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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2359a740-8ee2-47e2-98bc-499c9976dcad | zero-shot-generalizable-end-to-end-task | 2303.16252 | null | https://arxiv.org/abs/2303.16252v1 | https://arxiv.org/pdf/2303.16252v1.pdf | Zero-Shot Generalizable End-to-End Task-Oriented Dialog System using Context Summarization and Domain Schema | Task-oriented dialog systems empower users to accomplish their goals by facilitating intuitive and expressive natural language interactions. State-of-the-art approaches in task-oriented dialog systems formulate the problem as a conditional sequence generation task and fine-tune pre-trained causal language models in the... | ['A. B. Siddique', 'M. H. Maqbool', 'Adib Mosharrof'] | 2023-03-28 | null | null | null | null | ['response-generation'] | ['natural-language-processing'] | [ 2.26877898e-01 5.90071142e-01 -1.53582647e-01 -7.08417356e-01
-6.06771469e-01 -8.22051585e-01 9.66961920e-01 -1.25473723e-01
-3.11201155e-01 1.10570478e+00 6.84438229e-01 -2.33263314e-01
1.00446798e-01 -6.22476935e-01 -2.92907298e-01 -2.33979642e-01
1.50165394e-01 1.29731762e+00 3.45565349e-01 -8.34995985... | [12.822306632995605, 8.023255348205566] |
4855786f-6cf5-44ed-b0dd-7c5406f9aa85 | corefdre-document-level-relation-extraction | 2202.10744 | null | https://arxiv.org/abs/2202.10744v1 | https://arxiv.org/pdf/2202.10744v1.pdf | CorefDRE: Document-level Relation Extraction with coreference resolution | Document-level relation extraction is to extract relation facts from a document consisting of multiple sentences, in which pronoun crossed sentences are a ubiquitous phenomenon against a single sentence. However, most of the previous works focus more on mentions coreference resolution except for pronouns, and rarely pa... | ['Zhong Jiang', 'Qizhu Dai', 'Rongzhen Li', 'Zhongxuan Xue'] | 2022-02-22 | null | null | null | null | ['document-level-relation-extraction'] | ['natural-language-processing'] | [ 2.57689327e-01 5.28668821e-01 -4.98504370e-01 -4.07355785e-01
-5.52476704e-01 -6.14810169e-01 7.23472416e-01 5.71530759e-01
-3.61554801e-01 8.41424227e-01 9.14159656e-01 -1.82924628e-01
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1.62192285e-01 9.28804696e-01 4.61103618e-01 -7.29194224... | [9.30571174621582, 8.819731712341309] |
e2243dbc-4248-4276-ac47-1fc5886086df | conformer-based-elderly-speech-recognition | 2206.13232 | null | https://arxiv.org/abs/2206.13232v1 | https://arxiv.org/pdf/2206.13232v1.pdf | Conformer Based Elderly Speech Recognition System for Alzheimer's Disease Detection | Early diagnosis of Alzheimer's disease (AD) is crucial in facilitating preventive care to delay further progression. This paper presents the development of a state-of-the-art Conformer based speech recognition system built on the DementiaBank Pitt corpus for automatic AD detection. The baseline Conformer system trained... | ['Helen Meng', 'Xunying Liu', 'Zengrui Jin', 'Mingyu Cui', 'Yi Wang', 'Shoukang Hu', 'Zi Ye', 'Mengzhe Geng', 'Jiajun Deng', 'Tianzi Wang'] | 2022-06-23 | null | null | null | null | ['alzheimer-s-disease-detection'] | ['medical'] | [ 5.57898462e-01 5.12615561e-01 2.96227545e-01 -7.95778394e-01
-1.54347849e+00 8.49107057e-02 6.05115950e-01 1.16004713e-01
-8.61278832e-01 8.08140159e-01 6.21469617e-01 -2.55467981e-01
-1.33492574e-01 -2.51839191e-01 -8.54031146e-02 -3.59223038e-01
-2.22782120e-01 8.85145485e-01 3.73590350e-01 -5.15634596... | [13.96345043182373, 5.465603828430176] |
