b138-7cd0dc8fe569 | offline-reinforcement-learning-with-in-sample | null | null | https://openreview.net/forum?id=68n2s9ZJWF8 | https://openreview.net/pdf?id=68n2s9ZJWF8 | Offline Reinforcement Learning with In-sample Q-Learning | Offline reinforcement learning requires reconciling two conflicting aims: learning a policy that improves over the behavior policy that collected the dataset, while at the same time minimizing the deviation from the behavior policy so as to avoid errors due to distributional shift. This tradeoff is critical, because mo... | ['Sergey Levine', 'Ashvin Nair', 'Ilya Kostrikov'] | 2021-09-29 | null | null | null | iclr-2022-4 | ['d4rl'] | ['robots'] | [-1.56249598e-01 2.31753871e-01 -5.98332405e-01 -2.03694031e-01
-1.19957852e+00 -1.07239091e+00 2.88126945e-01 1.58394858e-01
-9.35478508e-01 1.10596025e+00 -1.92213152e-02 -4.86023486e-01
-1.27079979e-01 -7.81742573e-01 -1.05399489e+00 -9.94737864e-01
-1.20794155e-01 7.92755187e-01 2.87454594e-02 -1.30539656... | [4.054871559143066, 2.1754000186920166] |
ca2b2349-209d-4dcd-90cf-59d9ea830ff7 | on-differentially-private-federated-linear | 2302.13945 | null | https://arxiv.org/abs/2302.13945v2 | https://arxiv.org/pdf/2302.13945v2.pdf | On Differentially Private Federated Linear Contextual Bandits | We consider cross-silo federated linear contextual bandit (LCB) problem under differential privacy, where multiple silos (agents) interact with the local users and communicate via a central server to realize collaboration while without sacrificing each user's privacy. We identify three issues in the state-of-the-art: (... | ['Sayak Ray Chowdhury', 'Xingyu Zhou'] | 2023-02-27 | null | null | null | null | ['multi-armed-bandits'] | ['miscellaneous'] | [ 3.02758187e-01 2.28888065e-01 -2.63866037e-01 -3.43476593e-01
-1.11070395e+00 -1.16710746e+00 3.54006559e-01 1.59484133e-01
-4.31103140e-01 1.01169932e+00 2.77910024e-01 -6.20694637e-01
-4.52615023e-01 -7.41307199e-01 -1.09182525e+00 -1.06043720e+00
-6.38957918e-02 1.84361488e-01 -1.45273060e-01 5.25674550... | [5.900582790374756, 6.553079605102539] |
44c36d4c-49c2-428e-be9c-ae693e6e4452 | learning-canonical-view-representation-for-3d | 2108.07084 | null | https://arxiv.org/abs/2108.07084v2 | https://arxiv.org/pdf/2108.07084v2.pdf | Learning Canonical View Representation for 3D Shape Recognition with Arbitrary Views | In this paper, we focus on recognizing 3D shapes from arbitrary views, i.e., arbitrary numbers and positions of viewpoints. It is a challenging and realistic setting for view-based 3D shape recognition. We propose a canonical view representation to tackle this challenge. We first transform the original features of arbi... | ['Jian Sun', 'Xing Sun', 'Fudong Wang', 'Yifei Gong', 'Xin Wei'] | 2021-08-16 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Wei_Learning_Canonical_View_Representation_for_3D_Shape_Recognition_With_Arbitrary_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Wei_Learning_Canonical_View_Representation_for_3D_Shape_Recognition_With_Arbitrary_ICCV_2021_paper.pdf | iccv-2021-1 | ['3d-shape-recognition', '3d-shape-representation'] | ['computer-vision', 'computer-vision'] | [-9.41246003e-02 -3.58415753e-01 8.30639452e-02 -7.16169417e-01
-5.84437490e-01 -1.06091452e+00 7.01431036e-01 -5.84521592e-01
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-1.47175863e-01 -6.68689907e-01 -6.84155703e-01 -8.26186359e-01
5.31279504e-01 6.02460504e-01 9.46986079e-02 2.30399258... | [8.193814277648926, -3.6220157146453857] |
1e6b01f2-248b-48a4-8d9f-b39cef99c25a | explain-your-move-understanding-agent-actions | null | null | https://openreview.net/forum?id=SJgzLkBKPB | https://openreview.net/pdf?id=SJgzLkBKPB | Explain Your Move: Understanding Agent Actions Using Focused Feature Saliency | As deep reinforcement learning (RL) is applied to more tasks, there is a need to visualize and understand the behavior of learned agents. Saliency maps explain agent behavior by highlighting the features of the input state that are most relevant for the agent in taking an action. Existing perturbation-based approaches ... | ['Shripad Deshmukh', 'Sukriti Verma', 'Sameer Singh', 'Nikaash Puri', 'Piyush Gupta', 'Balaji Krishnamurthy', 'Dhruv Kayastha'] | 2020-05-01 | null | null | null | iclr-2020-1 | ['board-games'] | ['playing-games'] | [ 1.98212266e-01 3.81234348e-01 9.79114547e-02 -7.04974383e-02
-9.76225585e-02 -5.56576490e-01 7.29747951e-01 5.11857390e-01
-5.98103583e-01 1.05107927e+00 4.58323300e-01 -1.65319532e-01
-3.95875931e-01 -4.95903045e-01 -7.34104991e-01 -6.61588848e-01
-3.59422743e-01 3.50726545e-01 5.63517392e-01 -8.60965550... | [4.035083770751953, 1.5099034309387207] |
61438a1c-dffb-4f78-b5ce-bb21b2f53f0a | deep-idempotent-network-for-efficient-single | 2210.07122 | null | https://arxiv.org/abs/2210.07122v2 | https://arxiv.org/pdf/2210.07122v2.pdf | Deep Idempotent Network for Efficient Single Image Blind Deblurring | Single image blind deblurring is highly ill-posed as neither the latent sharp image nor the blur kernel is known. Even though considerable progress has been made, several major difficulties remain for blind deblurring, including the trade-off between high-performance deblurring and real-time processing. Besides, we obs... | ['Xin Yu', 'Yuchao Dai', 'Zhexiong Wan', 'Yuxin Mao'] | 2022-10-13 | null | null | null | null | ['single-image-blind-deblurring'] | ['computer-vision'] | [ 1.65522456e-01 -6.23484433e-01 -9.48840231e-02 3.34493443e-02
-5.89239419e-01 -4.81587261e-01 5.49678922e-01 -7.86874115e-01
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-6.02174960e-02 -9.18336436e-02 -5.64872324e-01 -7.57225811e-01
1.02839947e-01 -2.01491803e-01 -3.20875049e-02 8.85605067... | [11.575082778930664, -2.6668639183044434] |
992cdb45-7687-4906-96e4-90e26589bb5d | geomagnetic-field-influences-probabilistic | 2306.16292 | null | https://arxiv.org/abs/2306.16292v1 | https://arxiv.org/pdf/2306.16292v1.pdf | Geomagnetic field influences probabilistic abstract decision-making in humans | To resolve disputes or determine the order of things, people commonly use binary choices such as tossing a coin, even though it is obscure whether the empirical probability equals to the theoretical probability. The geomagnetic field (GMF) is broadly applied as a sensory cue for various movements in many organisms incl... | ['Yongkuk Kim', 'Soo-Chan Kim', 'Yong-Hwan Kim', 'Soo Hyun Jeong', 'In-Taek Oh', 'Kwon-Seok Chae'] | 2023-06-28 | null | null | null | null | ['decision-making'] | ['reasoning'] | [ 3.66524726e-01 -1.30811438e-01 7.71279782e-02 3.09181251e-02
