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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
39febac2-044c-4be5-a253-74325285acab | self-supervised-tumor-segmentation-through | 2109.03230 | null | https://arxiv.org/abs/2109.03230v2 | https://arxiv.org/pdf/2109.03230v2.pdf | Self-supervised Tumor Segmentation through Layer Decomposition | In this paper, we target self-supervised representation learning for zero-shot tumor segmentation. We make the following contributions: First, we advocate a zero-shot setting, where models from pre-training should be directly applicable for the downstream task, without using any manual annotations. Second, we take insp... | ['Qi Tian', 'Xin Chen', 'Yanfeng Wang', 'Ya zhang', 'Chaoqin Huang', 'Weidi Xie', 'Xiaoman Zhang'] | 2021-09-07 | null | null | null | null | ['zero-shot-segmentation'] | ['computer-vision'] | [ 5.73000073e-01 7.61551857e-01 -3.29791605e-01 -4.52456586e-02
-1.18449855e+00 -1.87384695e-01 6.62064254e-01 1.36724278e-01
-3.31068575e-01 5.27404785e-01 1.32635236e-01 -4.79444742e-01
-2.84617543e-02 -5.66972911e-01 -5.03936291e-01 -8.81536424e-01
6.55527040e-02 4.87069547e-01 4.35139239e-01 -1.47969320... | [14.702743530273438, -2.2107694149017334] |
670a1bb8-7d8a-4483-b665-522cf0716d71 | biophysical-model-for-signal-embedded-droplet | 2305.06438 | null | https://arxiv.org/abs/2305.06438v1 | https://arxiv.org/pdf/2305.06438v1.pdf | Biophysical Model for Signal-Embedded Droplet Soaking into 2D Cell Culture | Using agar plates hosting a 2D cell population stimulated with signaling molecules is crucial for experiments such as gene regulation and drug discovery in a wide range of biological studies. In this paper, a biophysical model is proposed that incorporates droplet soaking, diffusion of molecules within agar, cell growt... | ['Adam Noel', 'Christophe Corre', 'Hamidreza Arjmandi', 'Ibrahim Isik'] | 2023-05-10 | null | null | null | null | ['drug-discovery', 'culture'] | ['medical', 'speech'] | [ 3.51693362e-01 -3.86230528e-01 1.61550894e-01 4.61751491e-01
-5.11085242e-02 -6.72326565e-01 1.66103721e-01 7.23112464e-01
-5.07874250e-01 8.34391534e-01 -4.06488508e-01 -1.46007121e-01
2.92224556e-01 -9.73516822e-01 -9.69903827e-01 -1.24960518e+00
-8.00178796e-02 3.39260876e-01 5.33015132e-01 -1.30953975... | [13.710511207580566, -3.088231086730957] |
b127ee02-efe2-4669-87e1-f8f806c98193 | cstr-a-classification-perspective-on-scene | 2102.10884 | null | https://arxiv.org/abs/2102.10884v3 | https://arxiv.org/pdf/2102.10884v3.pdf | Revisiting Classification Perspective on Scene Text Recognition | The prevalent perspectives of scene text recognition are from sequence to sequence (seq2seq) and segmentation. Nevertheless, the former is composed of many components which makes implementation and deployment complicated, while the latter requires character level annotations that is expensive. In this paper, we revisit... | ['Yichao Xiong', 'Jun Sun', 'Hongxiang Cai'] | 2021-02-22 | null | null | null | null | ['scene-text-recognition'] | ['computer-vision'] | [ 5.78220308e-01 -4.82702792e-01 -1.29910916e-01 -4.46915269e-01
-3.62255186e-01 -5.34108639e-01 6.98202610e-01 3.52730677e-02
-5.93482018e-01 3.60239446e-02 2.37809658e-01 -4.97468174e-01
3.85972053e-01 -6.82803750e-01 -6.41733766e-01 -7.13780046e-01
7.97558069e-01 1.19556867e-01 4.57981110e-01 -9.91365090... | [11.886061668395996, 2.196058750152588] |
3d1613a6-8207-4a83-95d6-e11027c0c41c | learning-policies-from-self-play-with-policy | 1905.05809 | null | https://arxiv.org/abs/1905.05809v1 | https://arxiv.org/pdf/1905.05809v1.pdf | Learning Policies from Self-Play with Policy Gradients and MCTS Value Estimates | In recent years, state-of-the-art game-playing agents often involve policies that are trained in self-playing processes where Monte Carlo tree search (MCTS) algorithms and trained policies iteratively improve each other. The strongest results have been obtained when policies are trained to mimic the search behaviour of... | ['Éric Piette', 'Cameron Browne', 'Dennis J. N. J. Soemers', 'Matthew Stephenson'] | 2019-05-14 | null | null | null | null | ['board-games'] | ['playing-games'] | [ 1.61122456e-01 3.24273646e-01 -2.94991076e-01 -1.62004694e-01
-6.72322810e-01 -6.39504910e-01 8.67225289e-01 4.68238862e-03
-7.31098592e-01 1.08968687e+00 2.37035722e-01 -7.84040987e-01
-2.88358301e-01 -8.69501770e-01 -6.21095181e-01 -4.88580644e-01
-2.19856873e-01 6.44379020e-01 4.17735726e-01 -2.01971069... | [3.8125972747802734, 1.6824069023132324] |
de8af402-373e-40b8-9a53-4c12ce6ec323 | omnisafe-an-infrastructure-for-accelerating | 2305.09304 | null | https://arxiv.org/abs/2305.09304v1 | https://arxiv.org/pdf/2305.09304v1.pdf | OmniSafe: An Infrastructure for Accelerating Safe Reinforcement Learning Research | AI systems empowered by reinforcement learning (RL) algorithms harbor the immense potential to catalyze societal advancement, yet their deployment is often impeded by significant safety concerns. Particularly in safety-critical applications, researchers have raised concerns about unintended harms or unsafe behaviors of... | ['Yaodong Yang', 'Mickel Liu', 'Yiran Geng', 'Weidong Huang', 'Ruiyang Sun', 'Xuehai Pan', 'Juntao Dai', 'Borong Zhang', 'Jiayi Zhou', 'Jiaming Ji'] | 2023-05-16 | null | null | null | null | ['philosophy'] | ['miscellaneous'] | [ 7.85631314e-02 2.41339207e-01 -6.26783490e-01 -9.64999124e-02
-5.48699677e-01 -7.09195554e-01 7.54885972e-01 1.99815184e-01
-5.49045444e-01 1.02355337e+00 3.72773707e-01 -4.67107356e-01
-2.86920011e-01 -7.99507797e-01 -5.93326747e-01 -5.34444690e-01
4.45446745e-02 1.68479845e-01 -3.86014462e-01 -3.84348899... | [4.428651809692383, 2.06496524810791] |
9e9aa6ad-3b20-47ff-a76e-34aea35fa30a | erfnet-efficient-residual-factorized-convnet | null | null | https://ieeexplore.ieee.org/abstract/document/8063438 | http://www.robesafe.uah.es/personal/eduardo.romera/pdfs/Romera17tits.pdf | ERFNet: Efficient Residual Factorized ConvNet for Real-time Semantic Segmentation | Semantic segmentation is a challenging task that addresses most of the perception needs of Intelligent Vehicles (IV) in an unified way. Deep Neural Networks excel at this task, as they can be trained end-to-end to accurately classify multiple object categories in an image at pixel level. However, a good trade-off betwe... | ['L. M. Bergasa and R. Arroyo', 'E. Romera', 'J. M. Alvarez'] | 2017-10-09 | null | null | null | transactions-on-intelligent-transportation | ['thermal-image-segmentation'] | ['computer-vision'] | [-1.19110420e-02 4.50352468e-02 4.36835177e-02 -5.65039575e-01
-6.38035297e-01 -5.46740890e-01 4.17095572e-01 -1.59920692e-01
-7.32951105e-01 2.35972419e-01 -7.57989883e-01 -6.59115851e-01
2.93699205e-01 -9.22886372e-01 -1.05145144e+00 -4.55023557e-01
2.45575994e-01 7.11249888e-01 7.75279343e-01 -2.38414958... | [8.95814037322998, -0.7699064612388611] |
f83a353e-4b2c-4d7d-9f50-72cf0bae3f52 | leafai-query-generator-for-clinical-cohort | 2304.06203 | null | https://arxiv.org/abs/2304.06203v1 | https://arxiv.org/pdf/2304.06203v1.pdf | LeafAI: query generator for clinical cohort discovery rivaling a human programmer | Objective: Identifying study-eligible patients within clinical databases is a critical step in clinical research. However, accurate query design typically requires extensive technical and biomedical expertise. We sought to create a system capable of generating data model-agnostic queries while also providing novel logi... | ['Meliha Yetisgen', 'Ozlem Uzuner', 'Robert Harrington', 'H. Nina Kim', 'Kristine Lan', 'Weipeng Zhou', 'Bin Han', 'Nicholas J Dobbins'] | 2023-04-13 | null | null | null | null | ['logical-reasoning'] | ['reasoning'] | [ 1.04524232e-01 3.54449838e-01 -5.90207875e-01 -5.55552185e-01
-1.39519060e+00 -6.84751451e-01 -2.02241093e-02 9.35843170e-01
-6.07174754e-01 8.35904598e-01 1.30856156e-01 -8.12920272e-01
-3.48070323e-01 -1.07584155e+00 -5.43317020e-01 1.19601451e-01
9.46578104e-03 1.36997724e+00 -7.30448961e-02 3.47971916... | [8.478904724121094, 8.603950500488281] |
52cd00c7-0c41-4002-a1d7-a4f1ea0d093c | semantic-feature-integration-network-for-fine | 2302.10275 | null | https://arxiv.org/abs/2302.10275v1 | https://arxiv.org/pdf/2302.10275v1.pdf | Semantic Feature Integration network for Fine-grained Visual Classification | Fine-Grained Visual Classification (FGVC) is known as a challenging task due to subtle differences among subordinate categories. Many current FGVC approaches focus on identifying and locating discriminative regions by using the attention mechanism, but neglect the presence of unnecessary features that hinder the unders... | ['Haichi Luo', 'Yueyang Li', 'Hui Wang'] | 2023-02-13 | null | null | null | null | ['fine-grained-image-classification'] | ['computer-vision'] | [ 8.04219395e-02 -3.23402643e-01 -1.81492001e-01 -6.27436101e-01
-5.11841118e-01 -2.31411234e-01 4.11879897e-01 -4.00537811e-02
-3.34634393e-01 5.58805287e-01 1.72682211e-01 2.00915739e-01
-8.04295167e-02 -6.78256989e-01 -6.07629418e-01 -8.26103330e-01
1.43935354e-02 -1.28379256e-01 5.52335441e-01 1.00724101... | [9.631943702697754, 2.0049691200256348] |
a80ebaa9-34ca-47df-a8ff-7f3543802c0c | a-reproducible-and-realistic-evaluation-of | 2210.01210 | null | https://arxiv.org/abs/2210.01210v1 | https://arxiv.org/pdf/2210.01210v1.pdf | A Reproducible and Realistic Evaluation of Partial Domain Adaptation Methods | Unsupervised Domain Adaptation (UDA) aims at classifying unlabeled target images leveraging source labeled ones. In this work, we consider the Partial Domain Adaptation (PDA) variant, where we have extra source classes not present in the target domain. Most successful algorithms use model selection strategies that rely... | ['Adam Oberman', 'Ioannis Mitliagkas', 'Kilian Fatras', 'Tiago Salvador'] | 2022-10-03 | null | null | null | null | ['partial-domain-adaptation'] | ['methodology'] | [ 3.90439332e-01 -1.37470454e-01 -6.45549119e-01 -4.18832392e-01
-8.44975591e-01 -8.92374098e-01 7.18722403e-01 3.85867544e-02
-5.24593234e-01 9.90256011e-01 -2.37239793e-01 -1.33717522e-01
-1.99594706e-01 -4.93856996e-01 -4.94179577e-01 -8.84093046e-01
2.55966663e-01 8.43026042e-01 5.13281345e-01 3.52448560... | [10.21611499786377, 3.146399974822998] |
d033c7a3-2a82-4280-b98e-a3132c9b5515 | contour-knowledge-transfer-for-salient-object | null | null | http://openaccess.thecvf.com/content_ECCV_2018/html/Xin_Li_Contour_Knowledge_Transfer_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Xin_Li_Contour_Knowledge_Transfer_ECCV_2018_paper.pdf | Contour Knowledge Transfer for Salient Object Detection | In recent years, deep Convolutional Neural Networks (CNNs) have broken all records in salient object detection. However, training such a deep model requires a large amount of manual annotations. Our goal is to overcome this limitation by automatically converting an existing deep contour detection model into a salient o... | ['Hong Cheng', 'Dinggang Shen', 'Wei Liu', 'Fan Yang', 'Xin Li'] | 2018-09-01 | null | null | null | eccv-2018-9 | ['contour-detection'] | ['computer-vision'] | [ 5.99603295e-01 3.68536204e-01 -1.67655453e-01 -3.39869976e-01
-5.67629993e-01 -2.63967752e-01 4.25080210e-01 2.73320526e-01
-2.16313258e-01 5.12808025e-01 3.07568163e-02 -1.67591691e-01
5.66976488e-01 -8.71216655e-01 -8.70777369e-01 -5.72266340e-01
2.75620580e-01 5.76343387e-02 1.23838449e+00 -2.41989240... | [9.812196731567383, -0.383261114358902] |
6d178cd1-55b9-4883-b324-a64e7db39e51 | gastric-cancer-detection-from-x-ray-images | 2108.08158 | null | https://arxiv.org/abs/2108.08158v2 | https://arxiv.org/pdf/2108.08158v2.pdf | Practical X-ray Gastric Cancer Screening Using Refined Stochastic Data Augmentation and Hard Boundary Box Training | In gastric cancer screening, X-rays can be performed by radiographers, allowing them to see far more patients than endoscopy, which can only be performed by physicians. However, due to subsequent diagnostic difficulties, the sensitivity of gastric X-ray is only 85.5%, and little research has been done on automated diag... | ['Hitoshi Iyatomi', 'Jun Hashimoto', 'Kazuhito Nabeshima', 'Takakiyo Nomura', 'Hideaki Okamoto'] | 2021-08-18 | null | null | null | null | ['image-augmentation'] | ['computer-vision'] | [ 2.19602153e-01 6.06042027e-01 -2.07239375e-01 1.18004225e-01
-1.08559024e+00 -2.10680276e-01 1.67786151e-01 2.44802788e-01
-4.54136461e-01 4.27205801e-01 -1.94205850e-01 -7.19600737e-01
2.84631819e-01 -1.05055606e+00 -8.05244386e-01 -1.03000855e+00
-1.50845021e-01 4.82356995e-01 5.97001672e-01 -3.77914906... | [15.018389701843262, -2.554835081100464] |
9c8c1d1c-dbe1-48e9-b165-95f1af21484e | an-end-to-end-ocr-text-re-organization | null | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/4879_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123700086.pdf | An End-to-End OCR Text Re-organization Sequence Learning for Rich-text Detail Image Comprehension | Nowadays rich description on detail images help users know more about the commodities. With the help of OCR technology, the description text can be detected and recognized as auxiliary information to remove the comprehending barriers among the visual impaired users. However, for lack of proper logical structure among t... | ['Zhi Yu', 'Jiajun Bu', 'Feiyu Gao', 'Yongpan Wang', 'Qi Zheng', 'Liangcheng Li'] | null | null | null | null | eccv-2020-8 | ['image-comprehension'] | ['computer-vision'] | [ 4.13458526e-01 5.84041029e-02 7.54998922e-02 -2.00467303e-01
-2.28873026e-02 -4.04142141e-01 9.55238789e-02 -8.44204873e-02
-2.98811376e-01 1.22887217e-01 9.17004228e-01 -4.85358298e-01
-5.15517369e-02 -3.40918124e-01 -5.04771590e-01 -1.83823586e-01
3.90189826e-01 4.45571467e-02 1.16810985e-02 -3.74565601... | [11.713937759399414, 2.2448625564575195] |