56518bc6-3f7e-4134-9570-93723856b9f6 | a-preliminary-study-on-environmental-sound | null | null | https://aclanthology.org/2021.rocling-1.14 | https://aclanthology.org/2021.rocling-1.14.pdf | A Preliminary Study on Environmental Sound Classification Leveraging Large-Scale Pretrained Model and Semi-Supervised Learning | With the widespread commercialization of smart devices, research on environmental sound classification has gained more and more attention in recent years. In this paper, we set out to make effective use of large-scale audio pretrained model and semi-supervised model training paradigm for environmental sound classificat... | ['Berlin Chen', 'Shi-Yan Weng', 'Jiun-Ting Li', 'Tien-Hong Lo', 'You-Sheng Tsao'] | null | null | null | null | rocling-2021-10 | ['environmental-sound-classification', 'sound-classification'] | ['audio', 'audio'] | [ 5.51531136e-01 -5.89181669e-02 1.99466616e-01 -3.91082585e-01
-9.74821270e-01 -3.20327610e-01 5.17103553e-01 9.88503844e-02
-3.13785642e-01 3.33204240e-01 1.27079591e-01 -3.49995673e-01
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-4.07613488e-03 -6.37588277e-02 1.85637847e-01 -9.85341519... | [15.169901847839355, 5.2015767097473145] |
1efa69b6-4653-4914-9abf-ed5a23ff7d73 | somoformer-multi-person-pose-forecasting-with | 2208.14023 | null | https://arxiv.org/abs/2208.14023v1 | https://arxiv.org/pdf/2208.14023v1.pdf | SoMoFormer: Multi-Person Pose Forecasting with Transformers | Human pose forecasting is a challenging problem involving complex human body motion and posture dynamics. In cases that there are multiple people in the environment, one's motion may also be influenced by the motion and dynamic movements of others. Although there are several previous works targeting the problem of mult... | ['Hamid Rezatofighi', 'Ehsan Adeli', 'Satyajit Kumar', 'Edward Vendrow'] | 2022-08-30 | null | null | null | null | ['multi-person-pose-forecasting', 'human-pose-forecasting'] | ['computer-vision', 'computer-vision'] | [-3.04886997e-01 -2.03785107e-01 2.08188519e-02 -1.15292564e-01
-2.77568072e-01 -4.19346809e-01 7.39166796e-01 -4.93953586e-01
-4.83046293e-01 3.22132766e-01 9.10029233e-01 5.77071309e-01
1.61008626e-01 -4.43874091e-01 -5.64604282e-01 -5.81881225e-01
-1.08634569e-01 1.14036071e+00 2.85791218e-01 -3.98850083... | [7.232895851135254, -0.361954927444458] |
ab0b2bc9-5ce6-4cfd-a64d-4e2e47293933 | openmixup-open-mixup-toolbox-and-benchmark | 2209.04851 | null | https://arxiv.org/abs/2209.04851v1 | https://arxiv.org/pdf/2209.04851v1.pdf | OpenMixup: Open Mixup Toolbox and Benchmark for Visual Representation Learning | With the remarkable progress of deep neural networks in computer vision, data mixing augmentation techniques are widely studied to alleviate problems of degraded generalization when the amount of training data is limited. However, mixup strategies have not been well assembled in current vision toolboxes. In this paper,... | ['Stan Z. Li', 'Di wu', 'Zicheng Liu', 'Zedong Wang', 'Siyuan Li'] | 2022-09-11 | null | null | null | null | ['semi-supervised-image-classification'] | ['computer-vision'] | [-1.84824578e-02 -2.16527283e-01 -4.10626829e-01 -3.31393868e-01
-3.91510993e-01 -3.93605471e-01 6.85413122e-01 -3.10066372e-01
-2.70106196e-01 3.04297119e-01 -1.12747297e-01 -4.28105682e-01
3.74219745e-01 -4.68131602e-01 -4.67624128e-01 -7.66652107e-01
4.30717796e-01 3.75477731e-01 -1.53512478e-01 -1.76501900... | [9.73228931427002, 1.80404531955719] |