-1.61710177e-02 -3.70843053e-01 6.34860277e-01 -1.90876365e-01
-5.35776794e-01 8.97776425e-01 -3.86337861e-02 -4.97120768e-01
-5.91356099e-01 -7.04629660e-01 -7.90615678e-01 -1.29554570e+00
-1.73615739e-01 2.30661482e-01 4.33797896e-01 -5.88879228... | [5.74675989151001, 4.7952775955200195] |
21740a03-14d4-45ba-bddc-796921391fff | audio-visual-understanding-of-passenger-1 | 2007.03876 | null | https://arxiv.org/abs/2007.03876v1 | https://arxiv.org/pdf/2007.03876v1.pdf | Audio-Visual Understanding of Passenger Intents for In-Cabin Conversational Agents | Building multimodal dialogue understanding capabilities situated in the in-cabin context is crucial to enhance passenger comfort in autonomous vehicle (AV) interaction systems. To this end, understanding passenger intents from spoken interactions and vehicle vision systems is a crucial component for developing contextu... | ['Shachi H. Kumar', 'Eda Okur', 'Saurav Sahay', 'Lama Nachman'] | 2020-07-08 | audio-visual-understanding-of-passenger | https://aclanthology.org/2020.challengehml-1.7 | https://aclanthology.org/2020.challengehml-1.7.pdf | ws-2020-7 | ['dialogue-understanding'] | ['natural-language-processing'] | [-3.33574623e-01 3.40038091e-01 2.78912455e-01 -6.67369664e-01
-1.08827305e+00 -9.60098743e-01 9.43764627e-01 4.59110551e-02
-4.30530518e-01 5.43662906e-01 5.32427847e-01 -4.94838417e-01
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2.56825924e-01 4.20250237e-01 2.76623685e-02 -8.13496113... | [10.898118019104004, 1.2109346389770508] |
0487b625-7952-4aad-a920-d0b0ce7ccbc9 | masil-towards-maximum-separable-class | 2304.05362 | null | https://arxiv.org/abs/2304.05362v1 | https://arxiv.org/pdf/2304.05362v1.pdf | MASIL: Towards Maximum Separable Class Representation for Few Shot Class Incremental Learning | Few Shot Class Incremental Learning (FSCIL) with few examples per class for each incremental session is the realistic setting of continual learning since obtaining large number of annotated samples is not feasible and cost effective. We present the framework MASIL as a step towards learning the maximal separable classi... | ['Anant Khandelwal'] | 2023-04-08 | null | null | null | null | ['class-incremental-learning', 'few-shot-class-incremental-learning'] | ['computer-vision', 'methodology'] | [ 3.18135887e-01 1.51488334e-01 -3.08789790e-01 -5.68984449e-01
-6.98385000e-01 -4.52003360e-01 2.65651077e-01 3.08412075e-01
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-3.85007143e-01 7.09392667e-01 5.78678370e-01 1.62235647... | [9.910314559936523, 3.1875011920928955] |
169f3613-2db9-4beb-8462-7d10d1049052 | st-nsurl-2019-shared-task-semantic-question | null | null | https://aclanthology.org/2019.nsurl-1.12 | https://aclanthology.org/2019.nsurl-1.12.pdf | ST NSURL 2019 Shared Task: Semantic Question Similarity in Arabic | null | ['Abed Alhakim Freihat', 'Besma Benaziz', 'Mourad Abbas', 'Mohamed Lichouri'] | null | null | null | null | nsurl-2019-9 | ['question-similarity'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.340472221374512, 3.732516288757324] |
8b0d8c6c-0bd6-4ac9-94f5-e17a6a9fde38 | the-pitfalls-of-sample-selection-a-case-study | 2108.05386 | null | https://arxiv.org/abs/2108.05386v1 | https://arxiv.org/pdf/2108.05386v1.pdf | The Pitfalls of Sample Selection: A Case Study on Lung Nodule Classification | Using publicly available data to determine the performance of methodological contributions is important as it facilitates reproducibility and allows scrutiny of the published results. In lung nodule classification, for example, many works report results on the publicly available LIDC dataset. In theory, this should all... | ['Julia A. Schnabel', 'Ben Glocker', 'Sujal Desai', 'Arjun Nair', 'Sam Ellis', 'Octavio E. Martinez Manzanera', 'Loic Le Folgoc', 'Kyriaki-Margarita Bintsi', 'Vasileios Baltatzis'] | 2021-08-11 | null | null | null | null | ['lung-nodule-classification'] | ['medical'] | [ 2.55973309e-01 1.29096970e-01 -3.28245997e-01 -2.41915286e-01
-1.26851487e+00 -6.32043779e-01 5.54337978e-01 2.71615535e-01
-5.65701246e-01 7.09926903e-01 2.60591209e-01 -4.92778599e-01
-3.86584491e-01 -4.60917354e-01 -2.65361011e-01 -1.05485296e+00
1.95622087e-01 6.67358696e-01 3.66236508e-01 2.79289126... | [15.165529251098633, -2.7236838340759277] |
9a74f5d5-828f-463a-ab5a-c09d209edab2 | mtlts-a-multi-task-framework-to-obtain | 2112.05798 | null | https://arxiv.org/abs/2112.05798v1 | https://arxiv.org/pdf/2112.05798v1.pdf | MTLTS: A Multi-Task Framework To Obtain Trustworthy Summaries From Crisis-Related Microblogs | Occurrences of catastrophes such as natural or man-made disasters trigger the spread of rumours over social media at a rapid pace. Presenting a trustworthy and summarized account of the unfolding event in near real-time to the consumers of such potentially unreliable information thus becomes an important task. In this ... | ['Niloy Ganguly', 'Pawan Goyal', 'Koustav Rudra', 'Sourangshu Bhattacharya', 'Hari Chandana Peruri', 'Uppada Vishnu', 'Rajdeep Mukherjee'] | 2021-12-10 | null | null | null | null | ['rumour-detection'] | ['natural-language-processing'] | [-4.41728346e-02 4.94168073e-01 -1.55033231e-01 -3.50535542e-01
-1.55043793e+00 -3.79906416e-01 1.00381005e+00 8.71343553e-01
-3.16879243e-01 8.51825893e-01 8.36827755e-01 -1.54573724e-01
4.91730243e-01 -5.45038998e-01 -5.74031055e-01 -3.72191519e-01
-2.98325717e-01 6.42405987e-01 1.09842114e-01 -3.81853342... | [8.217247009277344, 10.126784324645996] |
7a2de21f-e194-445e-a3d8-370c84451823 | comparing-the-performance-of-cnns-and-shallow | null | null | https://aclanthology.org/2021.vardial-1.12 | https://aclanthology.org/2021.vardial-1.12.pdf | Comparing the Performance of CNNs and Shallow Models for Language Identification | In this work we compare the performance of convolutional neural networks and shallow models on three out of the four language identification shared tasks proposed in the VarDial Evaluation Campaign 2021. In our experiments, convolutional neural networks and shallow models yielded comparable performance in the Romanian ... | ['Andrea Ceolin'] | null | null | null | null | eacl-vardial-2021-4 | ['dialect-identification'] | ['natural-language-processing'] | [-4.66508389e-01 -2.31169000e-01 -1.81275263e-01 -4.76313442e-01