c5c70669-bafe-455f-95e2-3d4199e003ab | evaluation-and-analysis-of-different | 2202.08261 | null | https://arxiv.org/abs/2202.08261v2 | https://arxiv.org/pdf/2202.08261v2.pdf | Evaluation and Analysis of Different Aggregation and Hyperparameter Selection Methods for Federated Brain Tumor Segmentation | Availability of large, diverse, and multi-national datasets is crucial for the development of effective and clinically applicable AI systems in the medical imaging domain. However, forming a global model by bringing these datasets together at a central location, comes along with various data privacy and ownership probl... | ['Alptekin Temizel', 'Altan Kocyigit', 'Gorkem Polat', 'Ece Isik-Polat'] | 2022-02-16 | null | null | null | null | ['brain-tumor-segmentation'] | ['medical'] | [-2.88898289e-01 9.65425149e-02 -5.32927513e-01 -6.35165691e-01
-1.03791869e+00 -3.28583866e-01 1.26087606e-01 1.52705655e-01
-5.23144662e-01 9.48922634e-01 2.89589584e-01 -2.54822880e-01
-4.78997171e-01 -5.45722544e-01 -2.61347890e-01 -9.83991027e-01
-8.40682313e-02 5.06947994e-01 -1.02436751e-01 3.05128992... | [6.152334213256836, 6.4794769287109375] |
2a7ef47c-54e3-4ea0-b5ab-b9fc273167ad | mutual-balancing-in-state-object-components | 2211.10647 | null | https://arxiv.org/abs/2211.10647v1 | https://arxiv.org/pdf/2211.10647v1.pdf | Mutual Balancing in State-Object Components for Compositional Zero-Shot Learning | Compositional Zero-Shot Learning (CZSL) aims to recognize unseen compositions from seen states and objects. The disparity between the manually labeled semantic information and its actual visual features causes a significant imbalance of visual deviation in the distribution of various object classes and state classes, w... | ['Ling Shao', 'Haofeng Zhang', 'Yuming Shen', 'Shidong Wang', 'Dubing Chen', 'Chenyi Jiang'] | 2022-11-19 | null | null | null | null | ['compositional-zero-shot-learning'] | ['computer-vision'] | [ 3.68098646e-01 -8.56506750e-02 -3.66250813e-01 -2.03791901e-01
-8.62859547e-01 -6.55690610e-01 6.41126037e-01 -3.22454758e-02
-4.85775955e-02 6.25195384e-01 1.66343674e-01 -2.56249577e-01
6.70260340e-02 -6.34986520e-01 -5.05716085e-01 -1.24855399e+00
7.02890694e-01 4.73713845e-01 4.17168587e-01 -3.05020176... | [10.11493968963623, 2.3427839279174805] |
84742806-46d4-4521-aa40-aa03cce433a4 | data-driven-color-augmentation-techniques-for | 1703.03702 | null | http://arxiv.org/abs/1703.03702v1 | http://arxiv.org/pdf/1703.03702v1.pdf | Data-Driven Color Augmentation Techniques for Deep Skin Image Analysis | Dermoscopic skin images are often obtained with different imaging devices,
under varying acquisition conditions. In this work, instead of attempting to
perform intensity and color normalization, we propose to leverage computational
color constancy techniques to build an artificial data augmentation technique
suitable f... | ['Aurélio Campilho', 'A. M. Mendonça', 'Guilherme Aresta', 'Teresa Araújo', 'Aitor Alvarez-Gila', 'Maria Ines Meyer', 'Adrian Galdran', 'Pedro Costa', 'Estibaliz Garrote', 'Cristina L. Saratxaga'] | 2017-03-10 | null | null | null | null | ['color-constancy', 'skin-lesion-segmentation', 'skin-lesion-classification'] | ['computer-vision', 'medical', 'medical'] | [ 1.04584205e+00 -1.02174357e-01 -1.79219037e-01 -4.03037101e-01
-3.69950622e-01 -6.58548534e-01 4.29538697e-01 -3.23701836e-02
-6.55477345e-01 3.82798016e-01 -3.28890055e-01 -5.36217570e-01
2.18396410e-01 -5.80159903e-01 -5.04653990e-01 -7.71146834e-01
2.27400824e-01 -1.88994929e-01 -1.26483455e-01 1.26943231... | [15.655868530273438, -2.9276156425476074] |
5f0a2c37-1cdb-4019-a44f-d70ecdc604a0 | towards-generalizable-and-robust-text-to-sql | 2210.12674 | null | https://arxiv.org/abs/2210.12674v1 | https://arxiv.org/pdf/2210.12674v1.pdf | Towards Generalizable and Robust Text-to-SQL Parsing | Text-to-SQL parsing tackles the problem of mapping natural language questions to executable SQL queries. In practice, text-to-SQL parsers often encounter various challenging scenarios, requiring them to be generalizable and robust. While most existing work addresses a particular generalization or robustness challenge, ... | ['Yongbin Li', 'Luo Si', 'Fei Huang', 'Binhua Li', 'Wai Lam', 'Wenxuan Zhang', 'Bowen Li', 'Chang Gao'] | 2022-10-23 | null | null | null | null | ['text-to-sql'] | ['computer-code'] | [ 4.89244983e-02 -7.01355413e-02 -1.99789599e-01 -7.61752427e-01
-1.10124052e+00 -9.96112168e-01 1.50089115e-01 2.11687550e-01
1.03703409e-01 1.26362815e-01 1.53650502e-02 -6.78743124e-01
-2.14696199e-01 -9.83645320e-01 -1.08706665e+00 -9.94570702e-02
2.02983961e-01 4.93349075e-01 6.84265137e-01 -3.02792519... | [9.89306354522705, 7.860285758972168] |
444a157a-c864-406e-960d-d01af10f50fd | littleyolo-spp-a-delicate-real-time-vehicle | 2011.05940 | null | https://arxiv.org/abs/2011.05940v1 | https://arxiv.org/pdf/2011.05940v1.pdf | LittleYOLO-SPP: A Delicate Real-Time Vehicle Detection Algorithm | Vehicle detection in real-time is a challenging and important task. The existing real-time vehicle detection lacks accuracy and speed. Real-time systems must detect and locate vehicles during criminal activities like theft of vehicle and road traffic violations with high accuracy. Detection of vehicles in complex scene... | ['Esther Rani P', 'Sri Jamiya S'] | 2020-11-11 | null | null | null | null | ['fast-vehicle-detection'] | ['computer-vision'] | [-2.91344523e-01 -5.26373863e-01 3.85325029e-02 -4.02849555e-01
-4.60953385e-01 -3.91412973e-01 4.10350144e-01 -2.19544277e-01
-8.70531321e-01 4.04054075e-01 -7.49939024e-01 -3.36683393e-01
4.79996622e-01 -1.01854336e+00 -8.27935696e-01 -7.68768191e-01
-3.95649821e-02 -9.96418148e-02 9.69745576e-01 -1.52681962... | [8.155455589294434, -0.9164428114891052] |
2406f2f1-5dbf-44cb-9a56-6ba26388c4d2 | 2d-gans-meet-unsupervised-single-view-3d | 2207.10183 | null | https://arxiv.org/abs/2207.10183v1 | https://arxiv.org/pdf/2207.10183v1.pdf | 2D GANs Meet Unsupervised Single-view 3D Reconstruction | Recent research has shown that controllable image generation based on pre-trained GANs can benefit a wide range of computer vision tasks. However, less attention has been devoted to 3D vision tasks. In light of this, we propose a novel image-conditioned neural implicit field, which can leverage 2D supervisions from GAN... | ['Xiaoming Liu', 'Feng Liu'] | 2022-07-20 | null | null | null | null | ['single-view-3d-reconstruction'] | ['computer-vision'] | [ 3.46709818e-01 5.67886531e-01 1.52517855e-01 -4.14771408e-01
-6.99421883e-01 -3.66874784e-01 8.07164371e-01 -7.86430955e-01
1.30567523e-02 6.63627267e-01 1.75451204e-01 9.03768912e-02
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6.77428186e-01 4.51360762e-01 -1.30410820e-01 -2.12572627... | [9.250606536865234, -3.165703535079956] |
442b2efc-7d69-4ce0-989d-b4df492625c3 | on-second-thought-let-s-not-think-step-by | 2212.08061 | null | https://arxiv.org/abs/2212.08061v2 | https://arxiv.org/pdf/2212.08061v2.pdf | On Second Thought, Let's Not Think Step by Step! Bias and Toxicity in Zero-Shot Reasoning | Generating a Chain of Thought (CoT) has been shown to consistently improve large language model (LLM) performance on a wide range of NLP tasks. However, prior work has mainly focused on logical reasoning tasks (e.g. arithmetic, commonsense QA); it remains unclear whether improvements hold for more diverse types of reas... | ['Diyi Yang', 'Michael Bernstein', 'William Held', 'Hongxin Zhang', 'Omar Shaikh'] | 2022-12-15 | null | null | null | null | ['logical-reasoning'] | ['reasoning'] | [ 9.04778093e-02 6.15603387e-01 -5.44313975e-02 -5.04082680e-01
-8.09976041e-01 -5.51881552e-01 5.97811043e-01 4.96854067e-01
-5.04013538e-01 5.88377178e-01 9.81159985e-01 -6.80262983e-01
-3.41475219e-01 -6.82487965e-01 -5.53892612e-01 -2.11146936e-01
4.91344750e-01 4.90574658e-01 -6.41431734e-02 -5.78359187... | [9.994014739990234, 7.606517314910889] |
96a4376b-640a-4360-8906-73d077719a88 | on-the-limitations-of-continual-learning-for | 2208.06568 | null | https://arxiv.org/abs/2208.06568v1 | https://arxiv.org/pdf/2208.06568v1.pdf | On the Limitations of Continual Learning for Malware Classification | Malicious software (malware) classification offers a unique challenge for continual learning (CL) regimes due to the volume of new samples received on a daily basis and the evolution of malware to exploit new vulnerabilities. On a typical day, antivirus vendors receive hundreds of thousands of unique pieces of software... | ['Matthew Wright', 'Scott E. Coull', 'Mohammad Saidur Rahman'] | 2022-08-13 | null | null | null | null | ['classification'] | ['methodology'] | [ 2.49323770e-01 -5.46432257e-01 -5.86708307e-01 -1.44180208e-01
-6.06885672e-01 -8.25365782e-01 8.78148973e-01 2.67064184e-01
-3.04650486e-01 6.95482373e-01 -4.67596412e-01 -1.10023057e+00
1.33156955e-01 -4.02211040e-01 -7.56336510e-01 -6.18147492e-01
-6.05691731e-01 4.48042333e-01 5.39708018e-01 -6.45300448... | [14.402678489685059, 9.667919158935547] |
a208fb4e-d5bd-4f1e-ba1f-3af6d65e82b2 | a-sequence-to-sequence-approach-for-document | 2204.01098 | null | https://arxiv.org/abs/2204.01098v2 | https://arxiv.org/pdf/2204.01098v2.pdf | A sequence-to-sequence approach for document-level relation extraction | Motivated by the fact that many relations cross the sentence boundary, there has been increasing interest in document-level relation extraction (DocRE). DocRE requires integrating information within and across sentences, capturing complex interactions between mentions of entities. Most existing methods are pipeline-bas... | ['Bo wang', 'Gary D. Bader', 'John Giorgi'] | 2022-04-03 | null | https://aclanthology.org/2022.bionlp-1.2 | https://aclanthology.org/2022.bionlp-1.2.pdf | bionlp-acl-2022-5 | ['document-level-relation-extraction', 'joint-entity-and-relation-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [ 7.13712573e-02 4.57619429e-01 -8.99426043e-02 -4.63373482e-01
-1.28364348e+00 -7.50066578e-01 4.82748121e-01 5.96126378e-01
-5.49974501e-01 8.94834936e-01 5.93992829e-01 -2.93210894e-01
-2.23485902e-01 -4.98925447e-01 -6.23691916e-01 -8.01888034e-02
-3.04123431e-01 6.66970611e-01 3.82079631e-01 -2.27814242... | [9.129508972167969, 8.93576717376709] |
62b4ead1-d873-4efd-ba36-4838a309e348 | signet-scalable-embeddings-for-signed | 1702.06819 | null | http://arxiv.org/abs/1702.06819v5 | http://arxiv.org/pdf/1702.06819v5.pdf | Distributed Representations of Signed Networks | Recent successes in word embedding and document embedding have motivated
researchers to explore similar representations for networks and to use such
representations for tasks such as edge prediction, node label prediction, and
community detection. Such network embedding methods are largely focused on
finding distribute... | ['Naren Ramakrishnan', 'B. Aditya Prakash', 'Mohammad Raihanul Islam'] | 2017-02-22 | null | null | null | null | ['document-embedding'] | ['methodology'] | [-5.02396747e-03 6.39312267e-01 -7.03092754e-01 -3.91977429e-01
5.26844203e-01 -4.55540955e-01 9.78928566e-01 7.30193138e-01
-1.92362547e-01 4.28600848e-01 8.25595260e-01 -3.84503722e-01
-6.23933733e-01 -1.24472368e+00 -5.66490628e-02 -1.35478795e-01
-6.22377038e-01 6.37179911e-01 1.50449380e-01 -5.95823109... | [7.151599407196045, 6.192379951477051] |
3f13e8aa-ab0a-478c-9102-442c67f4889d | oneee-a-one-stage-framework-for-fast | 2209.02693 | null | https://arxiv.org/abs/2209.02693v1 | https://arxiv.org/pdf/2209.02693v1.pdf | OneEE: A One-Stage Framework for Fast Overlapping and Nested Event Extraction | Event extraction (EE) is an essential task of information extraction, which aims to extract structured event information from unstructured text. Most prior work focuses on extracting flat events while neglecting overlapped or nested ones. A few models for overlapped and nested EE includes several successive stages to e... | ['Donghong Ji', 'Liang Zhao', 'Bobo Li', 'Shengqiong Wu', 'Hao Fei', 'Fei Li', 'Fangfang Su', 'Jingye Li', 'Hu Cao'] | 2022-09-06 | null | https://aclanthology.org/2022.coling-1.170 | https://aclanthology.org/2022.coling-1.170.pdf | coling-2022-10 | ['event-extraction'] | ['natural-language-processing'] | [ 1.20801158e-01 1.87916327e-02 -2.76161462e-01 -2.74146289e-01
-9.84678745e-01 -2.47360989e-01 6.79954350e-01 6.43555403e-01
-7.16533780e-01 6.98750496e-01 4.67287004e-01 -2.18115494e-01
3.05996127e-02 -1.05712795e+00 -5.33013225e-01 -6.92313373e-01
-3.66711289e-01 4.55622137e-01 5.78517556e-01 2.79505961... | [9.053628921508789, 9.129734992980957] |
ad7bb817-213e-4c83-b338-b1fb05b50ef6 | generating-data-for-symbolic-language-with | 2305.13917 | null | https://arxiv.org/abs/2305.13917v1 | https://arxiv.org/pdf/2305.13917v1.pdf | Generating Data for Symbolic Language with Large Language Models | While large language models (LLMs) bring not only performance but also complexity, recent work has started to turn LLMs into data generators rather than task inferencers, where another affordable task model is trained for efficient deployment and inference. However, such an approach has primarily been applied to natura... | ['Tao Yu', 'Lingpeng Kong', 'Chengzu Li', 'Jiacheng Ye'] | 2023-05-23 | null | null | null | null | ['code-generation', 'semantic-parsing'] | ['computer-code', 'natural-language-processing'] | [ 1.89693466e-01 7.20615387e-01 -1.69459641e-01 -6.00019157e-01
-1.33926809e+00 -5.88666797e-01 5.60272872e-01 -5.28155193e-02
-4.00761902e-01 9.37793314e-01 -2.62616724e-02 -6.03903890e-01
4.13635194e-01 -4.79825795e-01 -1.07230520e+00 -2.04446331e-01
9.02713612e-02 7.60763288e-01 1.67227626e-01 -1.10738073... | [10.776106834411621, 8.422845840454102] |
13715e1f-b22f-4acb-83d5-9d021079f475 | pmt-iqa-progressive-multi-task-learning-for | 2301.01182 | null | https://arxiv.org/abs/2301.01182v1 | https://arxiv.org/pdf/2301.01182v1.pdf | PMT-IQA: Progressive Multi-task Learning for Blind Image Quality Assessment | Blind image quality assessment (BIQA) remains challenging due to the diversity of distortion and image content variation, which complicate the distortion patterns crossing different scales and aggravate the difficulty of the regression problem for BIQA. However, existing BIQA methods often fail to consider multi-scale ... | ['Pei Yang', 'Jingyi Zhang', 'Letu Qingge', 'Ning Guo', 'Qingyi Pan'] | 2023-01-03 | null | null | null | null | ['blind-image-quality-assessment'] | ['computer-vision'] | [-5.77705679e-03 -9.41352487e-01 1.46963105e-01 -2.64535218e-01