33ed29ee-71c4-4eba-8caa-2af93f558c9f | a-generalized-latent-factor-model-approach-to | 2211.09272 | null | https://arxiv.org/abs/2211.09272v1 | https://arxiv.org/pdf/2211.09272v1.pdf | A Generalized Latent Factor Model Approach to Mixed-data Matrix Completion with Entrywise Consistency | Matrix completion is a class of machine learning methods that concerns the prediction of missing entries in a partially observed matrix. This paper studies matrix completion for mixed data, i.e., data involving mixed types of variables (e.g., continuous, binary, ordinal). We formulate it as a low-rank matrix estimation... | ['Xiaoou Li', 'Yunxiao Chen'] | 2022-11-17 | null | null | null | null | ['matrix-completion'] | ['methodology'] | [ 2.03254700e-01 8.96143094e-02 -3.43040675e-01 -4.15686697e-01
-9.86064017e-01 -4.79374737e-01 1.82090759e-01 3.79563570e-01
-2.95200795e-01 8.53414774e-01 4.27038610e-01 -4.11022991e-01
-9.35318112e-01 -4.26208019e-01 -9.71184552e-01 -5.96984982e-01
-3.13931435e-01 4.65925932e-01 -4.87205714e-01 1.11818343... | [7.064157962799072, 4.608391761779785] |
025b767c-024f-4095-bdeb-4ec41bc03542 | genres-parsers-and-bert-the-interaction | null | null | https://aclanthology.org/2021.adaptnlp-1.7 | https://aclanthology.org/2021.adaptnlp-1.7.pdf | Genres, Parsers, and BERT: The Interaction Between Parsers and BERT Models in Cross-Genre Constituency Parsing in English and Swedish | Genre and domain are often used interchangeably, but are two different properties of a text. Successful parser adaptation requires both cross-domain and cross-genre sensitivity (Rehbein and Bildhauer, 2017). While the impact domain differences have on parser performance degradation is more easily measurable in respect ... | ['Daniel Dakota'] | null | null | null | null | eacl-adaptnlp-2021-4 | ['constituency-parsing'] | ['natural-language-processing'] | [ 1.46286711e-01 -2.66370386e-01 2.35009082e-02 -5.58541059e-01
-9.65590417e-01 -1.05303943e+00 5.24068952e-01 3.47718745e-01
-7.02764273e-01 5.63581228e-01 7.03826129e-01 -3.28506470e-01
-1.73238218e-01 -3.89840335e-01 -5.31956196e-01 -2.77905434e-01
3.27733427e-01 3.49856585e-01 2.90937245e-01 -3.73645693... | [10.672430992126465, 9.589637756347656] |
27c30247-64a9-4954-a1d1-aa3e04a936ad | language-adaptive-weight-generation-for-multi-1 | 2306.04652 | null | https://arxiv.org/abs/2306.04652v1 | https://arxiv.org/pdf/2306.04652v1.pdf | Language Adaptive Weight Generation for Multi-task Visual Grounding | Although the impressive performance in visual grounding, the prevailing approaches usually exploit the visual backbone in a passive way, i.e., the visual backbone extracts features with fixed weights without expression-related hints. The passive perception may lead to mismatches (e.g., redundant and missing), limiting ... | ['Xi Li', 'Zheyang Li', 'Liang Qiao', 'Gaoang Wang', 'Huanzhang Dou', 'Peihan Miao', 'Wei Su'] | 2023-06-06 | language-adaptive-weight-generation-for-multi | http://openaccess.thecvf.com//content/CVPR2023/html/Su_Language_Adaptive_Weight_Generation_for_Multi-Task_Visual_Grounding_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Su_Language_Adaptive_Weight_Generation_for_Multi-Task_Visual_Grounding_CVPR_2023_paper.pdf | cvpr-2023-1 | ['visual-grounding', 'referring-expression'] | ['computer-vision', 'computer-vision'] | [ 1.60731986e-01 3.53992254e-01 -2.33877495e-01 -4.50825185e-01
-7.06732452e-01 -6.82071567e-01 4.78501052e-01 1.96914256e-01
-5.38826942e-01 2.74531454e-01 3.70091766e-01 7.12703392e-02
9.41463336e-02 -4.99404609e-01 -8.38656068e-01 -7.44688451e-01
4.69235569e-01 2.75357086e-02 1.64721444e-01 -5.00046134... | [10.447237968444824, 1.3096363544464111] |