-8.18241477e-01 -8.11096072e-01 1.20780087e+00 -1.71974733e-01
-9.26755846e-01 6.25941098e-01 5.58253229e-01 -6.29919231e-01
4.10108678e-02 -3.33479822e-01 -9.53047350e-02 -3.14511836e-01
9.84351486e-02 1.21316051e+00 -2.14538857e-01 -6.03423655... | [10.247540473937988, 10.633569717407227] |
f3b6e5e5-1967-4e18-a499-8311b2e87309 | graphmapper-efficient-visual-navigation-by | 2205.08325 | null | https://arxiv.org/abs/2205.08325v1 | https://arxiv.org/pdf/2205.08325v1.pdf | GraphMapper: Efficient Visual Navigation by Scene Graph Generation | Understanding the geometric relationships between objects in a scene is a core capability in enabling both humans and autonomous agents to navigate in new environments. A sparse, unified representation of the scene topology will allow agents to act efficiently to move through their environment, communicate the environm... | ['Rakesh Kumar', 'Supun Samarasekera', 'Han-Pang Chiu', 'Niluthpol Chowdhury Mithun', 'Zachary Seymour'] | 2022-05-17 | null | null | null | null | ['scene-graph-generation'] | ['computer-vision'] | [ 8.09954479e-02 8.37894380e-02 1.68241173e-01 -4.55982089e-01
2.63175517e-02 -8.86166990e-01 7.15399086e-01 2.54440457e-01
-4.60770279e-01 5.15045047e-01 6.88464753e-03 -5.37329197e-01
4.24510464e-02 -1.10875607e+00 -8.02786112e-01 -3.18479717e-01
-2.93953240e-01 6.30515993e-01 4.72152352e-01 -4.44425136... | [4.540118217468262, 0.595683217048645] |
16a4c169-f8af-4ebf-b74a-66f3c74df368 | towards-omni-generalizable-neural-methods-for | 2305.19587 | null | https://arxiv.org/abs/2305.19587v2 | https://arxiv.org/pdf/2305.19587v2.pdf | Towards Omni-generalizable Neural Methods for Vehicle Routing Problems | Learning heuristics for vehicle routing problems (VRPs) has gained much attention due to the less reliance on hand-crafted rules. However, existing methods are typically trained and tested on the same task with a fixed size and distribution (of nodes), and hence suffer from limited generalization performance. This pape... | ['Jie Zhang', 'Zhiguang Cao', 'Wen Song', 'Yaoxin Wu', 'Jianan Zhou'] | 2023-05-31 | null | null | null | null | ['combinatorial-optimization'] | ['methodology'] | [ 6.47995844e-02 7.01365694e-02 -5.34140527e-01 -4.48900372e-01
-9.49574530e-01 -7.38155723e-01 3.04003924e-01 4.76416573e-02
-2.65360951e-01 9.42322195e-01 -4.11836356e-01 -7.09150195e-01
-4.93083268e-01 -8.71554136e-01 -1.13070464e+00 -6.74834490e-01
-3.52375031e-01 7.94084430e-01 7.49202147e-02 -1.75424471... | [5.077127933502197, 2.7832467555999756] |
cd884e8e-f5b6-4e3a-8fbb-d0a1e7554a99 | vision-based-autonomous-car-racing-using-deep | 2107.08325 | null | https://arxiv.org/abs/2107.08325v1 | https://arxiv.org/pdf/2107.08325v1.pdf | Vision-Based Autonomous Car Racing Using Deep Imitative Reinforcement Learning | Autonomous car racing is a challenging task in the robotic control area. Traditional modular methods require accurate mapping, localization and planning, which makes them computationally inefficient and sensitive to environmental changes. Recently, deep-learning-based end-to-end systems have shown promising results for... | ['Ming Liu', 'Yuxuan Liu', 'Huaiyang Huang', 'Hengli Wang', 'Peide Cai'] | 2021-07-18 | null | null | null | null | ['carracing-v0'] | ['playing-games'] | [-4.11654800e-01 6.41633794e-02 -1.49892882e-01 -1.55785739e-01
-7.63854742e-01 -4.20159519e-01 5.16268253e-01 -1.99522123e-01
-5.54756880e-01 7.63243020e-01 -3.54121983e-01 -2.87221909e-01
-1.62980724e-02 -5.65240800e-01 -1.19749391e+00 -6.29862487e-01
-9.68598574e-02 8.00109208e-01 5.32388926e-01 -7.10643411... | [4.831624984741211, 1.1373673677444458] |
12649243-99c7-4525-b7ee-5d2c0a2a171e | monotonicity-for-ai-ethics-and-society-an | 2301.07060 | null | https://arxiv.org/abs/2301.07060v1 | https://arxiv.org/pdf/2301.07060v1.pdf | Monotonicity for AI ethics and society: An empirical study of the monotonic neural additive model in criminology, education, health care, and finance | Algorithm fairness in the application of artificial intelligence (AI) is essential for a better society. As the foundational axiom of social mechanisms, fairness consists of multiple facets. Although the machine learning (ML) community has focused on intersectionality as a matter of statistical parity, especially in di... | ['Luyao Zhang', 'Dangxing Chen'] | 2023-01-17 | null | null | null | null | ['additive-models'] | ['methodology'] | [ 3.61691654e-01 4.99352008e-01 -5.23833752e-01 -4.94272441e-01
2.55793333e-01 -2.98282318e-02 4.52065855e-01 5.62615180e-03
-7.36681998e-01 1.17878664e+00 1.50515556e-01 -7.57510185e-01
-7.33227313e-01 -7.72451520e-01 -4.21879679e-01 -1.79509819e-01
2.49236107e-01 3.95745873e-01 -4.67587322e-01 -2.41350546... | [8.839187622070312, 5.544148921966553] |
810ca3d9-3490-451c-a498-2780cfd28a8d | nonparametric-forest-structured-neural-topic | null | null | https://aclanthology.org/2022.coling-1.228 | https://aclanthology.org/2022.coling-1.228.pdf | Nonparametric Forest-Structured Neural Topic Modeling | Neural topic models have been widely used in discovering the latent semantics from a corpus. Recently, there are several researches on hierarchical neural topic models since the relationships among topics are valuable for data analysis and exploration. However, the existing hierarchical neural topic models are limited ... | ['Yanghui Rao', 'Xuewen Zhang', 'Zhihong Zhang'] | null | null | null | null | coling-2022-10 | ['topic-models'] | ['natural-language-processing'] | [-8.26086998e-02 6.21627748e-01 -6.89841449e-01 -6.40078187e-01
-4.00969595e-01 1.95239466e-02 5.49238324e-01 1.78770959e-01
2.28401870e-01 6.49008691e-01 6.92855537e-01 -7.78606907e-02
-8.80706757e-02 -1.18562877e+00 -4.81051654e-01 -7.34662771e-01
-4.19855088e-01 8.72461975e-01 4.75256711e-01 2.36593485... | [10.383428573608398, 6.937257289886475] |