-1.01377451e+00 -2.71198213e-01 2.81728774e-01 -3.08979034e-01
-1.96295634e-01 3.39037329e-01 3.54747206e-01 -1.67772919e-01
-3.78960311e-01 -4.27932233e-01 -3.32048684e-01 -7.62969375e-01
1.92631006e-01 -5.44191301e-02 4.39049453e-01 -2.23268300... | [11.857832908630371, -1.8606619834899902] |
71b6f313-8174-4318-9c5e-7c9150314905 | triple-classification-for-scholarly-knowledge | 2111.11845 | null | https://arxiv.org/abs/2111.11845v1 | https://arxiv.org/pdf/2111.11845v1.pdf | Triple Classification for Scholarly Knowledge Graph Completion | Scholarly Knowledge Graphs (KGs) provide a rich source of structured information representing knowledge encoded in scientific publications. With the sheer volume of published scientific literature comprising a plethora of inhomogeneous entities and relations to describe scientific concepts, these KGs are inherently inc... | ['Sören Auer', 'Markus Stocker', 'Kuldeep Singh', 'Mohamad Yaser Jaradeh'] | 2021-11-23 | null | null | null | null | ['triple-classification'] | ['graphs'] | [-4.14437473e-01 6.15994096e-01 -1.01192057e+00 1.59818791e-02
-6.61750913e-01 -6.76722050e-01 8.88835669e-01 6.43248856e-01
1.06228739e-01 9.32595015e-01 4.44428235e-01 -6.02734327e-01
-4.37709481e-01 -1.32645416e+00 -1.07770431e+00 1.21081471e-01
1.53019940e-02 7.19039559e-01 2.22831413e-01 -2.65423674... | [9.003183364868164, 8.014873504638672] |
c7aaa5a0-1feb-4336-b3b4-51f6ec4e35e0 | vision-aided-environment-semantics-extraction | 2301.08973 | null | https://arxiv.org/abs/2301.08973v2 | https://arxiv.org/pdf/2301.08973v2.pdf | Vision Aided Environment Semantics Extraction and Its Application in mmWave Beam Selection | In this letter, we propose a novel mmWave beam selection method based on the environment semantics extracted from user-side camera images. Specifically, we first define the environment semantics as the spatial distribution of the scatterers that affect the wireless propagation channels and utilize the keypoint detectio... | ['Guangyi Liu', 'Chengkang Pan', 'Feifei Gao', 'Weihua Xu', 'Feiyang Wen'] | 2023-01-21 | null | null | null | null | ['keypoint-detection'] | ['computer-vision'] | [ 1.21391360e-02 -5.72930336e-01 1.96769968e-01 -5.90641260e-01
-4.89738047e-01 -3.99601996e-01 8.57177451e-02 -3.32746565e-01
-4.63213563e-01 5.43579161e-01 -1.01593874e-01 -4.96648073e-01
-3.53886396e-01 -1.29054141e+00 -5.61437368e-01 -1.14289927e+00
2.02780575e-01 1.32153571e-01 1.39575541e-01 -1.53148212... | [6.367629528045654, 1.0862807035446167] |
9ee0fd31-5b49-482b-98e5-e51a7ff1af4a | efficient-passage-retrieval-with-hashing-for | 2106.00882 | null | https://arxiv.org/abs/2106.00882v1 | https://arxiv.org/pdf/2106.00882v1.pdf | Efficient Passage Retrieval with Hashing for Open-domain Question Answering | Most state-of-the-art open-domain question answering systems use a neural retrieval model to encode passages into continuous vectors and extract them from a knowledge source. However, such retrieval models often require large memory to run because of the massive size of their passage index. In this paper, we introduce ... | ['Hannaneh Hajishirzi', 'Akari Asai', 'Ikuya Yamada'] | 2021-06-02 | null | https://aclanthology.org/2021.acl-short.123 | https://aclanthology.org/2021.acl-short.123.pdf | acl-2021-5 | ['triviaqa'] | ['miscellaneous'] | [-2.01054484e-01 -3.49751115e-01 -2.77400881e-01 -1.01318493e-01
-1.84703445e+00 -6.60955608e-01 4.28354174e-01 5.79754472e-01
-6.13652408e-01 7.79158950e-01 4.59676266e-01 -5.15111625e-01
-2.42035463e-01 -1.12292409e+00 -8.80765855e-01 3.12609453e-04
7.84655362e-02 8.26515257e-01 5.35181165e-01 -6.14991367... | [11.4629545211792, 7.714874744415283] |
f96bee77-46f8-4585-be43-cd17527f4253 | graphpb-graphical-representations-of-prosody | 2012.02626 | null | https://arxiv.org/abs/2012.02626v1 | https://arxiv.org/pdf/2012.02626v1.pdf | GraphPB: Graphical Representations of Prosody Boundary in Speech Synthesis | This paper introduces a graphical representation approach of prosody boundary (GraphPB) in the task of Chinese speech synthesis, intending to parse the semantic and syntactic relationship of input sequences in a graphical domain for improving the prosody performance. The nodes of the graph embedding are formed by proso... | ['Jing Xiao', 'Lingwei Kong', 'Zhen Zeng', 'Huayi Peng', 'Ning Cheng', 'Jianzong Wang', 'Aolan Sun'] | 2020-12-03 | null | null | null | null | ['graph-to-sequence'] | ['natural-language-processing'] | [ 2.11797431e-01 6.40032887e-01 -3.36271256e-01 -1.40017092e-01
-2.33740389e-01 -3.21326137e-01 1.53355762e-01 5.47254607e-02
1.05131324e-03 7.70552635e-01 7.41552055e-01 -4.64722723e-01
3.74485821e-01 -7.21487582e-01 -5.38020194e-01 -4.25943524e-01
-1.65848285e-01 3.05537134e-01 3.89118850e-01 -3.96990508... | [14.768921852111816, 6.7308573722839355] |
20650e11-fd84-4554-8bc2-cc6272e12032 | ibm-research-at-the-conll-2018-shared-task-on | null | null | https://aclanthology.org/K18-2009 | https://aclanthology.org/K18-2009.pdf | IBM Research at the CoNLL 2018 Shared Task on Multilingual Parsing | This paper presents the IBM Research AI submission to the CoNLL 2018 Shared Task on Parsing Universal Dependencies. Our system implements a new joint transition-based parser, based on the Stack-LSTM framework and the Arc-Standard algorithm, that handles tokenization, part-of-speech tagging, morphological tagging and de... | ['Miguel Ballesteros', 'Vittorio Castelli', 'Young-suk Lee', 'Hui Wan', 'Tahira Naseem'] | 2018-10-01 | null | null | null | conll-2018-10 | ['morphological-tagging'] | ['natural-language-processing'] | [ 1.40296444e-01 3.69290024e-01 -2.01375544e-01 -5.90236962e-01
-1.15196157e+00 -8.28971744e-01 4.20549363e-02 3.80068362e-01
-8.73008728e-01 7.29475498e-01 4.81379867e-01 -9.39567327e-01
3.53708059e-01 -6.01476490e-01 -7.62565970e-01 -1.65837541e-01
-3.01209897e-01 6.09656930e-01 4.18651640e-01 -1.29321426... | [10.33702278137207, 9.687949180603027] |
89ffc02a-9690-4fb0-91e8-9e49f5371e7b | pysindy-a-comprehensive-python-package-for | 2111.08481 | null | https://arxiv.org/abs/2111.08481v2 | https://arxiv.org/pdf/2111.08481v2.pdf | PySINDy: A comprehensive Python package for robust sparse system identification | Automated data-driven modeling, the process of directly discovering the governing equations of a system from data, is increasingly being used across the scientific community. PySINDy is a Python package that provides tools for applying the sparse identification of nonlinear dynamics (SINDy) approach to data-driven mode... | ['Steven L. Brunton', 'J. Nathan Kutz', 'Zachary G. Nicolaou', 'Charles B. Delahunt', 'Jared L. Callaham', 'Andy J. Goldschmidt', 'Jean-Christophe Loiseau', 'Kathleen Champion', 'Kadierdan Kaheman', 'Urban Fasel', 'Brian M. de Silva', 'Alan A. Kaptanoglu'] | 2021-11-12 | null | null | null | null | ['model-discovery'] | ['miscellaneous'] | [-1.31707296e-01 -3.74574661e-01 7.30606467e-02 -1.19247347e-01
-5.93985140e-01 -5.90761423e-01 2.96090961e-01 -2.31818706e-01
2.46474206e-01 8.36012483e-01 1.45173132e-01 -1.16098471e-01
-7.14720726e-01 -3.06751318e-02 -5.07228911e-01 -9.70021129e-01
-4.74913239e-01 6.58127725e-01 -4.84303743e-01 -5.02835512... | [6.556718349456787, 3.5146307945251465] |
8b20cb19-1e09-421e-849f-6d22bb113369 | visual-textual-attentive-semantic-consistency | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Zhou_Visual-Textual_Attentive_Semantic_Consistency_for_Medical_Report_Generation_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Zhou_Visual-Textual_Attentive_Semantic_Consistency_for_Medical_Report_Generation_ICCV_2021_paper.pdf | Visual-Textual Attentive Semantic Consistency for Medical Report Generation | Diagnosing diseases from medical radiographs and writing reports requires professional knowledge and is time-consuming. To address this, automatic medical report generation approaches have recently gained interest. However, identifying diseases as well as correctly predicting their corresponding sizes, locations an... | ['Ling Shao', 'Huazhu Fu', 'Tao Zhou', 'Lei Huang', 'Yi Zhou'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['medical-report-generation'] | ['medical'] | [ 4.91329491e-01 1.14700615e-01 -2.79829383e-01 -6.38361335e-01
-1.49883020e+00 -2.99474359e-01 2.97232240e-01 6.43757284e-01
5.80259226e-02 5.99735200e-01 8.01685631e-01 -9.15607214e-02
-1.17899716e-01 -6.63814425e-01 -8.31570029e-01 -4.26015705e-01
7.57843256e-02 5.44278026e-01 1.78410485e-02 4.65879768... | [15.034404754638672, -1.4261852502822876] |
b3df0f4a-5cba-45a2-b628-8b4a98bba386 | tattoo-image-search-at-scale-joint-detection | 1811.00218 | null | http://arxiv.org/abs/1811.00218v1 | http://arxiv.org/pdf/1811.00218v1.pdf | Tattoo Image Search at Scale: Joint Detection and Compact Representation Learning | The explosive growth of digital images in video surveillance and social media
has led to the significant need for efficient search of persons of interest in
law enforcement and forensic applications. Despite tremendous progress in
primary biometric traits (e.g., face and fingerprint) based person
identification, a sing... | ['Xilin Chen', 'Anil K. Jain', 'Shiguang Shan', 'Jie Li', 'Hu Han'] | 2018-11-01 | null | null | null | null | ['person-identification'] | ['computer-vision'] | [ 1.05996929e-01 -9.01191950e-01 -8.36296305e-02 -3.17044288e-01
-6.14071846e-01 -7.42279112e-01 5.26790977e-01 -3.06800365e-01
-3.34534287e-01 2.94023097e-01 -3.69007498e-01 -1.94352403e-01
-3.52174014e-01 -8.46399307e-01 -3.79359156e-01 -7.14056551e-01
2.05121920e-01 6.56626165e-01 -1.71971291e-01 -1.84299886... | [11.847639083862305, 0.6684306263923645] |
1adadcc1-3140-4eb2-ba88-28c8ba7832e7 | pppne-personalized-proximity-preserved | null | null | http://fange.pro/files/2021PPPNE.pdf | http://fange.pro/files/2021PPPNE.pdf | PPPNE: Personalized proximity preserved network embedding | After being proved extremely useful in many applications, the network embedding has played a critical role in the network analysis. Most of recent works usually model the network by minimizing the joint probability that the target node co-occurs with its neighboring nodes. These methods may fail to capture the personal... | ['Wei Zeng', 'Changjie Fan', 'Kai Wang', 'Jianrong Tao', 'Biao Geng', 'Ge Fan'] | 2022-02-01 | null | null | null | neurocomputing-2022-2 | ['network-embedding'] | ['methodology'] | [-1.01079218e-01 1.79904044e-01 -7.87872195e-01 -2.15167522e-01
3.91601659e-02 -4.57681179e-01 4.67550755e-01 3.68557632e-01
-4.66942713e-02 5.79023957e-01 2.86161751e-01 -1.21803962e-01
-6.86260104e-01 -1.13921714e+00 -6.76495016e-01 -7.02106833e-01
-4.94718701e-01 6.77150071e-01 2.73589700e-01 -1.36319265... | [7.294334411621094, 6.178258419036865] |
efd5d862-619a-49fd-b297-9b26f869ee1b | large-neural-networks-learning-from-scratch | 2205.08836 | null | https://arxiv.org/abs/2205.08836v2 | https://arxiv.org/pdf/2205.08836v2.pdf | Large Neural Networks Learning from Scratch with Very Few Data and without Explicit Regularization | Recent findings have shown that highly over-parameterized Neural Networks generalize without pretraining or explicit regularization. It is achieved with zero training error, i.e., complete over-fitting by memorizing the training data. This is surprising, since it is completely against traditional machine learning wisdo... | ['Thomas Martinetz', 'Christoph Linse'] | 2022-05-18 | null | null | null | null | ['image-augmentation', 'fine-grained-image-classification'] | ['computer-vision', 'computer-vision'] | [ 1.54816806e-01 3.97907972e-01 -1.13667235e-01 -5.03081262e-01
-2.25003794e-01 -6.34400010e-01 5.19343376e-01 -1.72352672e-01
-8.50687921e-01 1.00745082e+00 -1.90493450e-01 -4.15408224e-01
-1.22577772e-01 -8.54858398e-01 -1.20722497e+00 -5.43182969e-01
-4.70216349e-02 5.54699838e-01 5.54551929e-02 -2.98033237... | [9.015288352966309, 2.9267430305480957] |
02d0b6f7-9deb-4385-87ae-d4708c9aa6e1 | prompting-language-models-for-linguistic | 2211.07830 | null | https://arxiv.org/abs/2211.07830v2 | https://arxiv.org/pdf/2211.07830v2.pdf | Prompting Language Models for Linguistic Structure | Although pretrained language models (PLMs) can be prompted to perform a wide range of language tasks, it remains an open question how much this ability comes from generalizable linguistic understanding versus surface-level lexical patterns. To test this, we present a structured prompting approach for linguistic structu... | ['Luke Zettlemoyer', 'Hila Gonen', 'Terra Blevins'] | 2022-11-15 | null | null | null | null | ['memorization'] | ['natural-language-processing'] | [ 3.86098087e-01 5.34566998e-01 -4.00687903e-01 -6.47944868e-01
-7.51245856e-01 -6.51201308e-01 6.20902777e-01 5.13385653e-01
-6.80825055e-01 7.25469053e-01 5.82116127e-01 -5.06302476e-01
2.42014185e-01 -5.27646005e-01 -6.15417898e-01 -1.51552677e-01
-1.57551855e-01 4.08660442e-01 2.26874977e-01 -1.87119648... | [10.771398544311523, 8.60168170928955] |
3713f331-22ba-47b5-99a2-b14f618dc65a | machine-learning-enabled-experimental-design | 2306.02015 | null | https://arxiv.org/abs/2306.02015v1 | https://arxiv.org/pdf/2306.02015v1.pdf | Machine learning enabled experimental design and parameter estimation for ultrafast spin dynamics | Advanced experimental measurements are crucial for driving theoretical developments and unveiling novel phenomena in condensed matter and material physics, which often suffer from the scarcity of facility resources and increasing complexities. To address the limitations, we introduce a methodology that combines machine... | ['Joshua J. Turner', 'Chun Hong Yoon', 'Sugata Chowdhury', 'Alana Okullo', 'Sathya R. Chitturi', 'Alexander N. Petsch', 'Cheng Peng', 'Zhantao Chen'] | 2023-06-03 | null | null | null | null | ['experimental-design'] | ['methodology'] | [ 1.61020622e-01 -5.66973269e-01 -4.85779554e-01 -4.00410861e-01
-5.99621654e-01 -3.21654856e-01 6.11725807e-01 -9.23339874e-02
-4.17745948e-01 1.21801162e+00 5.74147180e-02 -5.65665901e-01
-4.01803255e-01 -5.67562819e-01 -4.07857478e-01 -1.27587223e+00
3.70898135e-02 7.48432159e-01 1.48672296e-03 -2.41993014... | [5.647029876708984, 4.844171524047852] |