365c36c1-b43e-4f69-852f-40159b0b105d | semantic-line-detection-using-mirror-1 | 2203.15285 | null | https://arxiv.org/abs/2203.15285v1 | https://arxiv.org/pdf/2203.15285v1.pdf | Semantic Line Detection Using Mirror Attention and Comparative Ranking and Matching | A novel algorithm to detect semantic lines is proposed in this paper. We develop three networks: detection network with mirror attention (D-Net) and comparative ranking and matching networks (R-Net and M-Net). D-Net extracts semantic lines by exploiting rich contextual information. To this end, we design the mirror att... | ['Chang-Su Kim', 'Jun-Tae Lee', 'Dongkwon Jin'] | 2022-03-29 | semantic-line-detection-using-mirror | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/3397_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123650120.pdf | eccv-2020-8 | ['line-detection'] | ['computer-vision'] | [-9.00793374e-02 -8.01321268e-02 -1.42935768e-01 -3.49445105e-01
-5.96678197e-01 -4.60121065e-01 3.51055950e-01 -1.28456131e-01
-2.44589582e-01 2.43507221e-01 3.06592137e-01 -1.05882816e-01
-3.48940998e-01 -9.26451147e-01 -6.48744583e-01 -2.60843307e-01
1.32786080e-01 2.08601341e-01 4.48456973e-01 -2.26265073... | [8.346919059753418, -1.544702410697937] |
3cbee7b9-09a7-4bea-8fcf-3c04c4e54cd1 | diverse-and-faithful-knowledge-grounded | 2306.01153 | null | https://arxiv.org/abs/2306.01153v1 | https://arxiv.org/pdf/2306.01153v1.pdf | Diverse and Faithful Knowledge-Grounded Dialogue Generation via Sequential Posterior Inference | The capability to generate responses with diversity and faithfulness using factual knowledge is paramount for creating a human-like, trustworthy dialogue system. Common strategies either adopt a two-step paradigm, which optimizes knowledge selection and response generation separately, and may overlook the inherent corr... | ['Ying Nian Wu', 'Pascale Fung', 'Bo Pang', 'Ziwei Ji', 'Dehong Xu', 'Deqian Kong', 'Yan Xu'] | 2023-06-01 | null | null | null | null | ['dialogue-generation', 'response-generation', 'dialogue-generation'] | ['natural-language-processing', 'natural-language-processing', 'speech'] | [ 3.45635377e-02 7.69932747e-01 -1.79016814e-01 -4.67378557e-01
-1.01991415e+00 -8.30212355e-01 1.09469807e+00 -1.44922867e-01
-4.92185622e-01 1.17623150e+00 5.70904553e-01 -4.94236685e-03
1.45169944e-01 -7.07207143e-01 -3.80704254e-01 -4.25774246e-01
4.74316537e-01 8.68356586e-01 -9.23938642e-04 -6.04726136... | [12.591958045959473, 8.278257369995117] |
db938a08-dacb-43fd-9396-0098510dd842 | uncertainty-guided-multi-scale-residual-1 | 1906.11129 | null | https://arxiv.org/abs/1906.11129v1 | https://arxiv.org/pdf/1906.11129v1.pdf | Uncertainty Guided Multi-Scale Residual Learning-using a Cycle Spinning CNN for Single Image De-Raining | Single image de-raining is an extremely challenging problem since the rainy image may contain rain streaks which may vary in size, direction and density. Previous approaches have attempted to address this problem by leveraging some prior information to remove rain streaks from a single image. One of the major limitatio... | ['Rajeev Yasarla', 'Vishal M. Patel'] | 2019-06-12 | uncertainty-guided-multi-scale-residual | http://openaccess.thecvf.com/content_CVPR_2019/html/Yasarla_Uncertainty_Guided_Multi-Scale_Residual_Learning-Using_a_Cycle_Spinning_CNN_for_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Yasarla_Uncertainty_Guided_Multi-Scale_Residual_Learning-Using_a_Cycle_Spinning_CNN_for_CVPR_2019_paper.pdf | cvpr-2019-6 | ['single-image-deraining'] | ['computer-vision'] | [-3.13819572e-02 -5.12979984e-01 1.75163493e-01 -5.97763479e-01