b6ab1c9a-b997-4b17-89f0-9a859cbb3d18 | machine-learning-for-uav-propeller-fault | 2302.01556 | null | https://arxiv.org/abs/2302.01556v1 | https://arxiv.org/pdf/2302.01556v1.pdf | Machine Learning for UAV Propeller Fault Detection based on a Hybrid Data Generation Model | This paper describes the development of an on-board data-driven system that can monitor and localize the fault in a quadrotor unmanned aerial vehicle (UAV) and at the same time, evaluate the degree of damage of the fault under real scenarios. To achieve offline training data generation, a hybrid approach is proposed fo... | ['Y. F. Zhang', 'C. F. Li', 'F. Liao', 'W. Zhang', 'J. J. Tong'] | 2023-02-03 | null | null | null | null | ['fault-detection'] | ['miscellaneous'] | [-2.68377781e-01 -1.54038787e-01 5.38945556e-01 9.95019227e-02
2.75021166e-01 -6.96616292e-01 1.49883360e-01 1.25372201e-01
2.47774765e-01 4.38582689e-01 -7.75928915e-01 -4.00286287e-01
-5.33166289e-01 -8.52099478e-01 -8.52374971e-01 -7.26570368e-01
-3.10144931e-01 4.18684423e-01 -4.16524708e-03 -3.32859337... | [6.7830491065979, 2.377358913421631] |
3188b9d1-66fc-4c69-a37b-fb99b883afef | dutch-humor-detection-by-generating-negative | 2010.13652 | null | https://arxiv.org/abs/2010.13652v1 | https://arxiv.org/pdf/2010.13652v1.pdf | Dutch Humor Detection by Generating Negative Examples | Detecting if a text is humorous is a hard task to do computationally, as it usually requires linguistic and common sense insights. In machine learning, humor detection is usually modeled as a binary classification task, trained to predict if the given text is a joke or another type of text. Rather than using completely... | ['Pieter Delobelle', 'Thomas Winters'] | 2020-10-26 | null | null | null | null | ['humor-detection'] | ['natural-language-processing'] | [-2.81460792e-01 2.55691074e-02 1.25373438e-01 1.63308382e-01
-1.62902772e-01 -4.98080224e-01 9.82660115e-01 1.13402493e-01
-1.69225633e-01 6.06076777e-01 6.53459370e-01 -4.58620250e-01
3.70681822e-01 -7.97184765e-01 -1.57621399e-01 -2.03054383e-01
5.97128451e-01 6.56708360e-01 3.88495997e-02 -7.94740260... | [8.893619537353516, 11.041979789733887] |
afe26f9c-80be-463f-834e-3060c028b3c4 | a-review-corpus-annotated-for-negation | null | null | https://aclanthology.org/L12-1298 | https://aclanthology.org/L12-1298.pdf | A review corpus annotated for negation, speculation and their scope | This paper presents a freely available resource for research on handling negation and speculation in review texts. The SFU Review Corpus, consisting of 400 documents of movie, book, and consumer product reviews, was annotated at the token level with negative and speculative keywords and at the sentence level with their... | ['Manuel J. Ma{\\~n}a', 'Sheila C.M. de Sousa', 'Noa P. Cruz', 'Ruslan Mitkov', 'Natalia Konstantinova', 'Maite Taboada'] | 2012-05-01 | null | null | null | lrec-2012-5 | ['negation-detection'] | ['natural-language-processing'] | [ 1.26491755e-01 2.79252559e-01 -1.04727447e+00 -3.52303267e-01
-4.82644886e-01 -8.22416842e-01 6.55873239e-01 9.92706776e-01
-5.77524781e-01 9.71478999e-01 5.72895110e-01 -6.01804554e-01
3.50682288e-01 -3.27786505e-01 -1.37805164e-01 -6.45645102e-03
1.14748172e-01 3.07230614e-02 2.05503866e-01 -6.77141130... | [11.187997817993164, 6.963620185852051] |
e67b0cbc-b877-4f57-81b7-9d3dea6f53da | a-case-study-of-spatiotemporal-forecasting | 2209.14782 | null | https://arxiv.org/abs/2209.14782v1 | https://arxiv.org/pdf/2209.14782v1.pdf | A case study of spatiotemporal forecasting techniques for weather forecasting | The majority of real-world processes are spatiotemporal, and the data generated by them exhibits both spatial and temporal evolution. Weather is one of the most important processes that fall under this domain, and forecasting it has become a crucial part of our daily routine. Weather data analysis is considered the mos... | ['Ivan Oseledets', 'Shakir Showkat Sofi'] | 2022-09-29 | null | null | null | null | ['weather-forecasting'] | ['miscellaneous'] | [-0.5666259 -0.874865 -0.11432971 -0.5123118 0.01733748 -0.54978603
0.8380323 -0.04060211 -0.22145076 0.6520767 0.3015797 -0.65540284
-0.1908774 -0.9734474 -0.20296036 -1.0616741 -0.51711506 0.01695363
0.1225603 -0.6733955 0.29536417 0.63367116 -1.6754043 0.22206546
1.0796702 1.3144702 0.... | [6.620709419250488, 2.875352144241333] |
e12b203f-02aa-41a6-90c3-e217dfd42500 | learning-embeddings-for-transitive-verb | null | null | https://aclanthology.org/W15-4001 | https://aclanthology.org/W15-4001.pdf | Learning Embeddings for Transitive Verb Disambiguation by Implicit Tensor Factorization | null | ['Kazuma Hashimoto', 'Yoshimasa Tsuruoka'] | 2015-07-01 | null | null | null | ws-2015-7 | ['learning-word-embeddings'] | ['methodology'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.359359264373779, 3.6510541439056396] |
1a59bddd-c2b1-4928-83af-3c7855c42b15 | semscribe-natural-language-generation-for | null | null | https://aclanthology.org/L12-1032 | https://aclanthology.org/L12-1032.pdf | SemScribe: Natural Language Generation for Medical Reports | Natural language generation in the medical domain is heavily influenced by domain knowledge and genre-specific text characteristics. We present SemScribe, an implemented natural language generation system that produces doctor's letters, in particular descriptions of cardiological findings. Texts in this domain are char... | ['Sebastian Varges', 'Lukas C. Faulstich', 'Heike Bieler', 'Manfred Stede', 'Malik Atalla', 'Kristin Irsig'] | 2012-05-01 | null | null | null | lrec-2012-5 | ['referring-expression-generation'] | ['computer-vision'] | [ 5.92215538e-01 9.92633343e-01 -9.08214226e-02 -5.70766389e-01
-6.67800903e-01 -4.53539103e-01 8.22202384e-01 7.95077920e-01
-1.34477049e-01 1.17455602e+00 1.05962455e+00 -4.41689909e-01
-5.02634645e-01 -8.67926002e-01 -7.04227760e-02 -1.66750401e-01
1.03535801e-01 1.12371504e+00 5.00174947e-02 -5.68222284... | [8.536921501159668, 8.7034273147583] |
11cbeb71-5f40-43ba-8d25-8c9be4c03f18 | long-short-term-memory-and-learning-to-learn | 1803.09574 | null | http://arxiv.org/abs/1803.09574v4 | http://arxiv.org/pdf/1803.09574v4.pdf | Long short-term memory and learning-to-learn in networks of spiking neurons | Recurrent networks of spiking neurons (RSNNs) underlie the astounding
computing and learning capabilities of the brain. But computing and learning
capabilities of RSNN models have remained poor, at least in comparison with
artificial neural networks (ANNs). We address two possible reasons for that.