29e660df-436b-4a82-bbd6-4a95217f4638 | ads-me-anomaly-detection-system-for-micro | 1903.04354 | null | http://arxiv.org/abs/1903.04354v1 | http://arxiv.org/pdf/1903.04354v1.pdf | ADS-ME: Anomaly Detection System for Micro-expression Spotting | Micro-expressions (MEs) are infrequent and uncontrollable facial events that
can highlight emotional deception and appear in a high-stakes environment. This
paper propose an algorithm for spatiotemporal MEs spotting. Since MEs are
unusual events, we treat them as abnormal patterns that diverge from expected
Normal Faci... | ['Alice Caplier', 'Dawood Al Chanti'] | 2019-03-11 | null | null | null | null | ['micro-expression-spotting'] | ['computer-vision'] | [ 3.70586440e-02 8.06751177e-02 -2.19555832e-02 -4.66124833e-01
-3.41688454e-01 -3.92868400e-01 5.74862480e-01 -4.11240608e-01
-2.08042219e-01 5.85312605e-01 2.58696407e-01 3.00934345e-01
6.44153208e-02 -2.73039907e-01 -7.05552876e-01 -8.09410155e-01
-4.10020620e-01 -3.03656429e-01 -2.77076989e-01 1.57137409... | [13.610381126403809, 1.8609869480133057] |
4355948f-48ea-4e7e-adf1-7820bedaa315 | implicit-compressibility-of-overparametrized | 2306.08125 | null | https://arxiv.org/abs/2306.08125v1 | https://arxiv.org/pdf/2306.08125v1.pdf | Implicit Compressibility of Overparametrized Neural Networks Trained with Heavy-Tailed SGD | Neural network compression has been an increasingly important subject, due to its practical implications in terms of reducing the computational requirements and its theoretical implications, as there is an explicit connection between compressibility and the generalization error. Recent studies have shown that the choic... | ['Umut Simsekli', 'Abdellatif Zaidi', 'Yijun Wan'] | 2023-06-13 | null | null | null | null | ['neural-network-compression', 'neural-network-compression'] | ['methodology', 'miscellaneous'] | [ 7.71503597e-02 1.43921182e-01 6.62889779e-02 -8.51762518e-02
-2.59430587e-01 -1.89801961e-01 3.08533281e-01 -6.82391450e-02
-6.63307667e-01 8.32172930e-01 -1.83218509e-01 -4.88757163e-01
-4.10501927e-01 -7.15864182e-01 -9.14955616e-01 -1.21621525e+00
-4.41798568e-01 2.69888848e-01 5.86046837e-03 -4.27575856... | [7.633775234222412, 3.711951732635498] |
285b14fd-29c5-4687-9071-be038ecc97c7 | t-star-truthful-style-transfer-using-amr | 2212.01667 | null | https://arxiv.org/abs/2212.01667v1 | https://arxiv.org/pdf/2212.01667v1.pdf | T-STAR: Truthful Style Transfer using AMR Graph as Intermediate Representation | Unavailability of parallel corpora for training text style transfer (TST) models is a very challenging yet common scenario. Also, TST models implicitly need to preserve the content while transforming a source sentence into the target style. To tackle these problems, an intermediate representation is often constructed t... | ['Aravindan Raghuveer', 'Preksha Nema', 'Anubhav Jangra'] | 2022-12-03 | null | null | null | null | ['text-style-transfoer'] | ['natural-language-processing'] | [ 3.97292972e-01 2.79284120e-01 1.71879325e-02 -4.72863883e-01
-7.19065428e-01 -5.56836486e-01 6.58435166e-01 -4.71175537e-02
-1.99526817e-01 7.39707470e-01 4.33758229e-01 -4.05754685e-01
3.93498540e-01 -5.97744524e-01 -6.93397105e-01 -2.49850795e-01
7.62455463e-01 4.06540811e-01 -5.30609526e-02 -6.01636827... | [11.659430503845215, 9.527541160583496] |
34ddf460-61ef-4e91-99b2-26148f093f9c | b-bacn-bayesian-boundary-aware-convolutional | 2302.06827 | null | https://arxiv.org/abs/2302.06827v3 | https://arxiv.org/pdf/2302.06827v3.pdf | B-BACN: Bayesian Boundary-Aware Convolutional Network for Crack Characterization | Accurately detecting crack boundaries is crucial for reliability assessment and risk management of structures and materials, such as structural health monitoring, diagnostics, prognostics, and maintenance scheduling. Uncertainty quantification of crack detection is challenging due to various stochastic factors, such as... | ['Yongming Liu', 'Yutian Pang', 'Rahul Rathnakumar'] | 2023-02-14 | null | null | null | null | ['boundary-detection'] | ['computer-vision'] | [ 1.52906418e-01 -1.27839059e-01 1.68242052e-01 -4.10599113e-01
-1.28682387e+00 2.08364129e-01 -1.79333165e-02 4.40450698e-01
-1.33242980e-01 7.94777334e-01 -8.01079497e-02 -1.52203858e-01
-5.61682522e-01 -8.22938681e-01 -6.56484604e-01 -8.50601256e-01
2.02120453e-01 6.05094492e-01 4.66174752e-01 2.79615462... | [7.065578937530518, 2.3419435024261475] |
d060db4f-c9a9-4bbe-baae-9a30a1a6ad41 | joint-multilingual-supervision-for-cross | 1809.07657 | null | http://arxiv.org/abs/1809.07657v1 | http://arxiv.org/pdf/1809.07657v1.pdf | Joint Multilingual Supervision for Cross-lingual Entity Linking | Cross-lingual Entity Linking (XEL) aims to ground entity mentions written in
any language to an English Knowledge Base (KB), such as Wikipedia. XEL for most
languages is challenging, owing to limited availability of resources as
supervision. We address this challenge by developing the first XEL approach
that combines s... | ['Shyam Upadhyay', 'Dan Roth', 'Nitish Gupta'] | 2018-09-20 | joint-multilingual-supervision-for-cross-1 | https://aclanthology.org/D18-1270 | https://aclanthology.org/D18-1270.pdf | emnlp-2018-10 | ['cross-lingual-entity-linking'] | ['natural-language-processing'] | [-2.79052466e-01 3.31903726e-01 -7.01007783e-01 -8.07136521e-02
-1.17732573e+00 -6.50527716e-01 7.49387205e-01 2.76435018e-01
-8.88046920e-01 1.11596680e+00 3.42278689e-01 -2.76662678e-01
2.85526454e-01 -7.37107873e-01 -9.79662955e-01 1.14725260e-02
1.12923793e-01 6.01487219e-01 5.52718878e-01 -4.85766321... | [9.565901756286621, 8.951011657714844] |
d5158adf-47b0-4587-b3fb-cf97f0e9820c | autonoml-towards-an-integrated-framework-for | 2012.12600 | null | https://arxiv.org/abs/2012.12600v2 | https://arxiv.org/pdf/2012.12600v2.pdf | AutonoML: Towards an Integrated Framework for Autonomous Machine Learning | Over the last decade, the long-running endeavour to automate high-level processes in machine learning (ML) has risen to mainstream prominence, stimulated by advances in optimisation techniques and their impact on selecting ML models/algorithms. Central to this drive is the appeal of engineering a computational system t... | ['Bogdan Gabrys', 'Katarzyna Musial', 'David Jacob Kedziora'] | 2020-12-23 | null | null | null | null | ['automated-feature-engineering'] | ['methodology'] | [ 5.38617730e-01 1.49034381e-01 -1.32663339e-01 -2.84780502e-01
-5.77998579e-01 -6.59052789e-01 5.42743623e-01 2.03118816e-01
-4.75957513e-01 6.90832078e-01 -3.50839108e-01 -1.88911602e-01
-8.11036706e-01 -2.62496650e-01 -9.77702960e-02 -9.16690469e-01
2.39596586e-03 4.62746710e-01 -3.51297736e-01 -2.20741943... | [6.385350227355957, 3.869044303894043] |
940359ac-814d-4f85-8a87-edbdd0782b45 | improving-autoencoder-based-outlier-detection | 2304.00709 | null | https://arxiv.org/abs/2304.00709v1 | https://arxiv.org/pdf/2304.00709v1.pdf | Improving Autoencoder-based Outlier Detection with Adjustable Probabilistic Reconstruction Error and Mean-shift Outlier Scoring | Autoencoders were widely used in many machine learning tasks thanks to their strong learning ability which has drawn great interest among researchers in the field of outlier detection. However, conventional autoencoder-based methods lacked considerations in two aspects. This limited their performance in outlier detecti... | ['Susanto Rahardja', 'Sylwan Rahardja', 'Junqi Chen', 'Jiawei Yang', 'Xu Tan'] | 2023-04-03 | null | null | null | null | ['outlier-detection'] | ['methodology'] | [-4.77564484e-01 -1.44569039e-01 3.63828123e-01 -1.76077351e-01
-4.47397470e-01 1.24227315e-01 3.19337070e-01 2.63909101e-01
-4.30507332e-01 5.44188559e-01 1.67310610e-01 1.53520674e-01
-2.26155058e-01 -7.74858892e-01 -7.28367627e-01 -8.29073191e-01
-1.49942875e-01 8.11097845e-02 1.23743117e-01 9.35658533... | [7.653299808502197, 2.6444783210754395] |
3a5bed1e-f06e-4292-bc54-7164e9d9f6ee | anomalous-motion-detection-on-highway-using | 2006.08143 | null | https://arxiv.org/abs/2006.08143v1 | https://arxiv.org/pdf/2006.08143v1.pdf | Anomalous Motion Detection on Highway Using Deep Learning | Research in visual anomaly detection draws much interest due to its applications in surveillance. Common datasets for evaluation are constructed using a stationary camera overlooking a region of interest. Previous research has shown promising results in detecting spatial as well as temporal anomalies in these settings.... | ['Harpreet Singh', 'Emily M. Hand', 'Kostas Alexis'] | 2020-06-15 | null | null | null | null | ['motion-detection'] | ['computer-vision'] | [ 1.14976447e-02 -4.39043254e-01 1.57582268e-01 -4.07660455e-01
1.37162851e-02 -2.49022380e-01 9.62052226e-01 3.01586330e-01
-4.40983295e-01 2.38969103e-02 -1.04095906e-01 -8.83274138e-01
2.33961537e-01 -5.91816425e-01 -7.73825109e-01 -4.97633398e-01
-4.96364892e-01 1.98486090e-01 7.49030232e-01 -5.06317317... | [7.874203205108643, 1.5574196577072144] |
dd4cb117-1cd1-49b1-bd96-b7894124acb1 | synclay-interactive-synthesis-of-histology | 2212.13780 | null | https://arxiv.org/abs/2212.13780v1 | https://arxiv.org/pdf/2212.13780v1.pdf | SynCLay: Interactive Synthesis of Histology Images from Bespoke Cellular Layouts | Automated synthesis of histology images has several potential applications in computational pathology. However, no existing method can generate realistic tissue images with a bespoke cellular layout or user-defined histology parameters. In this work, we propose a novel framework called SynCLay (Synthesis from Cellular ... | ['Nasir Rajpoot', 'Fayyaz Minhas', 'Muhammad Dawood', 'Srijay Deshpande'] | 2022-12-28 | null | null | null | null | ['nuclear-segmentation'] | ['medical'] | [ 3.18488330e-01 2.55415797e-01 3.14933300e-01 -1.04375727e-01
-7.95477748e-01 -7.34020233e-01 3.66588026e-01 3.48052830e-01
-3.38801384e-01 7.95208931e-01 -1.20349117e-01 -2.44736731e-01
2.08295658e-01 -8.65033567e-01 -7.53791451e-01 -1.12372303e+00
1.52985260e-01 7.96197712e-01 1.48967177e-01 1.44941285... | [14.78854751586914, -2.6412007808685303] |
cce69782-fdd2-4b78-97f8-c6e7d6e4ff34 | error-corrected-margin-based-deep-cross-modal | 2004.03378 | null | https://arxiv.org/abs/2004.03378v1 | https://arxiv.org/pdf/2004.03378v1.pdf | Error-Corrected Margin-Based Deep Cross-Modal Hashing for Facial Image Retrieval | Cross-modal hashing facilitates mapping of heterogeneous multimedia data into a common Hamming space, which can beutilized for fast and flexible retrieval across different modalities. In this paper, we propose a novel cross-modal hashingarchitecture-deep neural decoder cross-modal hashing (DNDCMH), which uses a binary ... | ['Fariborz Taherkhani', 'Nasser M. Nasrabadi', 'Matthew C. Valenti', 'Veeru Talreja'] | 2020-04-03 | null | null | null | null | ['face-image-retrieval'] | ['computer-vision'] | [-1.25998586e-01 -7.30175823e-02 -3.33986908e-01 -3.18523288e-01
-1.29308581e+00 -3.19916070e-01 3.19740772e-01 4.10028577e-01
-4.87374097e-01 4.98329461e-01 9.95204151e-02 3.86376753e-02
1.87874705e-01 -1.00408781e+00 -8.68884981e-01 -8.71415257e-01
-4.10079181e-01 4.58895922e-01 2.65035570e-01 -2.73323916... | [11.39410400390625, 0.9332911372184753] |
f4546d00-20ab-4032-a4e1-3ff4afb94324 | planning-for-manipulation-among-movable | 2303.13385 | null | https://arxiv.org/abs/2303.13385v1 | https://arxiv.org/pdf/2303.13385v1.pdf | Planning for Manipulation among Movable Objects: Deciding Which Objects Go Where, in What Order, and How | We are interested in pick-and-place style robot manipulation tasks in cluttered and confined 3D workspaces among movable objects that may be rearranged by the robot and may slide, tilt, lean or topple. A recently proposed algorithm, M4M, determines which objects need to be moved and where by solving a Multi-Agent Pathf... | ['Maxim Likhachev', 'Dhruv Saxena'] | 2023-03-23 | null | null | null | null | ['robot-manipulation'] | ['robots'] | [ 2.58956254e-01 4.12990600e-01 1.05141602e-01 8.95389635e-03
-7.40492120e-02 -8.02297533e-01 4.68230605e-01 2.46099994e-01
-4.73856270e-01 9.50556338e-01 -3.80993307e-01 -5.79844296e-01
-9.36705351e-01 -7.75147855e-01 -8.54828775e-01 -3.93438548e-01
-5.65666795e-01 1.30649471e+00 6.92895234e-01 -6.99488938... | [4.901201248168945, 1.5267436504364014] |
cf07aeb5-ac81-431f-895c-c5947be43c91 | robust-importance-sampling-for-error | 2109.02150 | null | https://arxiv.org/abs/2109.02150v1 | https://arxiv.org/pdf/2109.02150v1.pdf | Robust Importance Sampling for Error Estimation in the Context of Optimal Bayesian Transfer Learning | Classification has been a major task for building intelligent systems as it enables decision-making under uncertainty. Classifier design aims at building models from training data for representing feature-label distributions--either explicitly or implicitly. In many scientific or clinical settings, training data are ty... | ['Byung-Jun Yoon', 'Edward R. Dougherty', 'Francis J. Alexander', 'Xiaoning Qian', 'Omar Maddouri'] | 2021-09-05 | null | null | null | null | ['decision-making-under-uncertainty', 'decision-making-under-uncertainty'] | ['medical', 'reasoning'] | [ 7.77811646e-01 8.63115937e-02 -4.57796782e-01 -8.10506642e-01
-1.46727467e+00 -2.95667410e-01 5.60886204e-01 4.32774633e-01
-4.47609991e-01 1.37560606e+00 -9.06487703e-02 -2.67702550e-01
-5.97870588e-01 -6.88063800e-01 -8.43982995e-01 -9.48828399e-01
3.10144052e-02 5.58698952e-01 -1.22142337e-01 5.25630713... | [8.355683326721191, 4.233211040496826] |
8ed69b9d-c89c-480b-929c-19a90cac413a | music-artist-classification-with-wavenet | 2004.04371 | null | https://arxiv.org/abs/2004.04371v3 | https://arxiv.org/pdf/2004.04371v3.pdf | MDCNN-SID: Multi-scale Dilated Convolution Network for Singer Identification | Most singer identification methods are processed in the frequency domain, which potentially leads to information loss during the spectral transformation. In this paper, instead of the frequency domain, we propose an end-to-end architecture that addresses this problem in the waveform domain. An encoder based on Multi-sc... | ['Jing Xiao', 'Ning Cheng', 'Jianzong Wang', 'xulong Zhang'] | 2020-04-09 | null | null | null | null | ['artist-classification', 'singer-identification'] | ['computer-vision', 'music'] | [ 3.37396055e-01 -3.11647683e-01 4.33897167e-01 -2.34409764e-01