-6.22791886e-01 -2.71760672e-01 5.91322556e-02 -3.20712388e-01
-2.84458935e-01 9.84112442e-01 -1.81558594e-01 -1.61181390e-01
1.38921484e-01 -7.49709725e-01 -6.32362306e-01 -1.10242486e+00
-1.63263101e-02 -5.65772951e-02 4.14493740e-01 -1.06170572... | [10.932170867919922, -3.27445125579834] |
2109c26c-391a-4310-99f9-86e9ada2e18a | generating-equation-by-utilizing-operators | null | null | https://aclanthology.org/2020.coling-main.38 | https://aclanthology.org/2020.coling-main.38.pdf | Generating Equation by Utilizing Operators : GEO model | Math word problem solving is an emerging research topic in Natural Language Processing. Recently, to address the math word problem-solving task, researchers have applied the encoder-decoder architecture, which is mainly used in machine translation tasks. The state-of-the-art neural models use hand-crafted features and ... | ['Gahgene Gweon', 'Bugeun Kim', 'Donggeon Lee', 'Kyung Seo Ki'] | 2020-12-01 | null | null | null | coling-2020-8 | ['math-word-problem-solving', 'math-word-problem-solving', 'math-word-problem-solving'] | ['knowledge-base', 'reasoning', 'time-series'] | [ 2.61647493e-01 -8.49740133e-02 -4.70036939e-02 -5.20279050e-01
-5.88105083e-01 -1.86983302e-01 2.19237119e-01 1.26577571e-01
-4.83227015e-01 9.27699745e-01 -1.85733940e-02 -5.38575232e-01
-1.00938581e-01 -1.17768347e+00 -1.03157389e+00 -2.34514475e-01
3.65425587e-01 3.73911262e-01 3.50769199e-02 -5.31106651... | [9.805419921875, 7.497879981994629] |
67f5e0ca-5d9a-4537-aa21-674d243386ea | convolutional-monge-mapping-normalization-for | 2305.18831 | null | https://arxiv.org/abs/2305.18831v2 | https://arxiv.org/pdf/2305.18831v2.pdf | Convolutional Monge Mapping Normalization for learning on biosignals | In many machine learning applications on signals and biomedical data, especially electroencephalogram (EEG), one major challenge is the variability of the data across subjects, sessions, and hardware devices. In this work, we propose a new method called Convolutional Monge Mapping Normalization (CMMN), which consists i... | ['Alexandre Gramfort', 'Rémi Flamary', 'Théo Gnassounou'] | 2023-05-30 | null | null | null | null | ['eeg', 'eeg'] | ['methodology', 'time-series'] | [ 2.43551224e-01 -1.29471883e-01 3.56346160e-01 -4.35032636e-01
-4.27560061e-01 -2.86132067e-01 1.48322985e-01 2.29231387e-01
-7.52982914e-01 1.14777493e+00 -7.69921616e-02 -4.80930880e-02
-4.85009789e-01 -2.89798170e-01 -7.75253117e-01 -7.74183512e-01
-4.26568151e-01 3.51755083e-01 -5.09979278e-02 -5.98344058... | [13.109780311584473, 3.448014497756958] |
80d33001-0a15-4fc9-89a5-9a55a50913c4 | multi-modal-learning-with-prior-visual | 1812.09681 | null | https://arxiv.org/abs/1812.09681v2 | https://arxiv.org/pdf/1812.09681v2.pdf | Scene Graph Reasoning with Prior Visual Relationship for Visual Question Answering | One of the key issues of Visual Question Answering (VQA) is to reason with semantic clues in the visual content under the guidance of the question, how to model relational semantics still remains as a great challenge. To fully capture visual semantics, we propose to reason over a structured visual representation - scen... | ['Zhuoqian Yang', 'Yue Hu', 'Zengchang Qin', 'Jing Yu'] | 2018-12-23 | null | null | null | null | ['cross-modal-information-retrieval'] | ['miscellaneous'] | [-4.27813306e-02 3.93912494e-01 -5.59675768e-02 -5.98224103e-01
-5.60137093e-01 -5.18770993e-01 5.66808760e-01 2.47195795e-01