One is that RSNNs in... | ['Anand Subramoney', 'Wolfgang Maass', 'Robert Legenstein', 'Guillaume Bellec', 'Darjan Salaj'] | 2018-03-26 | long-short-term-memory-and-learning-to-learn-1 | http://papers.nips.cc/paper/7359-long-short-term-memory-and-learning-to-learn-in-networks-of-spiking-neurons | http://papers.nips.cc/paper/7359-long-short-term-memory-and-learning-to-learn-in-networks-of-spiking-neurons.pdf | neurips-2018-12 | ['sequential-image-classification'] | ['computer-vision'] | [ 2.14266092e-01 2.27722034e-01 3.95434171e-01 8.98007751e-02
5.70970774e-01 -5.73797941e-01 7.90037692e-01 -1.76847264e-01
-8.25851977e-01 8.91319692e-01 -2.83886999e-01 -1.67767331e-01
-2.74730355e-01 -8.97020638e-01 -8.80236685e-01 -1.05655229e+00
-2.37645552e-01 3.79174113e-01 5.84459186e-01 -7.37330616... | [8.070276260375977, 2.9589343070983887] |
b6141b16-2799-4b77-9b66-3a1d0b1f3c47 | synthesis-of-adversarial-ddos-attacks-using | 2212.14109 | null | https://arxiv.org/abs/2212.14109v1 | https://arxiv.org/pdf/2212.14109v1.pdf | Synthesis of Adversarial DDOS Attacks Using Tabular Generative Adversarial Networks | Network Intrusion Detection Systems (NIDS) are tools or software that are widely used to maintain the computer networks and information systems keeping them secure and preventing malicious traffics from penetrating into them, as they flag when somebody is trying to break into the system. Best effort has been set up on ... | ['Sarah Hossam Elmowafy', 'Ahmed Shehata AboMoustafa', 'Mohamed Sayed Hussein', 'Abdelmageed Ahmed Hassan'] | 2022-12-14 | null | null | null | null | ['network-intrusion-detection'] | ['miscellaneous'] | [ 6.82711601e-02 1.66467875e-01 1.07089646e-01 -6.68578222e-02
3.63678962e-01 -1.01294851e+00 8.13994825e-01 -4.86559987e-01
3.45868208e-02 5.64305127e-01 -2.58542299e-01 -7.58514762e-01
1.39105558e-01 -1.20868146e+00 -3.03625226e-01 -4.31544691e-01
-2.60432780e-01 4.46814358e-01 5.94807863e-01 -5.60350537... | [5.472100734710693, 7.427201271057129] |
dafcac54-ca7f-4ce6-b4a9-c57a2cb0d400 | r-btn-cross-domain-face-composite-and | 1706.00556 | null | http://arxiv.org/abs/1706.00556v2 | http://arxiv.org/pdf/1706.00556v2.pdf | r-BTN: Cross-domain Face Composite and Synthesis from Limited Facial Patches | We start by asking an interesting yet challenging question, "If an eyewitness
can only recall the eye features of the suspect, such that the forensic artist
can only produce a sketch of the eyes (e.g., the top-left sketch shown in Fig.
1), can advanced computer vision techniques help generate the whole face
image?" A m... | ['Zhifei Zhang', 'Yang Song', 'Hairong Qi'] | 2017-06-02 | null | null | null | null | ['facial-inpainting'] | ['computer-vision'] | [ 3.23187470e-01 4.47636396e-01 1.73739702e-01 -2.02492833e-01
-7.72818685e-01 -6.73699796e-01 3.28327268e-01 -6.93049431e-01
1.12650348e-02 8.30354273e-01 -9.29813907e-02 -2.31354073e-01
2.51673222e-01 -7.91430652e-01 -9.01563406e-01 -6.25668287e-01
3.93093437e-01 3.74213785e-01 -1.90586850e-01 -2.46949866... | [12.490459442138672, -0.18763181567192078] |
d0e92623-c5e7-4a2e-b43a-d73afdee2a9e | learning-spatial-regularization-with-image | 1702.05891 | null | http://arxiv.org/abs/1702.05891v2 | http://arxiv.org/pdf/1702.05891v2.pdf | Learning Spatial Regularization with Image-level Supervisions for Multi-label Image Classification | Multi-label image classification is a fundamental but challenging task in
computer vision. Great progress has been achieved by exploiting semantic
relations between labels in recent years. However, conventional approaches are
unable to model the underlying spatial relations between labels in multi-label
images, because... | ['Hongsheng Li', 'Feng Zhu', 'Wanli Ouyang', 'Xiaogang Wang', 'Nenghai Yu'] | 2017-02-20 | learning-spatial-regularization-with-image-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Zhu_Learning_Spatial_Regularization_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Zhu_Learning_Spatial_Regularization_CVPR_2017_paper.pdf | cvpr-2017-7 | ['multi-label-image-classification'] | ['computer-vision'] | [ 4.34347838e-01 -5.24392873e-02 -2.65154213e-01 -7.38996804e-01
-7.98959136e-01 -5.48782647e-01 4.52922046e-01 2.32608527e-01
-4.97490942e-01 3.20296437e-01 -1.59638405e-01 -1.35354683e-01
-3.43559571e-02 -3.96367282e-01 -8.37515235e-01 -6.02427006e-01
3.70704919e-01 3.39442730e-01 3.98848474e-01 1.53896129... | [9.817400932312012, 3.99479079246521] |
e9e5be41-6481-450a-96a7-7d7d60754cdc | self-supervised-scanpath-prediction-framework | null | null | https://openaccess.thecvf.com/content/CVPR2022W/Ego4D-EPIC/html/Tliba_Self_Supervised_Scanpath_Prediction_Framework_for_Painting_Images_CVPRW_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022W/Ego4D-EPIC/papers/Tliba_Self_Supervised_Scanpath_Prediction_Framework_for_Painting_Images_CVPRW_2022_paper.pdf | Self Supervised Scanpath Prediction Framework for Painting Images | In our paper, we propose a novel strategy to learn distortion invariant latent representation from painting pictures for visual attention modelling downstream task. In further detail, we design an unsupervised framework that jointly maximises the mutual information over different painting styles. To show the effectiven... | ['Alessandro Bruno', 'Aladine Chetouani', 'Mohamed Amine Kerkouri', 'Marouane Tliba'] | 2022-06-19 | null | null | null | cvpr-2022-6 | ['scanpath-prediction'] | ['computer-vision'] | [ 6.81182384e-01 3.41296911e-01 -5.84815405e-02 -5.44638038e-01
-9.62334812e-01 -5.43332696e-01 7.41801023e-01 -5.40011644e-01
-2.36953586e-01 3.75349909e-01 4.79826570e-01 1.37709975e-01
-1.64040789e-01 -5.21341681e-01 -7.77363718e-01 -5.80399275e-01
2.51922965e-01 4.19412941e-01 1.50650144e-01 1.80622369... | [11.420309066772461, -0.2376602739095688] |
e5a0beda-592c-4123-8f00-9038e48293d0 | fast-video-moment-retrieval | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Gao_Fast_Video_Moment_Retrieval_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Gao_Fast_Video_Moment_Retrieval_ICCV_2021_paper.pdf | Fast Video Moment Retrieval | This paper targets at fast video moment retrieval (fast VMR), aiming to localize the target moment efficiently and accurately as queried by a given natural language sentence. We argue that most existing VMR approaches can be divided into three modules namely video encoder, text encoder, and cross-modal interaction ... | ['Changsheng Xu', 'Junyu Gao'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['moment-retrieval'] | ['computer-vision'] | [ 2.68420398e-01 -2.90026814e-01 -5.98135591e-01 -4.34042752e-01