-6.63977802e-01 -5.16577005e-01 1.13702506e-01 -3.91918749e-01
-6.26947045e-01 4.69123781e-01 2.39554048e-01 8.36217850e-02
-7.80727416e-02 -8.81492734e-01 -4.86354351e-01 -6.67568624e-01
-3.34411152e-02 -3.14825982e-01 6.36678329e-03 4.07187492... | [15.523578643798828, 5.454248428344727] |
336094ab-d57a-4784-8efe-3d607a7c3b7b | hitsz-hlt-at-semeval-2021-task-5-ensemble | null | null | https://aclanthology.org/2021.semeval-1.63 | https://aclanthology.org/2021.semeval-1.63.pdf | HITSZ-HLT at SemEval-2021 Task 5: Ensemble Sequence Labeling and Span Boundary Detection for Toxic Span Detection | This paper presents the winning system that participated in SemEval-2021 Task 5: Toxic Spans Detection. This task aims to locate those spans that attribute to the text{'}s toxicity within a text, which is crucial for semi-automated moderation in online discussions. We formalize this task as the Sequence Labeling (SL) p... | ['Ruifeng Xu', 'Yixue Dang', 'Qihui Lin', 'Xiang Li', 'Jingyi Sun', 'Yice Zhang', 'Zijie Lin', 'Qinglin Zhu'] | 2021-08-01 | null | null | null | semeval-2021 | ['boundary-detection', 'toxic-spans-detection'] | ['computer-vision', 'natural-language-processing'] | [ 2.44742811e-01 3.46229881e-01 -1.89062566e-01 4.19892371e-02
-1.17366529e+00 -8.09458852e-01 3.71721894e-01 4.12208050e-01
-1.33368343e-01 1.09774244e+00 7.68010318e-01 -4.88531500e-01
1.78022921e-01 -3.31636786e-01 -5.27437568e-01 -1.79143891e-01
1.13168590e-01 1.60215348e-01 2.09616750e-01 -1.30976290... | [8.948453903198242, 10.586723327636719] |
55a86eac-d191-47c4-a81d-6c608aaa2a7d | hierarchical-kickstarting-for-skill-transfer | 2207.11584 | null | https://arxiv.org/abs/2207.11584v2 | https://arxiv.org/pdf/2207.11584v2.pdf | Hierarchical Kickstarting for Skill Transfer in Reinforcement Learning | Practising and honing skills forms a fundamental component of how humans learn, yet artificial agents are rarely specifically trained to perform them. Instead, they are usually trained end-to-end, with the hope being that useful skills will be implicitly learned in order to maximise discounted return of some extrinsic ... | ['Tim Rocktäschel', 'Edward Grefenstette', 'Jack Parker-Holder', 'Mikayel Samvelyan', 'Michael Matthews'] | 2022-07-23 | null | null | null | null | ['nethack'] | ['playing-games'] | [ 2.92268574e-01 2.99539655e-01 -4.99549992e-02 -2.06271246e-01
-4.95799124e-01 -6.44733250e-01 7.95531392e-01 7.21375868e-02
-1.02876997e+00 1.14323354e+00 8.46676081e-02 -2.27821752e-01
-3.01537156e-01 -6.78276539e-01 -8.01097155e-01 -6.48812294e-01
-3.54389340e-01 7.46257842e-01 3.04203868e-01 -7.70150661... | [3.9997551441192627, 1.5518301725387573] |
eb52c411-cc28-4895-9e16-135a5f9a9185 | flexible-k-nearest-neighbors-classifier | 2304.10151 | null | https://arxiv.org/abs/2304.10151v1 | https://arxiv.org/pdf/2304.10151v1.pdf | Flexible K Nearest Neighbors Classifier: Derivation and Application for Ion-mobility Spectrometry-based Indoor Localization | The K Nearest Neighbors (KNN) classifier is widely used in many fields such as fingerprint-based localization or medicine. It determines the class membership of unlabelled sample based on the class memberships of the K labelled samples, the so-called nearest neighbors, that are closest to the unlabelled sample. The cho... | ['Philipp Müller'] | 2023-04-20 | null | null | null | null | ['indoor-localization'] | ['computer-vision'] | [ 3.87385875e-01 -4.15415376e-01 -5.03560960e-01 -4.78960276e-01
-3.85128379e-01 -6.15948021e-01 4.82225567e-01 4.37113613e-01
-5.23366868e-01 8.72594178e-01 -3.74172419e-01 -3.02603573e-01
-9.96735394e-01 -9.19737041e-01 -4.45585251e-01 -9.52903688e-01
-2.33016685e-01 5.82514584e-01 4.67766821e-01 8.43153372... | [8.216154098510742, 4.241058349609375] |
a4f208ce-731e-4440-92c0-7db4c4e30290 | rotation-invariant-deep-cbir | 2006.13046 | null | https://arxiv.org/abs/2006.13046v1 | https://arxiv.org/pdf/2006.13046v1.pdf | Rotation Invariant Deep CBIR | Introduction of Convolutional Neural Networks has improved results on almost every image-based problem and Content-Based Image Retrieval is not an exception. But the CNN features, being rotation invariant, creates problems to build a rotation-invariant CBIR system. Though rotation-invariant features can be hand-enginee... | ['Subhadip Maji', 'Smarajit Bose'] | 2020-06-21 | null | null | null | null | ['content-based-image-retrieval'] | ['computer-vision'] | [-3.67687911e-01 -7.54498065e-01 -1.19070649e-01 -3.23313594e-01
-6.35424376e-01 -5.28928459e-01 6.12444639e-01 2.30874444e-04
-4.41723436e-01 5.23713455e-02 4.30082753e-02 -7.12625235e-02
-5.46464980e-01 -1.15756261e+00 -4.76859957e-01 -7.56877542e-01
-7.81172439e-02 1.13709345e-01 3.22331339e-01 -8.56098413... | [10.600622177124023, 0.4080486297607422] |
7e19c716-50be-49d5-94fd-85ab5240ed80 | gradient-free-structured-pruning-with | 2303.04185 | null | https://arxiv.org/abs/2303.04185v1 | https://arxiv.org/pdf/2303.04185v1.pdf | Gradient-Free Structured Pruning with Unlabeled Data | Large Language Models (LLMs) have achieved great success in solving difficult tasks across many domains, but such success comes with a high computation cost, and inference latency. As developers and third parties customize these models, the need to provide efficient inference has increased. Many efforts have attempted ... | ['Dale Schuurmans', 'Hanjun Dai', 'Azade Nova'] | 2023-03-07 | null | null | null | null | ['model-compression'] | ['methodology'] | [ 1.06507115e-01 -6.30442202e-02 -1.55749902e-01 -6.18554592e-01
-7.90761769e-01 -3.82674009e-01 3.14914227e-01 5.30399084e-01
-7.33568668e-01 8.75171900e-01 -5.86000562e-01 -4.14608389e-01
2.99477160e-01 -7.55910099e-01 -7.67335176e-01 -2.81714618e-01
1.65708750e-01 7.04693317e-01 3.81124914e-01 9.00647193... | [8.630537033081055, 3.546825647354126] |
370b61ad-5bce-4901-ac64-326b66077cb6 | towards-trustworthy-energy-disaggregation-a | 2207.02009 | null | https://arxiv.org/abs/2207.02009v1 | https://arxiv.org/pdf/2207.02009v1.pdf | Towards trustworthy Energy Disaggregation: A review of challenges, methods and perspectives for Non-Intrusive Load Monitoring | Non-intrusive load monitoring (NILM) is the task of disaggregating the total power consumption into its individual sub-components. Over the years, signal processing and machine learning algorithms have been combined to achieve this. A lot of publications and extensive research works are performed on energy disaggregati... | ['Anastasios Doulamis', 'Nikolaos Doulamis', 'Athanasios Voulodimos', 'Eftychios Protopapadakis', 'Maria Kaselimi'] | 2022-07-05 | null | null | null | null | ['non-intrusive-load-monitoring', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring'] | ['knowledge-base', 'miscellaneous', 'time-series'] | [ 6.42924160e-02 -2.29423180e-01 -3.08040231e-01 -2.99810797e-01
-5.63106835e-01 -4.13476765e-01 5.04142225e-01 -1.21437661e-01
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-3.74604851e-01 -7.54601955e-01 -1.71607081e-02 -1.15374422e+00
-1.59096375e-01 2.11768195e-01 -3.42443228e-01 6.45129010... | [6.011253833770752, 2.595768451690674] |
b96e06de-bcdb-486a-977f-a8bbb6c80188 | permutation-models-for-collaborative-ranking | 1407.6128 | null | http://arxiv.org/abs/1407.6128v1 | http://arxiv.org/pdf/1407.6128v1.pdf | Permutation Models for Collaborative Ranking | We study the problem of collaborative filtering where ranking information is
available. Focusing on the core of the collaborative ranking process, the user
and their community, we propose new models for representation of the underlying
permutations and prediction of ranks. The first approach is based on the
assumption ... | ['Truyen Tran', 'Svetha Venkatesh'] | 2014-07-23 | null | null | null | null | ['collaborative-ranking'] | ['graphs'] | [ 1.68425873e-01 1.30849704e-01 -1.46385148e-01 -3.27556103e-01
-5.49188554e-01 -7.32179642e-01 8.49459350e-01 2.33173683e-01
-4.73618746e-01 5.73248446e-01 7.05353498e-01 -3.46837282e-01
-7.45559752e-01 -9.53560531e-01 -5.28493941e-01 -6.46461248e-01
-1.47617847e-01 9.79228556e-01 1.89663768e-01 -7.30310427... | [9.595611572265625, 5.506509304046631] |
9a2b4672-8eb6-4178-af15-a040b39ec5f7 | rigidity-aware-detection-for-6d-object-pose | 2303.12396 | null | https://arxiv.org/abs/2303.12396v1 | https://arxiv.org/pdf/2303.12396v1.pdf | Rigidity-Aware Detection for 6D Object Pose Estimation | Most recent 6D object pose estimation methods first use object detection to obtain 2D bounding boxes before actually regressing the pose. However, the general object detection methods they use are ill-suited to handle cluttered scenes, thus producing poor initialization to the subsequent pose network. To address this, ... | ['Yinlin Hu', 'Mathieu Salzmann', 'Jiaojiao Li', 'Rui Song', 'Yang Hai'] | 2023-03-22 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Hai_Rigidity-Aware_Detection_for_6D_Object_Pose_Estimation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Hai_Rigidity-Aware_Detection_for_6D_Object_Pose_Estimation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['6d-pose-estimation-1', '6d-pose-estimation'] | ['computer-vision', 'computer-vision'] | [ 2.05867335e-01 2.02539757e-01 -2.04012781e-01 -1.37640148e-01
-5.29721081e-01 -5.79078197e-01 4.62548494e-01 1.82494000e-01
-5.11822104e-01 2.11065277e-01 -7.48566836e-02 8.27246308e-02
1.75798729e-01 -6.06751025e-01 -1.01883709e+00 -5.55568933e-01
-2.36236826e-02 9.07395422e-01 7.69627571e-01 -8.37361738... | [7.5348968505859375, -2.6413209438323975] |
d12818e8-a70c-4880-b5d1-4c6d78923572 | lymph-node-gross-tumor-volume-detection-in | 2008.13013 | null | https://arxiv.org/abs/2008.13013v1 | https://arxiv.org/pdf/2008.13013v1.pdf | Lymph Node Gross Tumor Volume Detection in Oncology Imaging via Relationship Learning Using Graph Neural Network | Determining the spread of GTV$_{LN}$ is essential in defining the respective resection or irradiating regions for the downstream workflows of surgical resection and radiotherapy for many cancers. Different from the more common enlarged lymph node (LN), GTV$_{LN}$ also includes smaller ones if associated with high posit... | ['Tsung-Ying Ho', 'Chun-Hung Chao', 'Ke Yan', 'Xianghua Ye', 'Min Sun', 'Le Lu', 'Jinzheng Cai', 'Jing Xiao', 'Zhuotun Zhu', 'Dazhou Guo', 'Dakai Jin', 'Alan Yuille', 'Adam P. Harrison'] | 2020-08-29 | null | null | null | null | ['clinical-knowledge'] | ['miscellaneous'] | [ 1.83105543e-01 4.30870980e-01 -4.80869651e-01 -7.48878196e-02
-1.18557525e+00 -5.50862491e-01 2.77958155e-01 4.21583295e-01
-5.90167940e-01 6.07761979e-01 -1.25102267e-01 -8.37494373e-01
-3.77859384e-01 -1.07507265e+00 -6.04456961e-01 -9.83859479e-01
-1.48396343e-01 3.16397429e-01 2.73663074e-01 5.81409112... | [14.727534294128418, -2.573491096496582] |
ad8d45c0-e4bd-48dc-8728-90a472747973 | advmil-adversarial-multiple-instance-learning | 2212.06515 | null | https://arxiv.org/abs/2212.06515v2 | https://arxiv.org/pdf/2212.06515v2.pdf | AdvMIL: Adversarial Multiple Instance Learning for the Survival Analysis on Whole-Slide Images | The survival analysis on histological whole-slide images (WSIs) is one of the most important means to estimate patient prognosis. Although many weakly-supervised deep learning models have been developed for gigapixel WSIs, their potential is generally restricted by classical survival analysis rules and fully-supervised... | ['Bo Fu', 'Feng Ye', 'Luping Ji', 'Pei Liu'] | 2022-12-13 | null | null | null | null | ['multiple-instance-learning', 'survival-analysis'] | ['methodology', 'miscellaneous'] | [ 1.03627786e-01 6.98874444e-02 -4.52451169e-01 -3.46531242e-01
-1.37084663e+00 -2.72272438e-01 2.66959339e-01 2.98197001e-01
-3.49655002e-01 8.38261724e-01 6.99126497e-02 -3.03461015e-01
-2.59224087e-01 -8.81420314e-01 -4.68336880e-01 -1.39789319e+00
-9.99747664e-02 5.49792230e-01 1.60543844e-01 -1.72850356... | [15.119588851928711, -2.786731481552124] |
0f6d5226-dde9-42ec-a56a-203fd0437272 | leno-adversarial-robust-salient-object | 2210.15392 | null | https://arxiv.org/abs/2210.15392v2 | https://arxiv.org/pdf/2210.15392v2.pdf | LeNo: Adversarial Robust Salient Object Detection Networks with Learnable Noise | Pixel-wise prediction with deep neural network has become an effective paradigm for salient object detection (SOD) and achieved remarkable performance. However, very few SOD models are robust against adversarial attacks which are visually imperceptible for human visual attention. The previous work robust saliency (ROSA... | ['Lin Wan', 'He Wang', 'He Tang'] | 2022-10-27 | null | null | null | null | ['superpixels', 'noise-estimation'] | ['computer-vision', 'medical'] | [ 1.76647782e-01 5.33353761e-02 2.18860582e-01 -8.60318840e-02
-6.89900041e-01 -5.87193251e-01 4.05294955e-01 -2.48349547e-01
-6.40027344e-01 6.03630960e-01 2.38399893e-01 -2.89410353e-01
3.80714327e-01 -7.63882399e-01 -1.04375792e+00 -8.47702742e-01
3.74503314e-01 -2.94849306e-01 8.14236403e-01 -3.71647179... | [5.516396999359131, 7.96879768371582] |
8b7000f2-5598-49a7-8052-8b2d4aa71aa0 | fully-convolutional-networks-for-panoptic-1 | 2108.07682 | null | https://arxiv.org/abs/2108.07682v3 | https://arxiv.org/pdf/2108.07682v3.pdf | Fully Convolutional Networks for Panoptic Segmentation with Point-based Supervision | In this paper, we present a conceptually simple, strong, and efficient framework for fully- and weakly-supervised panoptic segmentation, called Panoptic FCN. Our approach aims to represent and predict foreground things and background stuff in a unified fully convolutional pipeline, which can be optimized with point-bas... | ['Jiaya Jia', 'Jian Sun', 'Zeming Li', 'LiWei Wang', 'Lu Qi', 'Yukang Chen', 'Xiaojuan Qi', 'Hengshuang Zhao', 'Yanwei Li'] | 2021-08-17 | null | null | null | null | ['weakly-supervised-panoptic-segmentation'] | ['computer-vision'] | [-2.02775806e-01 -1.41924292e-01 -2.87283570e-01 -5.50258040e-01
-7.31028497e-01 -7.27903664e-01 6.31675661e-01 -1.67539537e-01
-1.28479972e-02 2.97246218e-01 -3.49978089e-01 -5.36854640e-02