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-4.26671058e-02 -8.60729814e-01 -9.82031286e-01 -1.60900101e-01
2.35893920e-01 5.63392699e-01 2.32626915e-01 -3.30309600... | [10.630026817321777, 1.7272694110870361] |
2906f963-ca45-46db-9b5b-78c593c73048 | text-independent-speaker-verification-using-1 | 1805.00604 | null | http://arxiv.org/abs/1805.00604v3 | http://arxiv.org/pdf/1805.00604v3.pdf | Text-Independent Speaker Verification Using Long Short-Term Memory Networks | In this paper, an architecture based on Long Short-Term Memory Networks has
been proposed for the text-independent scenario which is aimed to capture the
temporal speaker-related information by operating over traditional speech
features. For speaker verification, at first, a background model must be
created for speaker... | ['Mohammad Najarian', 'Aryan Mobiny'] | 2018-05-02 | null | null | null | null | ['text-independent-speaker-verification'] | ['speech'] | [ 9.70780998e-02 -9.44685936e-02 -1.96080394e-02 -9.56553102e-01
-8.35115135e-01 -1.36131614e-01 5.46338081e-01 8.19400512e-03
-3.33275616e-01 2.61496097e-01 2.33596191e-01 -3.01010758e-01
8.51638541e-02 -3.00404757e-01 -1.40056193e-01 -7.49176741e-01
-1.82249665e-01 3.56852084e-01 -1.63251549e-01 -9.50096757... | [14.413080215454102, 6.106123924255371] |
76f66437-721a-4640-b4be-a74cfe09b752 | playing-hard-exploration-games-by-watching | 1805.11592 | null | http://arxiv.org/abs/1805.11592v2 | http://arxiv.org/pdf/1805.11592v2.pdf | Playing hard exploration games by watching YouTube | Deep reinforcement learning methods traditionally struggle with tasks where
environment rewards are particularly sparse. One successful method of guiding
exploration in these domains is to imitate trajectories provided by a human
demonstrator. However, these demonstrations are typically collected under
artificial condi... | ['Ziyu Wang', 'Nando de Freitas', 'David Budden', 'Yusuf Aytar', 'Tom Le Paine', 'Tobias Pfaff'] | 2018-05-29 | playing-hard-exploration-games-by-watching-1 | http://papers.nips.cc/paper/7557-playing-hard-exploration-games-by-watching-youtube | http://papers.nips.cc/paper/7557-playing-hard-exploration-games-by-watching-youtube.pdf | neurips-2018-12 | ['montezumas-revenge'] | ['playing-games'] | [ 4.05531339e-02 1.38864100e-01 -2.86276005e-02 2.80256439e-02
-7.24556744e-01 -7.89009869e-01 7.00304389e-01 -9.22791213e-02
-1.03924191e+00 1.00984800e+00 -1.44840479e-01 6.16620742e-02
4.49314853e-03 -3.53241444e-01 -9.72662389e-01 -5.62308729e-01
-5.38649261e-01 4.39431250e-01 -3.04738302e-02 -2.06463158... | [4.273669242858887, 1.3595255613327026] |
411678b7-265e-4f25-988a-b90de2d2da6b | censnet-convolution-with-edge-node-switching | null | null | https://doi.org/10.24963/ijcai.2019/369 | https://www.ijcai.org/proceedings/2019/0369.pdf | CensNet: Convolution with Edge-Node Switching in Graph Neural Networks | In this paper, we present CensNet, Convolution with Edge-Node Switching graph neural network, for semi-supervised classification and regression in graph-structured data with both node and edge features. CensNet is a general graph embedding framework, which embeds both nodes and edges to a latent feature space. By using... | ['Sheng Li', 'Pengsheng Ji', 'Xiaodong Jiang'] | 2019-08-10 | null | null | null | proceedings-of-the-twenty-eighth | ['graph-regression'] | ['graphs'] | [ 2.56355740e-02 4.70376819e-01 -2.78983802e-01 -3.02422374e-01
5.08003592e-01 -4.61971641e-01 7.45397866e-01 3.79045606e-01
7.52926618e-02 7.07460225e-01 -1.64403260e-01 -6.49694026e-01