-1.23512745e+00 -4.64431316e-01 6.88627899e-01 -9.12861750e-02
-5.35984874e-01 2.25794539e-01 3.36057484e-01 -3.55230793e-02
-1.88194647e-01 -5.18307984e-01 -7.73278117e-01 -7.02645659e-01
1.73403546e-01 3.49415749e-01 2.62294918e-01 -8.00596084... | [10.31436824798584, 0.905454695224762] |
230fe43e-56e1-45ef-a237-d1fc6da7bf6b | neural-models-for-predicting-celtic-mutations | null | null | https://aclanthology.org/2020.sltu-1.1 | https://aclanthology.org/2020.sltu-1.1.pdf | Neural Models for Predicting Celtic Mutations | The Celtic languages share a common linguistic phenomenon known as initial mutations; these consist of pronunciation and spelling changes that occur at the beginning of some words, triggered in certain semantic or syntactic contexts. Initial mutations occur quite frequently and all non-trivial NLP systems for the Celti... | ['Kevin Scannell'] | 2020-05-01 | null | null | null | lrec-2020-5 | ['grammatical-error-detection'] | ['natural-language-processing'] | [ 1.52401049e-02 -8.63444209e-02 1.70853913e-01 -7.04594433e-01
-1.71834961e-01 -4.80894655e-01 2.52540976e-01 3.51901144e-01
-6.20130718e-01 9.99983788e-01 6.97310045e-02 -5.20357847e-01
1.06174618e-01 -4.69707608e-01 -7.52748609e-01 -1.64114803e-01
-1.44867778e-01 6.46506727e-01 -4.09485072e-01 -5.19216895... | [10.856019020080566, 10.118809700012207] |
135fceee-c81f-4938-a311-c408387245d1 | av-taris-online-audio-visual-speech | 2012.07467 | null | https://arxiv.org/abs/2012.07467v1 | https://arxiv.org/pdf/2012.07467v1.pdf | AV Taris: Online Audio-Visual Speech Recognition | In recent years, Automatic Speech Recognition (ASR) technology has approached human-level performance on conversational speech under relatively clean listening conditions. In more demanding situations involving distant microphones, overlapped speech, background noise, or natural dialogue structures, the ASR error rate ... | ['Naomi Harte', 'George Sterpu'] | 2020-12-14 | null | null | null | null | ['audio-visual-speech-recognition'] | ['speech'] | [ 2.54959524e-01 2.53771693e-01 2.86653906e-01 -1.38706982e-01
-1.40344024e+00 -6.42457128e-01 7.30627298e-01 -1.55625328e-01
-3.07401389e-01 2.52995551e-01 4.08967733e-01 -6.93113625e-01
2.28251979e-01 -5.32292575e-02 -4.72693950e-01 -5.84861755e-01
3.38230193e-01 2.18601659e-01 -2.45734826e-02 -1.56191707... | [14.380049705505371, 5.261716365814209] |
1da7fdc7-0a5f-463f-a020-a3752c66581e | accelerated-iterative-tomographic | 2202.08627 | null | https://arxiv.org/abs/2202.08627v1 | https://arxiv.org/pdf/2202.08627v1.pdf | Accelerated iterative tomographic reconstruction with x-ray edge illumination | Compared to standard tomographic reconstruction, iterative approaches offer the possibility to account for extraneous experimental influences, which allows for a suppression of related artifacts. However, the inclusion of corresponding parameters in the iterative forward model typically leads to longer computation time... | ['Marco Endrizzi', 'Alessandro Olivo', 'Lorenzo Massimi', 'Jeff Meganck', 'Tomasz Korzec', 'Peter Modregger'] | 2022-02-17 | null | null | null | null | ['tomographic-reconstructions'] | ['medical'] | [ 6.98517144e-01 -2.65454799e-01 5.87652206e-01 -1.60104290e-01
-8.32788408e-01 -5.71105145e-02 4.19880420e-01 1.36473060e-01
-5.63678741e-01 9.63101387e-01 -9.67271253e-02 -9.82210040e-02
-3.78523916e-01 -5.92458189e-01 -5.69204986e-01 -9.82544243e-01
5.67576662e-02 4.48637336e-01 4.06146467e-01 2.21411139... | [12.85299015045166, -2.751859188079834] |
ba26f0b9-1dfd-405d-92a7-8eeda8a0e2ed | analytic-automated-essay-scoring-based-on | null | null | https://aclanthology.org/2022.coling-1.257 | https://aclanthology.org/2022.coling-1.257.pdf | Analytic Automated Essay Scoring Based on Deep Neural Networks Integrating Multidimensional Item Response Theory | Essay exams have been attracting attention as a way of measuring the higher-order abilities of examinees, but they have two major drawbacks in that grading them is expensive and raises questions about fairness. As an approach to overcome these problems, automated essay scoring (AES) is in increasing need. Many AES mode... | ['Masaki Uto', 'Takumi Shibata'] | null | null | null | null | coling-2022-10 | ['automated-essay-scoring'] | ['natural-language-processing'] | [-5.02358735e-01 -2.99452931e-01 -2.46725485e-01 -7.47176468e-01
-4.40865099e-01 -3.48177850e-01 1.03881070e-02 3.17191750e-01
-4.74613637e-01 6.78963840e-01 8.17913339e-02 -1.81176811e-01
-3.49700511e-01 -8.13772500e-01 5.28880134e-02 -1.27694041e-01
5.98226786e-01 2.43182838e-01 1.31969631e-01 -2.87552625... | [11.356987953186035, 9.32766056060791] |
63a03a34-8373-43ef-b14f-637456f3c167 | temporal-and-heterogeneous-graph-neural | 2305.08740 | null | https://arxiv.org/abs/2305.08740v1 | https://arxiv.org/pdf/2305.08740v1.pdf | Temporal and Heterogeneous Graph Neural Network for Financial Time Series Prediction | The price movement prediction of stock market has been a classical yet challenging problem, with the attention of both economists and computer scientists. In recent years, graph neural network has significantly improved the prediction performance by employing deep learning on company relations. However, existing relati... | ['Yuqi Liang', 'Ying Zhang', 'Chencheng Shang', 'Dawei Cheng', 'Sheng Xiang'] | 2023-05-09 | null | null | null | null | ['time-series-prediction'] | ['time-series'] | [-4.13266093e-01 -2.87032396e-01 -1.83213085e-01 -1.38289675e-01
-2.19527945e-01 -4.68006045e-01 6.27809525e-01 -1.23546518e-01
-2.25515604e-01 3.99731338e-01 2.17858717e-01 -4.71518099e-01
-2.40390018e-01 -1.29836500e+00 -5.20152092e-01 -3.44925135e-01
-1.43673703e-01 4.73150760e-01 1.87784269e-01 -3.50070566... | [4.342118740081787, 4.308958053588867] |
fb48a16a-b231-41b3-8d95-20c6cc2667c5 | decomposition-enhances-reasoning-via-self | 2305.00633 | null | https://arxiv.org/abs/2305.00633v2 | https://arxiv.org/pdf/2305.00633v2.pdf | Decomposition Enhances Reasoning via Self-Evaluation Guided Decoding | We endow Large Language Models (LLMs) with fine-grained self-evaluation to refine multi-step reasoning inference. We propose an effective prompting approach that integrates self-evaluation guidance through stochastic beam search. Our approach explores the reasoning search space using a well-calibrated automatic criteri... | ['Qizhe Xie', 'Junxian He', 'Min-Yen Kan', 'Xu Zhao', 'Yiran Zhao', 'Kenji Kawaguchi', 'Yuxi Xie'] | 2023-05-01 | null | null | null | null | ['math-word-problem-solving', 'gsm8k', 'arithmetic-reasoning', 'math-word-problem-solving', 'strategyqa', 'math-word-problem-solving'] | ['knowledge-base', 'natural-language-processing', 'reasoning', 'reasoning', 'reasoning', 'time-series'] | [-1.42410100e-01 3.11829031e-01 -4.93293285e-01 -5.23507953e-01