-3.57899372e-03 -9.89332080e-01 -9.52823460e-01 -8.82962346e-01
1.82900816e-01 8.45406890e-01 7.02677369e-01 3.60395908... | [9.47311019897461, 0.23263493180274963] |
80cbe5e5-a347-4acd-bb28-e4849bf8283a | kuairec-a-fully-observed-dataset-for | 2202.10842 | null | https://arxiv.org/abs/2202.10842v3 | https://arxiv.org/pdf/2202.10842v3.pdf | KuaiRec: A Fully-observed Dataset and Insights for Evaluating Recommender Systems | The progress of recommender systems is hampered mainly by evaluation as it requires real-time interactions between humans and systems, which is too laborious and expensive. This issue is usually approached by utilizing the interaction history to conduct offline evaluation. However, existing datasets of user-item intera... | ['Biao Li', 'Jiawei Chen', 'Tat-Seng Chua', 'Jiaxin Mao', 'Xiangnan He', 'Peng Jiang', 'Wenqiang Lei', 'Shijun Li', 'Chongming Gao'] | 2022-02-22 | null | null | null | null | ['user-simulation'] | ['natural-language-processing'] | [-2.52960891e-01 -2.42895886e-01 -1.26746178e-01 -4.64160353e-01
-3.08651865e-01 -9.22888994e-01 6.19683206e-01 -2.14729868e-02
-4.43920732e-01 5.54373324e-01 4.35317963e-01 -5.09736061e-01
-1.61769778e-01 -5.42298794e-01 -7.49265790e-01 -2.11251795e-01
-2.46226355e-01 5.06917059e-01 2.89678331e-02 -3.15918416... | [10.286866188049316, 5.901273727416992] |
e89ea8d9-e23c-478e-bb72-4730173ba2c4 | dialog-system-using-real-time-crowdsourcing | null | null | https://aclanthology.org/W12-1631 | https://aclanthology.org/W12-1631.pdf | Dialog System Using Real-Time Crowdsourcing and Twitter Large-Scale Corpus | null | ['Yasuo Kuniyoshi', 'Fumihiro Bessho', 'Tatsuya Harada'] | 2012-07-01 | null | null | null | ws-2012-7 | ['stock-market-prediction'] | ['time-series'] | [-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... | [-7.27446174621582, 3.504033327102661] |
cf9562e9-b0bd-4852-9679-305e4ca6c644 | discretize-optimize-vs-optimize-discretize | 2005.13420 | null | https://arxiv.org/abs/2005.13420v2 | https://arxiv.org/pdf/2005.13420v2.pdf | Discretize-Optimize vs. Optimize-Discretize for Time-Series Regression and Continuous Normalizing Flows | We compare the discretize-optimize (Disc-Opt) and optimize-discretize (Opt-Disc) approaches for time-series regression and continuous normalizing flows (CNFs) using neural ODEs. Neural ODEs are ordinary differential equations (ODEs) with neural network components. Training a neural ODE is an optimal control problem whe... | ['Lars Ruthotto', 'Derek Onken'] | 2020-05-27 | null | null | null | null | ['time-series-regression'] | ['time-series'] | [-6.16743676e-02 -1.27343625e-01 2.75380425e-02 -4.47070338e-02
-2.39823654e-01 -5.67199469e-01 2.29002506e-01 -2.10404769e-01
-5.44532120e-01 9.01036561e-01 -3.59769195e-01 -8.33813190e-01
-3.07878196e-01 -4.08370376e-01 -4.94592965e-01 -6.12195969e-01
-3.57289910e-01 2.83497311e-02 -1.22115478e-01 -1.58176661... | [6.820402145385742, 3.50783371925354] |
254c31c4-3616-403e-a56e-9aa82e0210cb | characterizing-and-predicting-repeat-food | 1909.07683 | null | https://arxiv.org/abs/1909.07683v1 | https://arxiv.org/pdf/1909.07683v1.pdf | Characterizing and Predicting Repeat Food Consumption Behavior for Just-in-Time Interventions | Human beings are creatures of habit. In their daily life, people tend to repeatedly consume similar types of food items over several days and occasionally switch to consuming different types of items when the consumptions become overly monotonous. However, the novel and repeat consumption behaviors have not been studie... | ['Shou-De Lin', 'Tzu-Ling Cheng', 'Ee-Peng Lim', 'Helena Lee', 'Yue Liu', 'Palakorn Achananuparp'] | 2019-09-17 | characterizing-and-predicting-repeat-food-1 | https://arxiv.org/abs/1909.07683 | https://arxiv.org/pdf/1909.07683.pdf | null | ['food-recommendation'] | ['miscellaneous'] | [-2.90714443e-01 -3.91557485e-01 -8.13448370e-01 -4.41002041e-01
2.48918921e-01 -3.73803169e-01 -1.90799944e-02 6.93909764e-01
-1.54845864e-01 3.31020176e-01 6.22970045e-01 -7.09184855e-02
-2.74109393e-01 -1.08378100e+00 -3.99913877e-01 -4.62688953e-01
-4.79840875e-01 1.09322391e-01 7.65634924e-02 -4.14153665... | [11.543726921081543, 4.486254692077637] |
f9ad3c8d-b069-46f6-8e5f-af585059cfe4 | joint-stereo-video-deblurring-scene-flow | 1910.02442 | null | https://arxiv.org/abs/1910.02442v1 | https://arxiv.org/pdf/1910.02442v1.pdf | Joint Stereo Video Deblurring, Scene Flow Estimation and Moving Object Segmentation | Stereo videos for the dynamic scenes often show unpleasant blurred effects due to the camera motion and the multiple moving objects with large depth variations. Given consecutive blurred stereo video frames, we aim to recover the latent clean images, estimate the 3D scene flow and segment the multiple moving objects. T... | ['Quan Pan', 'Miaomiao Liu', 'Fatih Porikli', 'Liyuan Pan', 'Yuchao Dai'] | 2019-10-06 | null | null | null | null | ['scene-flow-estimation'] | ['computer-vision'] | [ 2.60902315e-01 -5.10425687e-01 1.21084303e-01 2.98089385e-02
-2.60229766e-01 -7.10751593e-01 5.26368618e-01 -9.08929706e-01
-1.69254124e-01 7.02963114e-01 4.73715097e-01 9.84802693e-02
-2.35706866e-02 -1.65628195e-01 -7.31352627e-01 -8.60350192e-01
2.04102039e-01 -3.42894979e-02 3.89809966e-01 3.46330136... | [11.361677169799805, -2.44521164894104] |
ab7cb2b8-db9e-4c91-8d84-0910ff082a6e | c2slr-consistency-enhanced-continuous-sign | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Zuo_C2SLR_Consistency-Enhanced_Continuous_Sign_Language_Recognition_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Zuo_C2SLR_Consistency-Enhanced_Continuous_Sign_Language_Recognition_CVPR_2022_paper.pdf | C2SLR: Consistency-Enhanced Continuous Sign Language Recognition | The backbone of most deep-learning-based continuous sign language recognition (CSLR) models consists of a visual module, a sequential module, and an alignment module. However, such CSLR backbones are hard to be trained sufficiently with a single connectionist temporal classification loss. In this work, we propose t... | ['Brian Mak', 'Ronglai Zuo'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['sign-language-recognition'] | ['computer-vision'] | [-3.95451905e-03 -2.64410004e-02 -2.48473659e-01 -4.47493017e-01
-4.80427265e-01 -2.44548962e-01 5.47587156e-01 -4.79847640e-01
-5.55632889e-01 4.50652391e-01 4.92812872e-01 -6.17190674e-02
-1.05335135e-02 -3.51515681e-01 -7.01312482e-01 -8.37283492e-01
2.03430086e-01 -1.18696570e-01 3.60558450e-01 -4.54543941... | [9.214446067810059, -6.505614757537842] |
d2147fb4-abe1-41a2-a8a4-8fb763930303 | bridging-cross-lingual-gaps-during-leveraging | 2204.07834 | null | https://arxiv.org/abs/2204.07834v2 | https://arxiv.org/pdf/2204.07834v2.pdf | Bridging Cross-Lingual Gaps During Leveraging the Multilingual Sequence-to-Sequence Pretraining for Text Generation and Understanding | For multilingual sequence-to-sequence pretrained language models (multilingual Seq2Seq PLMs), e.g. mBART, the self-supervised pretraining task is trained on a wide range of monolingual languages, e.g. 25 languages from CommonCrawl, while the downstream cross-lingual tasks generally progress on a bilingual language subs... | ['DaCheng Tao', 'Weifeng Liu', 'Yu Cao', 'Li Shen', 'Liang Ding', 'Changtong Zan'] | 2022-04-16 | null | null | null | null | ['cross-lingual-natural-language-inference'] | ['natural-language-processing'] | [ 2.16622204e-01 -1.59338355e-01 -5.63093662e-01 -4.53566551e-01
-1.54024136e+00 -1.00769103e+00 4.40447450e-01 -1.81896135e-01
-5.17062068e-01 1.13205910e+00 5.04285932e-01 -7.69251645e-01
4.24790174e-01 -3.22251916e-01 -1.11339641e+00 -4.20405835e-01
4.32347238e-01 8.10789227e-01 -2.41572127e-01 -5.21151781... | [11.561408042907715, 10.140364646911621] |
ff4c77dd-166a-46d0-a2ab-7ed0fcb0e3a5 | efficient-gradient-approximation-method-for | 2302.01970 | null | https://arxiv.org/abs/2302.01970v1 | https://arxiv.org/pdf/2302.01970v1.pdf | Efficient Gradient Approximation Method for Constrained Bilevel Optimization | Bilevel optimization has been developed for many machine learning tasks with large-scale and high-dimensional data. This paper considers a constrained bilevel optimization problem, where the lower-level optimization problem is convex with equality and inequality constraints and the upper-level optimization problem is n... | ['Minghui Zhu', 'Siyuan Xu'] | 2023-02-03 | null | null | null | null | ['bilevel-optimization'] | ['methodology'] | [-5.45317054e-01 -9.32649598e-02 -4.20112342e-01 -2.15748444e-01
-1.05240190e+00 -4.04878289e-01 8.31286684e-02 2.82064267e-02
-4.07454371e-01 9.06074643e-01 2.86983047e-02 -3.52099210e-01
-6.03923202e-01 -4.00919348e-01 -7.97930002e-01 -9.28874195e-01
-3.73320878e-01 6.14970922e-01 -1.89466029e-01 -1.31918237... | [6.739006996154785, 4.265745639801025] |
d4fdfe76-746c-4b85-9a81-59ed80116c77 | molecular-insights-from-conformational | null | null | https://www.cell.com/biophysj/fulltext/S0006-3495(19)34401-7 | https://www.cell.com/biophysj/pdfExtended/S0006-3495(19)34401-7 | Molecular Insights from Conformational Ensembles via Machine Learning | Biomolecular simulations are intrinsically high dimensional and generate noisy data sets of ever-increasing size. Extracting important features from the data is crucial for understanding the biophysical properties of molecular processes, but remains a big challenge. Machine learning (ML) provides powerful dimensionalit... | ['Fleetwood O', 'Kasimova MA', 'Delemotte L', 'Westerlund AM'] | 2020-02-04 | null | null | null | biophys-journal-2020-2 | ['physical-simulations'] | ['miscellaneous'] | [ 5.63060820e-01 -4.05794442e-01 -1.90933600e-01 -3.10901970e-01
-7.13299096e-01 -7.34279633e-01 4.52944398e-01 4.82750028e-01
-5.38602412e-01 1.34126806e+00 2.05800682e-01 -7.82535374e-01
-6.24492019e-02 -5.02850950e-01 -7.12618291e-01 -1.27623451e+00
-3.43775630e-01 7.86834240e-01 -7.28577003e-02 -6.61779270... | [4.828286170959473, 5.364819526672363] |
e04c2838-ba2f-4699-b062-8971652ff27f | deep-learning-for-conversational-ai | null | null | https://aclanthology.org/N18-6006 | https://aclanthology.org/N18-6006.pdf | Deep Learning for Conversational AI | Spoken Dialogue Systems (SDS) have great commercial potential as they promise to revolutionise the way in which humans interact with machines. The advent of deep learning led to substantial developments in this area of NLP research, and the goal of this tutorial is to familiarise the research community with the recent ... | ["Ivan Vuli{\\'c}", 'I{\\~n}igo Casanueva', "Nikola Mrk{\\v{s}}i{\\'c}", 'Pei-Hao Su'] | 2018-06-01 | null | null | null | naacl-2018-6 | ['goal-oriented-dialogue-systems'] | ['natural-language-processing'] | [ 9.41050947e-02 6.65488482e-01 -1.24427071e-02 -5.00805855e-01
-6.00300848e-01 -8.80891383e-01 9.68712986e-01 -1.73582003e-01
-1.06211424e-01 8.56712699e-01 4.11459774e-01 -3.64396155e-01
-2.35901568e-02 -6.31065309e-01 5.92440106e-02 -3.48158330e-01
-4.62800404e-03 9.61326241e-01 -5.51018715e-02 -9.06684339... | [12.876784324645996, 7.9445719718933105] |
5819425c-e5ab-4691-90f7-3dcdb9f95e69 | that-is-a-suspicious-reaction-interpreting-1 | 2204.04636 | null | https://arxiv.org/abs/2204.04636v2 | https://arxiv.org/pdf/2204.04636v2.pdf | "That Is a Suspicious Reaction!": Interpreting Logits Variation to Detect NLP Adversarial Attacks | Adversarial attacks are a major challenge faced by current machine learning research. These purposely crafted inputs fool even the most advanced models, precluding their deployment in safety-critical applications. Extensive research in computer vision has been carried to develop reliable defense strategies. However, th... | ['Javier Rando', 'Georg Groh', 'Shreyash Agarwal', 'Edoardo Mosca'] | 2022-04-10 | null | null | null | null | ['adversarial-text'] | ['adversarial'] | [ 4.51701254e-01 -1.45176351e-01 -2.14413881e-01 -1.61840826e-01
-7.56469429e-01 -1.26829183e+00 1.16420555e+00 2.19772682e-01
-5.51563919e-01 4.26780492e-01 -9.60960388e-02 -7.87640989e-01
1.32569507e-01 -6.67083442e-01 -6.46464288e-01 -7.71011472e-01
-4.05264795e-02 2.45096922e-01 4.14388537e-01 -4.35976416... | [5.8391432762146, 7.95525598526001] |
c6cccb58-005d-4b8e-b381-d40b916b6d70 | nasgem-neural-architecture-search-via-graph | 2007.04452 | null | https://arxiv.org/abs/2007.04452v2 | https://arxiv.org/pdf/2007.04452v2.pdf | NASGEM: Neural Architecture Search via Graph Embedding Method | Neural Architecture Search (NAS) automates and prospers the design of neural networks. Estimator-based NAS has been proposed recently to model the relationship between architectures and their performance to enable scalable and flexible search. However, existing estimator-based methods encode the architecture into a lat... | ['Shi-Yu Li', 'Feng Liang', 'Hsin-Pai Cheng', 'Yiran Chen', 'Vikas Chandra', 'Yixing Zhang', 'Meng Li', 'Tunhou Zhang', 'Hai Li', 'Feng Yan'] | 2020-07-08 | null | null | null | null | ['graph-similarity'] | ['graphs'] | [-1.28354847e-01 9.14191529e-02 -3.38675767e-01 -1.44855082e-01
-4.50636059e-01 -6.36010826e-01 4.06880945e-01 8.74709745e-04
-2.47984037e-01 3.92967016e-01 4.70845215e-02 -2.77644306e-01
-4.16207761e-01 -9.80067372e-01 -5.33444524e-01 -5.93460739e-01
-8.47516060e-02 6.07687950e-01 2.57134467e-01 -8.87585208... | [8.681675910949707, 3.4238815307617188] |
8e246e1d-aae5-4a59-b2c8-bb188bad950a | detection-of-network-and-sensor-cyber-attacks | 2104.03798 | null | https://arxiv.org/abs/2104.03798v1 | https://arxiv.org/pdf/2104.03798v1.pdf | Detection of Network and Sensor Cyber-Attacks in Platoons of Cooperative Autonomous Vehicles: a Sliding-Mode Observer Approach | Platoons of autonomous vehicles are being investigated as a way to increase road capacity and fuel efficiency. Cooperative Adaptive Cruise Control (CACC) is an approach to achieve such platoons, in which vehicles collaborate using wireless communication. While this collaboration improves performance, it also makes the ... | ['Riccardo M. G. Ferrari', 'Twan Keijzer'] | 2021-04-08 | null | null | null | null | ['cyber-attack-detection'] | ['miscellaneous'] | [ 1.23555712e-01 7.26183116e-01 -2.25496650e-01 1.19267434e-01