-3.16996783e-01 -1.23480392e+00 -3.71471822e-01 -5.81279993e-01
-8.12194705e-01 3.64496022e-01 5.97141162e-02 -3.03034820... | [7.067370891571045, 6.243435382843018] |
f2e13b17-f8e7-405b-8c3d-013d90655f63 | cdpmsr-conditional-diffusion-probabilistic | 2302.12831 | null | https://arxiv.org/abs/2302.12831v1 | https://arxiv.org/pdf/2302.12831v1.pdf | CDPMSR: Conditional Diffusion Probabilistic Models for Single Image Super-Resolution | Diffusion probabilistic models (DPM) have been widely adopted in image-to-image translation to generate high-quality images. Prior attempts at applying the DPM to image super-resolution (SR) have shown that iteratively refining a pure Gaussian noise with a conditional image using a U-Net trained on denoising at various... | ['Yanning Zhang', 'In So Kweon', 'Yu Zhu', 'Jinqiu Sun', 'Trung X. Pham', 'Kang Zhang', 'Axi Niu'] | 2023-02-14 | null | null | null | null | ['conditional-image-generation'] | ['computer-vision'] | [ 8.46473277e-01 5.36817238e-02 3.09082299e-01 -1.80836022e-01
-1.32945728e+00 -1.64079070e-01 6.93839073e-01 -7.04884827e-01
-1.42343998e-01 8.55500698e-01 3.21474403e-01 4.68237922e-02
-7.80881643e-02 -9.57495809e-01 -7.14421868e-01 -8.54252696e-01
3.47327769e-01 9.51134712e-02 4.67847556e-01 -2.78781384... | [11.16444206237793, -2.052915096282959] |
059d3600-68cb-4410-869f-f247c0f59b21 | learnable-triangulation-for-deep-learning | 2109.11844 | null | https://arxiv.org/abs/2109.11844v1 | https://arxiv.org/pdf/2109.11844v1.pdf | Learnable Triangulation for Deep Learning-based 3D Reconstruction of Objects of Arbitrary Topology from Single RGB Images | We propose a novel deep reinforcement learning-based approach for 3D object reconstruction from monocular images. Prior works that use mesh representations are template based. Thus, they are limited to the reconstruction of objects that have the same topology as the template. Methods that use volumetric grids as interm... | ['Hamid Laga', 'Aladine Chetouani', 'Hedi Tabia', 'Tarek Ben Charrada'] | 2021-09-24 | null | null | null | null | ['3d-object-reconstruction', 'object-reconstruction'] | ['computer-vision', 'computer-vision'] | [-1.39837101e-01 1.42832920e-01 3.42577994e-01 -1.86338633e-01
-4.64080691e-01 -4.30419207e-01 3.95137399e-01 4.35530916e-02
-8.99636894e-02 5.36516607e-01 -2.91208714e-01 -1.92913994e-01
1.74167618e-01 -1.33838534e+00 -1.34000409e+00 -6.02525949e-01
2.69665897e-01 1.06798971e+00 3.02522957e-01 7.80039802... | [8.597366333007812, -3.5109875202178955] |
26190b70-1135-41dc-bd9f-4708246fe7f7 | why-can-t-discourse-parsing-generalize-a | 2302.06488 | null | https://arxiv.org/abs/2302.06488v1 | https://arxiv.org/pdf/2302.06488v1.pdf | Why Can't Discourse Parsing Generalize? A Thorough Investigation of the Impact of Data Diversity | Recent advances in discourse parsing performance create the impression that, as in other NLP tasks, performance for high-resource languages such as English is finally becoming reliable. In this paper we demonstrate that this is not the case, and thoroughly investigate the impact of data diversity on RST parsing stabili... | ['Amir Zeldes', 'Yang Janet Liu'] | 2023-02-13 | null | null | null | null | ['cross-corpus', 'discourse-parsing'] | ['computer-vision', 'natural-language-processing'] | [ 2.86383688e-01 3.28433186e-01 -2.38197386e-01 -4.74610269e-01
-1.43672967e+00 -1.01254296e+00 5.92668176e-01 2.88475573e-01
-6.79446042e-01 6.98576868e-01 6.40401065e-01 -6.76096916e-01
9.34798047e-02 -4.37188268e-01 -8.92614543e-01 -2.40834221e-01