-1.48621917e+00 -6.70771599e-01 4.85674649e-01 1.29278973e-01
-2.62732416e-01 8.08822870e-01 2.66205460e-01 -7.07074225e-01
-4.99812961e-02 -8.75063419e-01 -8.24930727e-01 -2.80162603e-01
3.75204206e-01 6.32381320e-01 2.96863556e-01 -5.08789480... | [9.730432510375977, 7.446317195892334] |
b3f6545a-284b-41d4-99ee-2c9a7f1b9970 | sato-contextual-semantic-type-detection-in | 1911.06311 | null | https://arxiv.org/abs/1911.06311v3 | https://arxiv.org/pdf/1911.06311v3.pdf | Sato: Contextual Semantic Type Detection in Tables | Detecting the semantic types of data columns in relational tables is important for various data preparation and information retrieval tasks such as data cleaning, schema matching, data discovery, and semantic search. However, existing detection approaches either perform poorly with dirty data, support only a limited nu... | ['Wang-Chiew Tan', 'Çağatay Demiralp', 'Jinfeng Li', 'Dan Zhang', 'Yoshihiko Suhara', 'Madelon Hulsebos'] | 2019-11-14 | null | null | null | null | ['column-type-annotation'] | ['natural-language-processing'] | [ 1.27644837e-01 3.21969301e-01 -5.84038973e-01 -5.10872543e-01
-1.02988064e+00 -5.51569939e-01 5.55972040e-01 1.05487096e+00
-2.06678480e-01 4.86159474e-01 2.19269544e-01 -2.09197417e-01
-1.54731169e-01 -1.03465760e+00 -1.10155940e+00 -1.77688614e-01
-8.26097429e-02 8.46287966e-01 3.83034796e-01 -5.83110303... | [9.577529907226562, 7.884323596954346] |
794d86cd-03cd-4acd-9259-633a7fdbc2df | oriented-object-detection-in-aerial-images | 2008.07043 | null | https://arxiv.org/abs/2008.07043v2 | https://arxiv.org/pdf/2008.07043v2.pdf | Oriented Object Detection in Aerial Images with Box Boundary-Aware Vectors | Oriented object detection in aerial images is a challenging task as the objects in aerial images are displayed in arbitrary directions and are usually densely packed. Current oriented object detection methods mainly rely on two-stage anchor-based detectors. However, the anchor-based detectors typically suffer from a se... | ['Dimitris Metaxas', 'Hui Qu', 'Jingru Yi', 'Bo Liu', 'Qiaoying Huang', 'Pengxiang Wu'] | 2020-08-17 | null | null | null | null | ['object-detection-in-aerial-images'] | ['computer-vision'] | [-1.34755015e-01 -2.91474253e-01 -2.15186253e-01 -1.97781935e-01
-3.92920107e-01 -7.47747898e-01 1.87524945e-01 1.17851786e-01
-3.17903489e-01 1.49962202e-01 -5.90199381e-02 -1.37092367e-01
-2.45807115e-02 -6.59861863e-01 -5.75281978e-01 -8.12140584e-01
-2.89281845e-01 1.22288980e-01 7.12786674e-01 -2.63117939... | [8.683612823486328, -0.7659121751785278] |
85e77d46-32a7-4532-a529-c4d91c57221c | s3e-a-large-scale-multimodal-dataset-for | 2210.13723 | null | https://arxiv.org/abs/2210.13723v3 | https://arxiv.org/pdf/2210.13723v3.pdf | S3E: A Large-scale Multimodal Dataset for Collaborative SLAM | With the advanced request to employ a team of robots to perform a task collaboratively, the research community has become increasingly interested in collaborative simultaneous localization and mapping. Unfortunately, existing datasets are limited in the scale and variation of the collaborative trajectories, even though... | ['Hongbo Chen', 'Tao Jiang', 'Qiming Chen', 'Yudu Jiao', 'Zhiqiang Chen', 'Shipeng Zhong', 'Yuhua Qi', 'Dapeng Feng'] | 2022-10-25 | null | null | null | null | ['simultaneous-localization-and-mapping'] | ['computer-vision'] | [-2.71504313e-01 -3.91758978e-01 1.52804092e-01 -5.25495291e-01
-9.21365678e-01 -1.16248107e+00 8.60006690e-01 1.02688260e-01
-6.54713690e-01 8.71574104e-01 9.11384374e-02 -2.04529434e-01
-4.91207153e-01 -3.46102327e-01 -5.93129098e-01 -4.18100238e-01
-4.95429248e-01 8.67191970e-01 2.68421680e-01 -3.92626107... | [7.251621723175049, -2.135977268218994] |
542df122-f0de-4103-8069-b7bcb3408198 | dive-into-machine-learning-algorithms-for | 2207.13842 | null | https://arxiv.org/abs/2207.13842v1 | https://arxiv.org/pdf/2207.13842v1.pdf | Dive into Machine Learning Algorithms for Influenza Virus Host Prediction with Hemagglutinin Sequences | Influenza viruses mutate rapidly and can pose a threat to public health, especially to those in vulnerable groups. Throughout history, influenza A viruses have caused pandemics between different species. It is important to identify the origin of a virus in order to prevent the spread of an outbreak. Recently, there has... | ['Dominik Wojtczak', 'Yanhua Xu'] | 2022-07-28 | null | null | null | null | ['machine-learning', 'machine-learning'] | ['methodology', 'miscellaneous'] | [ 2.31282637e-01 -4.54582840e-01 -2.13083863e-01 -5.17417975e-02
-1.30850181e-03 -6.05184019e-01 3.51532191e-01 6.21293783e-01
-5.52550018e-01 7.69129992e-01 1.37067894e-02 -3.51438582e-01
6.05217516e-02 -8.31927061e-01 -2.35975757e-01 -8.41453075e-01
-3.37545782e-01 3.68172765e-01 1.37344962e-02 -2.19425738... | [5.11126184463501, 5.217215061187744] |
c085afc8-ecf4-4b71-959d-0eff199e9ba4 | acfnet-adaptively-cooperative-fusion-network | 2109.04627 | null | https://arxiv.org/abs/2109.04627v1 | https://arxiv.org/pdf/2109.04627v1.pdf | ACFNet: Adaptively-Cooperative Fusion Network for RGB-D Salient Object Detection | The reasonable employment of RGB and depth data show great significance in promoting the development of computer vision tasks and robot-environment interaction. However, there are different advantages and disadvantages in the early and late fusion of the two types of data. Besides, due to the diversity of object inform... | ['Jinchao Zhu'] | 2021-09-10 | null | null | null | null | ['rgb-d-salient-object-detection'] | ['computer-vision'] | [ 1.44887730e-01 -1.39095113e-01 1.90258846e-01 -6.14529848e-01
-1.19509228e-01 1.29653499e-01 4.47429091e-01 1.30730391e-01
-6.76495016e-01 3.37569565e-01 -5.89878932e-02 1.70373738e-01
-2.74386257e-01 -8.63700390e-01 -3.34589124e-01 -9.14771616e-01
1.62466809e-01 6.70899637e-03 6.12517715e-01 -4.90922332... | [9.579983711242676, -0.7005239129066467] |
aa896e8b-9752-484b-996f-d4daf1668100 | lottery-tickets-in-evolutionary-optimization | 2306.00045 | null | https://arxiv.org/abs/2306.00045v1 | https://arxiv.org/pdf/2306.00045v1.pdf | Lottery Tickets in Evolutionary Optimization: On Sparse Backpropagation-Free Trainability | Is the lottery ticket phenomenon an idiosyncrasy of gradient-based training or does it generalize to evolutionary optimization? In this paper we establish the existence of highly sparse trainable initializations for evolution strategies (ES) and characterize qualitative differences compared to gradient descent (GD)-bas... | ['Henning Sprekeler', 'Robert Tjarko Lange'] | 2023-05-31 | null | null | null | null | ['linear-mode-connectivity'] | ['knowledge-base'] | [ 3.87217589e-02 2.80080941e-02 -6.22942485e-02 -1.28737524e-01