-2.11492050e-02 -6.04000688e-01 1.15754461e+00 2.66749144e-01
-5.01223207e-01 8.40920806e-01 -5.73312342e-01 -7.52185881e-01
-3.29429984e-01 -1.05381823e+00 -5.98374069e-01 -1.02842593e+00
-5.77380836e-01 -7.82664269e-02 8.37864876e-01 -4.22157586... | [5.562346935272217, 1.5953603982925415] |
cd04f981-260f-49ce-8936-160f17afcb90 | self-supervised-learning-with-swin | 2105.04553 | null | https://arxiv.org/abs/2105.04553v2 | https://arxiv.org/pdf/2105.04553v2.pdf | Self-Supervised Learning with Swin Transformers | We are witnessing a modeling shift from CNN to Transformers in computer vision. In this work, we present a self-supervised learning approach called MoBY, with Vision Transformers as its backbone architecture. The approach basically has no new inventions, which is combined from MoCo v2 and BYOL and tuned to achieve reas... | ['Han Hu', 'Yue Cao', 'Qi Dai', 'Zheng Zhang', 'Zhuliang Yao', 'Yutong Lin', 'Zhenda Xie'] | 2021-05-10 | null | null | null | null | ['self-supervised-image-classification'] | ['computer-vision'] | [ 6.96339458e-02 2.20617607e-01 -3.33108276e-01 -4.09074277e-01
-6.37697875e-01 -6.42784119e-01 6.38149261e-01 -4.94962186e-01
-5.49667954e-01 3.42382073e-01 -9.77726281e-02 -5.84386289e-01
1.77814573e-01 -5.85305631e-01 -9.63325083e-01 -5.50697207e-01
2.87717879e-01 4.86302108e-01 6.20301425e-01 -2.20705003... | [9.532471656799316, 1.4110227823257446] |
d6de7847-0516-4636-954e-cb11f65734de | ultra-fine-entity-typing-with-prior-knowledge | 2305.12802 | null | https://arxiv.org/abs/2305.12802v1 | https://arxiv.org/pdf/2305.12802v1.pdf | Ultra-Fine Entity Typing with Prior Knowledge about Labels: A Simple Clustering Based Strategy | Ultra-fine entity typing (UFET) is the task of inferring the semantic types, from a large set of fine-grained candidates, that apply to a given entity mention. This task is especially challenging because we only have a small number of training examples for many of the types, even with distant supervision strategies. St... | ['Steven Schockaert', 'Zied Bouraoui', 'Na Li'] | 2023-05-22 | null | null | null | null | ['entity-typing'] | ['natural-language-processing'] | [ 7.98773170e-02 2.40155205e-01 -4.20193732e-01 -5.29479325e-01
-7.06019580e-01 -8.02924216e-01 8.28979194e-01 5.62912703e-01
-6.68011844e-01 7.15454102e-01 8.83606076e-02 -2.36286253e-01
7.09163025e-02 -7.81681120e-01 -7.56499708e-01 -3.80670071e-01
-3.08022629e-02 7.87614524e-01 5.74897647e-01 -1.03677094... | [9.631072044372559, 8.954218864440918] |
be4770a8-6bd7-4c9d-8f48-682670c2a949 | in-context-learning-through-the-bayesian | 2306.04891 | null | https://arxiv.org/abs/2306.04891v1 | https://arxiv.org/pdf/2306.04891v1.pdf | In-Context Learning through the Bayesian Prism | In-context learning is one of the surprising and useful features of large language models. How it works is an active area of research. Recently, stylized meta-learning-like setups have been devised that train these models on a sequence of input-output pairs $(x, f(x))$ from a function class using the language modeling ... | ['Navin Goyal', 'Madhur Panwar', 'Kabir Ahuja'] | 2023-06-08 | null | null | null | null | ['meta-learning'] | ['methodology'] | [ 2.23609626e-01 8.13457891e-02 -1.87881365e-01 -6.11022711e-01
-9.52850044e-01 -6.57292664e-01 7.05560446e-01 5.64131550e-02
-4.50224638e-01 6.16044641e-01 -1.21141151e-01 -5.88845551e-01
-3.32480133e-01 -7.98015475e-01 -1.27708924e+00 -1.00661349e+00
-3.10259968e-01 4.12309855e-01 1.75793134e-02 -2.98047632... | [10.432954788208008, 7.873956203460693] |
87ac4826-7127-4b62-8e50-3e4df9471f52 | morphological-analysis-without-expert | null | null | https://aclanthology.org/E17-2034 | https://aclanthology.org/E17-2034.pdf | Morphological Analysis without Expert Annotation | The task of morphological analysis is to produce a complete list of lemma+tag analyses for a given word-form. We propose a discriminative string transduction approach which exploits plain inflection tables and raw text corpora, thus obviating the need for expert annotation. Experiments on four languages demonstrate tha... | ['Garrett Nicolai', 'Grzegorz Kondrak'] | 2017-04-01 | null | null | null | eacl-2017-4 | ['morphological-tagging'] | ['natural-language-processing'] | [ 5.09439409e-01 -5.86844459e-02 -1.34785905e-01 -1.13112599e-01
-1.18557811e+00 -1.19771290e+00 1.58963218e-01 7.06594110e-01
-6.08748436e-01 7.03250825e-01 1.59048647e-01 -1.01058149e+00
3.99864376e-01 -9.37682569e-01 -3.50024492e-01 -2.07823247e-01
3.11719805e-01 3.69296521e-01 6.15580857e-01 -3.66636246... | [10.442625999450684, 10.10018539428711] |
107006b8-cada-488f-bbf1-200ef34ffe64 | nonlinear-distributional-gradient-temporal | 1805.07732 | null | http://arxiv.org/abs/1805.07732v3 | http://arxiv.org/pdf/1805.07732v3.pdf | Nonlinear Distributional Gradient Temporal-Difference Learning | We devise a distributional variant of gradient temporal-difference (TD)
learning. Distributional reinforcement learning has been demonstrated to
outperform the regular one in the recent study
\citep{bellemare2017distributional}. In the policy evaluation setting, we
design two new algorithms called distributional GTD2 a... | ['Chao Qu', 'Shie Mannor', 'Huan Xu'] | 2018-05-20 | null | null | null | null | ['distributional-reinforcement-learning'] | ['methodology'] | [-3.13215196e-01 3.72279212e-02 -3.13704282e-01 -2.45548651e-01
-1.17567861e+00 -3.96124750e-01 2.50169307e-01 -9.47116837e-02
-1.20005488e+00 1.22267520e+00 3.18399817e-02 -7.12632537e-01
-4.91370082e-01 -4.65832323e-01 -8.05183291e-01 -1.25113404e+00
-2.57471412e-01 3.95939916e-01 -2.07697093e-01 -3.36351469... | [4.170379638671875, 2.4909822940826416] |
cf355972-2816-44c9-a095-392619373979 | matching-objects-across-the-textured-smooth | 1306.3297 | null | http://arxiv.org/abs/1306.3297v1 | http://arxiv.org/pdf/1306.3297v1.pdf | Matching objects across the textured-smooth continuum | The problem of 3D object recognition is of immense practical importance, with
the last decade witnessing a number of breakthroughs in the state of the art.
Most of the previous work has focused on the matching of textured objects using
local appearance descriptors extracted around salient image points. The
recently pro... | ['Ognjen Arandjelovic'] | 2013-06-14 | null | null | null | null | ['3d-object-recognition'] | ['computer-vision'] | [ 4.23402280e-01 -2.49396786e-01 -5.06953076e-02 -6.02340281e-01
-9.71916735e-01 -5.37674725e-01 1.01717603e+00 4.68789160e-01
-1.54641837e-01 4.28006612e-02 1.98121414e-01 2.82310367e-01
-4.42206800e-01 -5.48185825e-01 -4.19509977e-01 -7.96400189e-01
1.27267644e-01 5.50366700e-01 3.13447386e-01 -1.76645249... | [8.196479797363281, -2.1944639682769775] |
e4ac69d1-6721-4869-9df8-c18b81887ed6 | real-time-semantic-segmentation-via-multiply | 1911.07217 | null | https://arxiv.org/abs/1911.07217v1 | https://arxiv.org/pdf/1911.07217v1.pdf | Real-Time Semantic Segmentation via Multiply Spatial Fusion Network | Real-time semantic segmentation plays a significant role in industry applications, such as autonomous driving, robotics and so on. It is a challenging task as both efficiency and performance need to be considered simultaneously. To address such a complex task, this paper proposes an efficient CNN called Multiply Spatia... | ['Feng Lu', 'Zhiqiang Zhang', 'Haiyang Si', 'Gang Yu', 'Feifan Lv'] | 2019-11-17 | null | null | null | null | ['2048'] | ['playing-games'] | [ 1.39627293e-01 -3.90312374e-01 1.28031686e-01 -4.23171520e-01
-3.84062827e-01 -1.44666016e-01 3.72039497e-01 1.23537406e-01
-9.19872701e-01 5.18099666e-01 -2.58271694e-01 -3.72117311e-01
-4.60405536e-02 -8.97353888e-01 -6.46604836e-01 -6.73093200e-01
3.00595373e-01 4.53891493e-02 8.10929358e-01 -1.24569662... | [9.289752006530762, -0.5834775567054749] |
941c1b00-937a-4bbb-bab3-ee49799f6caf | intent-discovery-for-enterprise-virtual | null | null | https://aclanthology.org/2022.naacl-industry.23 | https://aclanthology.org/2022.naacl-industry.23.pdf | Intent Discovery for Enterprise Virtual Assistants: Applications of Utterance Embedding and Clustering to Intent Mining | A key challenge in the creation and refinement of virtual assistants is the ability to mine unlabeled utterance data to discover common intents. We develop an approach to this problem that combines large-scale pre-training and multi-task learning to derive a semantic embedding that can be leveraged to identify clusters... | ['Daniel Pressel', 'S. Eman Mahmoodi', 'Michael Johnston', 'Badrinath Jayakumar', 'Minhua Chen'] | null | null | null | null | naacl-acl-2022-7 | ['intent-discovery'] | ['natural-language-processing'] | [ 3.05882394e-01 6.58526421e-01 7.72631019e-02 -9.77707982e-01
-1.02472365e+00 -5.69949806e-01 6.90776706e-01 3.87854993e-01
-3.79981816e-01 3.63498837e-01 7.93113589e-01 -5.59983194e-01
-5.59160151e-02 -2.17860863e-01 -2.60482371e-01 -1.02103487e-01
-4.40940894e-02 8.22166562e-01 -5.01617715e-02 -4.32026356... | [12.470476150512695, 7.59801721572876] |
f27b59f5-bc14-40a5-b725-0693243a49d5 | mirror-matching-document-matching-approach-in | 2112.14318 | null | https://arxiv.org/abs/2112.14318v1 | https://arxiv.org/pdf/2112.14318v1.pdf | Mirror Matching: Document Matching Approach in Seed-driven Document Ranking for Medical Systematic Reviews | When medical researchers conduct a systematic review (SR), screening studies is the most time-consuming process: researchers read several thousands of medical literature and manually label them relevant or irrelevant. Screening prioritization (ie., document ranking) is an approach for assisting researchers by providing... | ['Aixin Sun', 'Grace E. Lee'] | 2021-12-28 | null | null | null | null | ['document-ranking'] | ['natural-language-processing'] | [ 6.68541193e-01 -3.52419764e-01 -6.39885783e-01 -3.68880630e-01
-1.28385854e+00 -4.16083783e-01 5.87313533e-01 8.55918944e-01
-7.09758341e-01 6.08190835e-01 8.35809708e-01 -2.91291535e-01
-7.98562229e-01 -5.94491124e-01 -1.01638265e-01 -3.12088817e-01
2.29355752e-01 7.04714596e-01 3.11828852e-01 -5.95388450... | [8.785757064819336, 8.522794723510742] |
9890fb02-bd5e-4d30-a081-51d0de3f25a2 | multi-head-temporal-attention-augmented | 2201.05459 | null | https://arxiv.org/abs/2201.05459v1 | https://arxiv.org/pdf/2201.05459v1.pdf | Multi-head Temporal Attention-Augmented Bilinear Network for Financial time series prediction | Financial time-series forecasting is one of the most challenging domains in the field of time-series analysis. This is mostly due to the highly non-stationary and noisy nature of financial time-series data. With progressive efforts of the community to design specialized neural networks incorporating prior domain knowle... | ['Alexandros Iosifidis', 'Juho Kanniainen', 'Martin Magris', 'Dat Thanh Tran', 'Mostafa Shabani'] | 2022-01-14 | null | null | null | null | ['time-series-prediction'] | ['time-series'] | [-2.72110641e-01 -3.17286372e-01 -9.13681984e-02 -4.61787611e-01
-3.55729401e-01 -3.89462918e-01 8.28741789e-01 -2.16473378e-02
-2.94826448e-01 4.43288505e-01 4.23368603e-01 -4.02282625e-01
-2.47683704e-01 -5.59814513e-01 -4.77526218e-01 -4.07135755e-01
-3.50190639e-01 1.77381560e-01 1.99838236e-01 -3.89501691... | [6.7996416091918945, 3.0353660583496094] |
1ae6c1c9-f021-43ae-9c4f-79a5de5190a0 | context-dependent-diffusion-network-for | 1809.06213 | null | http://arxiv.org/abs/1809.06213v1 | http://arxiv.org/pdf/1809.06213v1.pdf | Context-Dependent Diffusion Network for Visual Relationship Detection | Visual relationship detection can bridge the gap between computer vision and
natural language for scene understanding of images. Different from pure object
recognition tasks, the relation triplets of subject-predicate-object lie on an
extreme diversity space, such as \textit{person-behind-person} and
\textit{car-behind... | ['Jian Yang', 'Zhen Cui', 'Wenming Zheng', 'Chunyan Xu'] | 2018-09-11 | null | null | null | null | ['visual-relationship-detection'] | ['computer-vision'] | [ 2.01070368e-01 -8.77943709e-02 -1.05149142e-01 -3.81649613e-01
1.45950064e-01 -3.71258408e-01 7.99512148e-01 3.19155693e-01
-1.60551891e-01 7.55655318e-02 3.87392849e-01 -2.00682610e-01
-4.83353794e-01 -8.72558415e-01 -4.43042129e-01 -6.29308462e-01
-2.77286805e-02 2.22488716e-01 3.41616273e-01 -8.78178850... | [10.253742218017578, 1.6549354791641235] |
898435f0-b7ab-4aa2-af65-87aaa77be401 | mipa-mutual-information-based-paraphrase | null | null | https://aclanthology.org/I17-1009 | https://aclanthology.org/I17-1009.pdf | MIPA: Mutual Information Based Paraphrase Acquisition via Bilingual Pivoting | We present a pointwise mutual information (PMI)-based approach to formalize paraphrasability and propose a variant of PMI, called MIPA, for the paraphrase acquisition. Our paraphrase acquisition method first acquires lexical paraphrase pairs by bilingual pivoting and then reranks them by PMI and distributional similari... | ['Daichi Mochihashi', 'Tomoyuki Kajiwara', 'Mamoru Komachi'] | 2017-11-01 | mipa-mutual-information-based-paraphrase-1 | https://aclanthology.org/I17-1009 | https://aclanthology.org/I17-1009.pdf | ijcnlp-2017-11 | ['learning-word-embeddings'] | ['methodology'] | [-1.07651256e-01 -3.60261917e-01 -8.35759580e-01 -4.52271849e-01
-9.13829684e-01 -9.20059741e-01 7.95234978e-01 4.43915784e-01
-5.16474009e-01 9.15069938e-01 6.49260640e-01 -5.98330736e-01
-5.52275360e-01 -7.04508245e-01 -5.14816940e-01 -3.47110555e-02
4.27716106e-01 7.81910002e-01 -5.31289726e-02 -3.38332444... | [11.401956558227539, 9.288990020751953] |
c3532d12-e7a7-4969-b432-64420feb281a | single-channel-speech-dereverberation-using | 2204.08765 | null | https://arxiv.org/abs/2204.08765v2 | https://arxiv.org/pdf/2204.08765v2.pdf | Speech Dereverberation with A Reverberation Time Shortening Target | This work proposes a new learning target based on reverberation time shortening (RTS) for speech dereverberation. The learning target for dereverberation is usually set as the direct-path speech or optionally with some early reflections. This type of target suddenly truncates the reverberation, and thus it may not be s... | ['Xiaofei Li', 'Wenye Zhu', 'Rui Zhou'] | 2022-04-19 | null | null | null | null | ['speech-denoising', 'speech-dereverberation'] | ['speech', 'speech'] | [-1.35048598e-01 -2.07055047e-01 3.74876171e-01 -1.34209841e-01