-1.76057324e-01 -5.67641199e-01 5.09373665e-01 7.41979405e-02
-5.12357593e-01 8.95951271e-01 1.21660724e-01 -1.54737011e-01
-7.42652595e-01 -6.24386907e-01 -8.17146778e-01 -9.06250417e-01
-4.17602956e-01 4.03920770e-01 1.80477589e-01 -6.19730175... | [8.074003219604492, 3.4652483463287354] |
ff95d43d-e040-4903-937c-ee9678a8c8a1 | countering-malicious-content-moderation | 2212.14727 | null | https://arxiv.org/abs/2212.14727v1 | https://arxiv.org/pdf/2212.14727v1.pdf | Countering Malicious Content Moderation Evasion in Online Social Networks: Simulation and Detection of Word Camouflage | Content moderation is the process of screening and monitoring user-generated content online. It plays a crucial role in stopping content resulting from unacceptable behaviors such as hate speech, harassment, violence against specific groups, terrorism, racism, xenophobia, homophobia, or misogyny, to mention some few, i... | ['David Camacho', 'Javier Huertas Tato', 'Alejandro Martín', 'Álvaro Huertas-García'] | 2022-12-27 | null | null | null | null | ['multilingual-named-entity-recognition'] | ['natural-language-processing'] | [-7.40657076e-02 1.96442887e-01 -1.20511211e-01 5.23435533e-01
-3.21228683e-01 -1.10870802e+00 1.18365884e+00 5.67578852e-01
-4.70645219e-01 6.00681245e-01 4.16371912e-01 -5.03773510e-01
-4.43598442e-02 -7.51439273e-01 -3.45116705e-01 -2.92153805e-01
1.64625332e-01 3.59213322e-01 2.94752568e-01 -6.78596795... | [8.68129825592041, 10.523524284362793] |
2a8720a0-fcf8-457f-bb5d-4c8c77088017 | devil-in-the-details-towards-accurate-single | 1809.05996 | null | http://arxiv.org/abs/1809.05996v3 | http://arxiv.org/pdf/1809.05996v3.pdf | Devil in the Details: Towards Accurate Single and Multiple Human Parsing | Human parsing has received considerable interest due to its wide application
potentials. Nevertheless, it is still unclear how to develop an accurate human
parsing system in an efficient and elegant way. In this paper, we identify
several useful properties, including feature resolution, global context
information and e... | ['Shikui Wei', 'Ting Liu', 'Yunchao Wei', 'Zilong Huang', 'Yao Zhao', 'Thomas Huang', 'Tao Ruan'] | 2018-09-17 | null | null | null | null | ['human-parsing'] | ['computer-vision'] | [ 1.35078371e-01 4.62712020e-01 3.93449776e-02 -4.48060304e-01
-1.20400608e+00 -5.89538276e-01 2.26450890e-01 6.92770183e-02
-4.47219342e-01 6.09650135e-01 1.12427577e-01 -3.93415153e-01
2.93448597e-01 -7.98603833e-01 -6.90487981e-01 -5.00923991e-01
-1.50028411e-02 -7.88938329e-02 2.11182401e-01 -8.43343288... | [8.72092342376709, 0.0014772055437788367] |
7e8a05d3-a2c0-45ba-b81f-c5d9d4340e3e | on-the-evaluations-of-chatgpt-and-emotion | 2304.03347 | null | https://arxiv.org/abs/2304.03347v2 | https://arxiv.org/pdf/2304.03347v2.pdf | Towards Interpretable Mental Health Analysis with ChatGPT | Automated mental health analysis shows great potential for enhancing the efficiency and accessibility of mental health care, with recent methods using pre-trained language models (PLMs) and incorporated emotional information. The latest large language models (LLMs), such as ChatGPT, exhibit dramatic capabilities on div... | ['Ziyan Kuang', 'Sophia Ananiadou', 'Qianqian Xie', 'Tianlin Zhang', 'Shaoxiong Ji', 'Kailai Yang'] | 2023-04-06 | null | null | null | null | ['causal-emotion-entailment'] | ['natural-language-processing'] | [ 7.89926760e-03 9.62398767e-01 -3.75045478e-01 -7.36934721e-01
-5.99831343e-01 1.31331339e-01 3.57460603e-02 4.71150398e-01
-1.10735305e-01 6.96751237e-01 6.05277240e-01 -3.51862788e-01
-2.76376218e-01 -3.50851655e-01 7.73855224e-02 -2.64063567e-01
-6.40465543e-02 7.30581999e-01 -6.50746226e-01 -2.78376222... | [12.66063117980957, 6.838527202606201] |
3388bb0d-1dd3-48da-9ecd-776c88b16520 | uni-mol-a-universal-3d-molecular | null | null | https://chemrxiv.org/engage/chemrxiv/article-details/628e5b4d5d948517f5ce6d72 | https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/628e5b4d5d948517f5ce6d72/original/uni-mol-a-universal-3d-molecular-representation-learning-framework.pdf | Uni-Mol: A Universal 3D Molecular Representation Learning Framework | Molecular representation learning (MRL) has gained tremendous attention due to its critical role in learning from limited supervised data for applications like drug design. In most MRL methods, molecules are treated as 1D sequential tokens or 2D topology graphs, limiting their ability to incorporate 3D information for ... | ['Guolin Ke', 'Linfeng Zhang', 'Zhewei Wei', 'Hongteng Xu', 'Hang Zheng', 'Qiankun Ding', 'Zhifeng Gao', 'Gengmo Zhou'] | 2022-09-08 | null | null | null | chemrxiv-2022-9 | ['molecular-property-prediction'] | ['miscellaneous'] | [ 3.44162047e-01 -4.78531495e-02 -6.82325125e-01 -1.62419677e-01
-1.03914154e+00 -5.12911141e-01 3.42368454e-01 3.44371587e-01
-9.55514237e-02 1.03640580e+00 3.69095244e-02 -8.35134029e-01
4.16709706e-02 -6.89066708e-01 -1.11554384e+00 -9.69641089e-01
-2.04351619e-01 6.12852037e-01 1.37475684e-01 -1.53507411... | [5.064981937408447, 5.835604190826416] |
ace4e89b-995e-4e99-a2a2-425668960549 | demystifying-the-transferability-of | 2110.04488 | null | https://arxiv.org/abs/2110.04488v3 | https://arxiv.org/pdf/2110.04488v3.pdf | Demystifying the Transferability of Adversarial Attacks in Computer Networks | Convolutional Neural Networks (CNNs) models are one of the most frequently used deep learning networks, and extensively used in both academia and industry. Recent studies demonstrated that adversarial attacks against such models can maintain their effectiveness even when used on models other than the one targeted by th... | ['Mauro Conti', 'Yassine Mekdad', 'Abdeslam El Fergougui', 'Mohammad Hajian Berenjestanaki', 'Ehsan Nowroozi'] | 2021-10-09 | null | null | null | null | ['breast-cancer-detection', 'breast-cancer-detection'] | ['knowledge-base', 'medical'] | [ 1.18390247e-01 1.43605620e-01 -8.10507387e-02 2.75755972e-02
-2.85688907e-01 -7.66455054e-01 8.18336070e-01 -3.07340562e-01
-5.42707741e-01 7.54839718e-01 -3.38552803e-01 -7.37779081e-01
-8.62831697e-02 -9.07920420e-01 -1.01983535e+00 -8.49265397e-01
-3.46513212e-01 -5.58677576e-02 6.99586987e-01 -3.83239090... | [5.519710063934326, 7.859066486358643] |
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