-4.14405853e-01 -2.25234732e-01 1.43716618e-01 -1.39445022e-01
-3.15510809e-01 7.14471579e-01 3.61956447e-01 -5.05250514e-01
-1.56816602e-01 -5.99779427e-01 -4.07983720e-01 -9.46702659e-01
-2.79000819e-01 -4.06967491e-01 1.58055335e-01 -5.03336251... | [15.084573745727539, 5.907669544219971] |
7e30f432-932d-4e4e-8cd1-d1eb5bd095b6 | characterbert-and-self-teaching-for-improving | 2204.00716 | null | https://arxiv.org/abs/2204.00716v2 | https://arxiv.org/pdf/2204.00716v2.pdf | CharacterBERT and Self-Teaching for Improving the Robustness of Dense Retrievers on Queries with Typos | Current dense retrievers are not robust to out-of-domain and outlier queries, i.e. their effectiveness on these queries is much poorer than what one would expect. In this paper, we consider a specific instance of such queries: queries that contain typos. We show that a small character level perturbation in queries (as ... | ['Guido Zuccon', 'Shengyao Zhuang'] | 2022-04-01 | null | null | null | null | ['passage-retrieval'] | ['natural-language-processing'] | [-2.05107093e-01 -3.07488889e-01 -1.22122161e-01 -1.20871522e-01
-1.37325442e+00 -8.04622769e-01 6.22281611e-01 3.59073162e-01
-8.79196644e-01 6.24368787e-01 4.90451843e-01 -2.78178573e-01
-2.14408934e-01 -8.18056107e-01 -1.07372260e+00 -4.42710817e-01
2.41063431e-01 7.93716013e-01 4.39377308e-01 -5.87112546... | [11.50539493560791, 7.71367883682251] |
6d844367-ae00-4f54-ba4b-807dbbd30563 | si-lstm-speaker-hybrid-long-short-term-memory | 2305.03506 | null | https://arxiv.org/abs/2305.03506v3 | https://arxiv.org/pdf/2305.03506v3.pdf | SI-LSTM: Speaker Hybrid Long-short Term Memory and Cross Modal Attention for Emotion Recognition in Conversation | Emotion Recognition in Conversation~(ERC) across modalities is of vital importance for a variety of applications, including intelligent healthcare, artificial intelligence for conversation, and opinion mining over chat history. The crux of ERC is to model both cross-modality and cross-time interactions throughout the c... | ['Ruifeng Xu', 'You Zou', 'Xingwei Liang'] | 2023-05-04 | null | null | null | null | ['emotion-recognition-in-conversation', 'opinion-mining'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.16059100e-03 -3.66584599e-01 1.26920924e-01 -5.91786087e-01
-8.26600134e-01 -2.96674490e-01 5.07547677e-01 -1.03277750e-01
-2.23193094e-01 4.57176745e-01 7.84665227e-01 7.90646300e-02
1.76868320e-01 -4.23329361e-02 -1.57332689e-01 -8.33011448e-01
-2.37555534e-01 4.86318469e-02 -3.19444001e-01 -4.23674881... | [13.164769172668457, 5.630122184753418] |
d349740e-3ac4-4c87-855e-6086e906a614 | answering-ambiguous-questions-via-iterative | 2307.03897 | null | https://arxiv.org/abs/2307.03897v1 | https://arxiv.org/pdf/2307.03897v1.pdf | Answering Ambiguous Questions via Iterative Prompting | In open-domain question answering, due to the ambiguity of questions, multiple plausible answers may exist. To provide feasible answers to an ambiguous question, one approach is to directly predict all valid answers, but this can struggle with balancing relevance and diversity. An alternative is to gather candidate ans... | ['Zhaochun Ren', 'Maarten de Rijke', 'Zhumin Chen', 'Pengjie Ren', 'Hongshen Chen', 'Hengyi Cai', 'Weiwei Sun'] | 2023-07-08 | null | null | null | null | ['question-answering', 'open-domain-question-answering'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.72309905e-01 1.76195994e-01 -9.14882198e-02 -4.86666650e-01
-1.41799462e+00 -9.31700289e-01 4.35522437e-01 2.87875950e-01
-3.07950228e-01 7.01613784e-01 4.37725365e-01 -5.05897224e-01
-2.07766071e-01 -7.75879622e-01 -5.44064164e-01 -1.09397061e-01
5.54383934e-01 8.63215804e-01 7.70043433e-01 -3.05605292... | [11.28713607788086, 8.005880355834961] |
29f7cbc8-b03e-4484-bb38-b127fcb80589 | biot-cross-data-biosignal-learning-in-the | 2305.10351 | null | https://arxiv.org/abs/2305.10351v1 | https://arxiv.org/pdf/2305.10351v1.pdf | BIOT: Cross-data Biosignal Learning in the Wild | Biological signals, such as electroencephalograms (EEG), play a crucial role in numerous clinical applications, exhibiting diverse data formats and quality profiles. Current deep learning models for biosignals are typically specialized for specific datasets and clinical settings, limiting their broader applicability. M... | ['Jimeng Sun', 'M. Brandon Westover', 'Chaoqi Yang'] | 2023-05-10 | null | null | null | null | ['seizure-detection'] | ['medical'] | [ 3.65440547e-01 -4.57057863e-01 -1.86615512e-01 -4.88586783e-01
-1.08836412e+00 -4.69860971e-01 2.45955393e-01 3.47268850e-01
-5.44250786e-01 8.27037692e-01 5.90891957e-01 -3.11405063e-01
-1.70453504e-01 -2.82813251e-01 -7.56022930e-01 -5.71427226e-01
-4.24716592e-01 -1.09310918e-01 -2.29768738e-01 -1.85434639... | [13.31419563293457, 3.493964195251465] |
aaaa8bc7-3071-4988-9566-3c05d817f564 | the-state-of-human-centered-nlp-technology | 2301.03056 | null | https://arxiv.org/abs/2301.03056v1 | https://arxiv.org/pdf/2301.03056v1.pdf | The State of Human-centered NLP Technology for Fact-checking | Misinformation threatens modern society by promoting distrust in science, changing narratives in public health, heightening social polarization, and disrupting democratic elections and financial markets, among a myriad of other societal harms. To address this, a growing cadre of professional fact-checkers and journalis... | ['Matthew Lease', 'Venelin Kovatchev', 'Houjiang Liu', 'Anubrata Das'] | 2023-01-08 | null | null | null | null | ['explainable-models'] | ['computer-vision'] | [ 3.88884060e-02 6.55209005e-01 -5.73488176e-01 -4.96057272e-01
-7.82398582e-01 -8.27799618e-01 7.50399590e-01 6.15163386e-01
4.42045294e-02 7.25521922e-01 8.10638905e-01 -8.72591138e-01
-2.07541525e-01 -6.98047101e-01 -6.13755524e-01 1.46658346e-01
1.50178730e-01 3.34608078e-01 -2.78305024e-01 -1.04758747... | [9.603793144226074, 7.86621618270874] |
cb83c8f2-21cd-47fa-bfe8-0e62a6b01fe2 | lung-nodule-classification-using-deep-local | 1904.10126 | null | http://arxiv.org/abs/1904.10126v1 | http://arxiv.org/pdf/1904.10126v1.pdf | Lung Nodule Classification using Deep Local-Global Networks | Purpose: Lung nodules have very diverse shapes and sizes, which makes
classifying them as benign/malignant a challenging problem. In this paper, we
propose a novel method to predict the malignancy of nodules that have the
capability to analyze the shape and size of a nodule using a global feature
extractor, as well as ... | ['Kwan-Hoong Ng', 'Mundher Al-Shabi', 'Maxine Tan', 'Boon Leong Lan', 'Wai Yee Chan'] | 2019-04-23 | null | null | null | null | ['lung-nodule-classification'] | ['medical'] | [-3.69555503e-02 2.64562905e-01 -9.95221511e-02 -2.07026973e-01
-9.36324120e-01 -1.53898761e-01 4.97507960e-01 -1.83107525e-01
-5.25625765e-01 4.38201696e-01 1.48839474e-01 -3.62210810e-01
-2.34923348e-01 -6.82255268e-01 -4.57382202e-01 -1.04094481e+00
-1.88746154e-01 5.35395205e-01 6.76798701e-01 2.22859815... | [15.40493392944336, -2.180588483810425] |
8ac1642e-1bd4-4f34-840c-d5bb3593ae0e | squeezesegv2-improved-model-structure-and | 1809.08495 | null | http://arxiv.org/abs/1809.08495v1 | http://arxiv.org/pdf/1809.08495v1.pdf | SqueezeSegV2: Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloud | Earlier work demonstrates the promise of deep-learning-based approaches for
point cloud segmentation; however, these approaches need to be improved to be
practically useful. To this end, we introduce a new model SqueezeSegV2 that is
more robust to dropout noise in LiDAR point clouds. With improved model
structure, trai... | ['Kurt Keutzer', 'Xuanyu Zhou', 'Xiangyu Yue', 'Bichen Wu', 'Sicheng Zhao'] | 2018-09-22 | null | null | null | null | ['robust-3d-semantic-segmentation'] | ['computer-vision'] | [ 2.73483008e-01 9.23242047e-03 3.45290340e-02 -5.68861961e-01
-1.16102207e+00 -6.38585150e-01 3.02806526e-01 -3.06409113e-02
-4.64817733e-01 5.78800440e-01 -5.61194539e-01 -2.84904301e-01
5.64943254e-01 -8.27129006e-01 -9.85861778e-01 -3.07586461e-01
2.04883873e-01 9.08968031e-01 5.70634663e-01 -2.72358954... | [8.143806457519531, -2.973273992538452] |
21b7579e-a59f-487e-a54b-5cb8aaee4d1e | not-only-look-but-also-listen-learning | 2007.04687 | null | https://arxiv.org/abs/2007.04687v2 | https://arxiv.org/pdf/2007.04687v2.pdf | Not only Look, but also Listen: Learning Multimodal Violence Detection under Weak Supervision | Violence detection has been studied in computer vision for years. However, previous work are either superficial, e.g., classification of short-clips, and the single scenario, or undersupplied, e.g., the single modality, and hand-crafted features based multimodality. To address this problem, in this work we first releas... | ['Zhaoyang Wu', 'Zhiwei Yang', 'Yujia Shi', 'Peng Wu', 'Fangtao Shao', 'Jing Liu', 'Yujia Sun'] | 2020-07-09 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/7476_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123750324.pdf | eccv-2020-8 | ['anomaly-detection-in-surveillance-videos', 'anomaly-detection-in-surveillance-videos'] | ['computer-vision', 'methodology'] | [-7.88745657e-03 -6.20665908e-01 -1.95051298e-01 -2.82251567e-01
-8.41292441e-01 -4.00805652e-01 3.62922877e-01 -7.19408616e-02
-3.15073878e-01 3.39878887e-01 5.13023198e-01 1.89001441e-01
-2.86394447e-01 -5.31724870e-01 -6.23338699e-01 -6.58929527e-01
-3.09018582e-01 -3.59892379e-03 3.71506453e-01 -3.45872313... | [13.437986373901367, 4.76267147064209] |
d2f29f42-db94-420a-9444-82ac0255f9fb | unsupervised-text-summarization-via-mixed | 1908.08566 | null | https://arxiv.org/abs/1908.08566v1 | https://arxiv.org/pdf/1908.08566v1.pdf | Unsupervised Text Summarization via Mixed Model Back-Translation | Back-translation based approaches have recently lead to significant progress in unsupervised sequence-to-sequence tasks such as machine translation or style transfer. In this work, we extend the paradigm to the problem of learning a sentence summarization system from unaligned data. We present several initial models wh... | ['Yacine Jernite'] | 2019-08-22 | null | null | null | null | ['abstractive-sentence-summarization', 'unsupervised-sentence-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 9.32111084e-01 4.36723262e-01 -2.73521185e-01 -5.90616822e-01
-1.35121286e+00 -8.47967029e-01 1.05100024e+00 2.10384488e-01
-3.99248540e-01 1.22895634e+00 9.58907187e-01 -3.07608277e-01
4.33707267e-01 -1.17828779e-01 -7.95889616e-01 -3.17996353e-01
4.61648017e-01 1.12173545e+00 7.96797201e-02 -5.58574080... | [12.470736503601074, 9.453383445739746] |
81bcf840-1b34-4489-a8c2-8aa16c06e590 | contribution-of-data-categories-to | 1803.07850 | null | http://arxiv.org/abs/1803.07850v2 | http://arxiv.org/pdf/1803.07850v2.pdf | Contribution of Data Categories to Readmission Prediction Accuracy | Identification of patients at high risk for readmission could help reduce
morbidity and mortality as well as healthcare costs. Most of the existing
studies on readmission prediction did not compare the contribution of data
categories. In this study we analyzed relative contribution of 90,101 variables
across 398,884 ad... | [] | 2018-03-22 | null | null | null | null | ['readmission-prediction'] | ['medical'] | [-2.35911772e-01 -2.20691696e-01 -6.07470214e-01 -2.82405138e-01
-6.62463248e-01 -2.62043446e-01 1.11028172e-01 1.12769878e+00
-4.96067554e-01 1.01966488e+00 1.07135510e+00 -9.43315804e-01
-7.36467004e-01 -7.92635024e-01 -3.19150805e-01 -1.55002087e-01
-2.90263087e-01 5.35219610e-01 -6.71033502e-01 3.77832413... | [8.000481605529785, 6.160478115081787] |
a07228e4-20b9-4c48-9c06-97d9ebc2e4c0 | named-entity-recognition-multi-task-learning | 2205.09651 | null | https://arxiv.org/abs/2205.09651v2 | https://arxiv.org/pdf/2205.09651v2.pdf | Wojood: Nested Arabic Named Entity Corpus and Recognition using BERT | This paper presents Wojood, a corpus for Arabic nested Named Entity Recognition (NER). Nested entities occur when one entity mention is embedded inside another entity mention. Wojood consists of about 550K Modern Standard Arabic (MSA) and dialect tokens that are manually annotated with 21 entity types including person,... | ['Sana Ghanem', 'Mohammed Khalilia', 'Mustafa Jarrar'] | 2022-05-19 | null | https://aclanthology.org/2022.lrec-1.387 | https://aclanthology.org/2022.lrec-1.387.pdf | lrec-2022-6 | ['nested-named-entity-recognition'] | ['natural-language-processing'] | [-6.91162705e-01 4.26025838e-01 6.81843758e-02 -2.87284493e-01
-1.01980555e+00 -9.99958158e-01 4.78323966e-01 5.14693201e-01
-7.41649687e-01 9.08392847e-01 2.92856693e-01 -1.67823538e-01
2.06527933e-01 -6.62454724e-01 -5.21192312e-01 -3.95190835e-01
-2.25216895e-01 5.38645566e-01 2.97291070e-01 -4.54510003... | [9.90077018737793, 9.779037475585938] |
74256ed8-dfbc-4cc8-a3f9-2efa80465718 | an-optimal-and-scalable-matrix-mechanism-for | 2305.08175 | null | https://arxiv.org/abs/2305.08175v1 | https://arxiv.org/pdf/2305.08175v1.pdf | An Optimal and Scalable Matrix Mechanism for Noisy Marginals under Convex Loss Functions | Noisy marginals are a common form of confidentiality-protecting data release and are useful for many downstream tasks such as contingency table analysis, construction of Bayesian networks, and even synthetic data generation. Privacy mechanisms that provide unbiased noisy answers to linear queries (such as marginals) ar... | ['Daniel Kifer', 'Danfeng Zhang', 'Guanlin He', 'Yingtai Xiao'] | 2023-05-14 | null | null | null | null | ['synthetic-data-generation', 'synthetic-data-generation'] | ['medical', 'miscellaneous'] | [ 7.82362819e-02 3.57401669e-01 -6.26983866e-02 -5.51388383e-01
-1.46266854e+00 -8.82609546e-01 4.80445743e-01 5.53641021e-01
-5.94225347e-01 9.22892570e-01 2.34053314e-01 -7.80722260e-01
-6.69782236e-02 -1.00162244e+00 -1.02603090e+00 -5.81624687e-01
-4.28551853e-01 6.85756326e-01 -6.95250090e-03 1.71940520... | [6.004108428955078, 6.83538818359375] |
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