-1.85569689e-01 5.51070452e-01 3.64196151e-01 -3.22236389... | [10.646891593933105, 9.480335235595703] |
d9baa06b-0380-4b67-bb11-e5f4594da245 | confidence-aware-3d-gaze-estimation-and | 2303.10062 | null | https://arxiv.org/abs/2303.10062v1 | https://arxiv.org/pdf/2303.10062v1.pdf | Confidence-aware 3D Gaze Estimation and Evaluation Metric | Deep learning appearance-based 3D gaze estimation is gaining popularity due to its minimal hardware requirements and being free of constraint. Unreliable and overconfident inferences, however, still limit the adoption of this gaze estimation method. To address the unreliable and overconfident issues, we introduce a con... | ['Xiaoli Zhang', 'Amy Zhang', 'Jiucai Zhang', 'Qiaojie Zheng'] | 2023-03-17 | null | null | null | null | ['gaze-estimation'] | ['computer-vision'] | [-1.90902174e-01 3.43291521e-01 1.17981203e-01 -8.80554676e-01
-6.19727850e-01 -1.31669939e-01 4.52203810e-01 1.92753181e-01
-4.52309579e-01 8.53714287e-01 -1.43442407e-01 -2.04750970e-01
-3.12997967e-01 1.44397952e-02 -7.14675426e-01 -5.46096623e-01
1.24589935e-01 -1.27269670e-01 1.70952603e-01 4.31647509... | [14.116935729980469, 0.06662078201770782] |
1e83730f-d64c-4477-904c-007de26c861e | singing-voice-synthesis-using-differentiable | 2306.17252 | null | https://arxiv.org/abs/2306.17252v1 | https://arxiv.org/pdf/2306.17252v1.pdf | Singing Voice Synthesis Using Differentiable LPC and Glottal-Flow-Inspired Wavetables | This paper introduces GlOttal-flow LPC Filter (GOLF), a novel method for singing voice synthesis (SVS) that exploits the physical characteristics of the human voice using differentiable digital signal processing. GOLF employs a glottal model as the harmonic source and IIR filters to simulate the vocal tract, resulting ... | ['György Fazekas', 'Chin-Yun Yu'] | 2023-06-29 | null | null | null | null | ['singing-voice-synthesis'] | ['speech'] | [-1.07930019e-01 -7.92381726e-03 1.58592850e-01 2.50755429e-01
-7.48729527e-01 -8.28813612e-01 2.94318736e-01 -4.74090993e-01
1.57832518e-01 4.74899232e-01 5.39051116e-01 -4.06985193e-01
-2.92368990e-04 -2.66755879e-01 -5.52203894e-01 -6.09173298e-01
-2.01999247e-01 -1.40789568e-01 7.84561560e-02 -4.14973140... | [15.450562477111816, 6.1111531257629395] |
589fca31-486a-4c0e-a822-b54502de9c7c | frsum-towards-faithful-abstractive-1 | 2211.00294 | null | https://arxiv.org/abs/2211.00294v1 | https://arxiv.org/pdf/2211.00294v1.pdf | FRSUM: Towards Faithful Abstractive Summarization via Enhancing Factual Robustness | Despite being able to generate fluent and grammatical text, current Seq2Seq summarization models still suffering from the unfaithful generation problem. In this paper, we study the faithfulness of existing systems from a new perspective of factual robustness which is the ability to correctly generate factual informatio... | ['Hua Wu', 'Sujian Li', 'Ziqiang Cao', 'Xinyan Xiao', 'Jiachen Liu', 'Wei Li', 'Wenhao Wu'] | 2022-11-01 | null | null | null | null | ['abstractive-text-summarization'] | ['natural-language-processing'] | [ 3.92856747e-01 6.83866382e-01 -6.04159497e-02 -3.54456723e-01
-1.00528121e+00 -1.08408546e+00 1.06290483e+00 1.50416018e-02
-9.31014419e-02 1.20892763e+00 8.39516163e-01 -1.62257269e-01
2.52868354e-01 -8.44212830e-01 -9.14294243e-01 -3.20518732e-01
2.34014392e-01 2.38365367e-01 -8.58120620e-02 -7.84960568... | [6.179298400878906, 8.184976577758789] |
b51cd257-acc6-47d3-9b88-494296d9113c | inter-rater-uncertainty-quantification-in | 2306.16556 | null | https://arxiv.org/abs/2306.16556v1 | https://arxiv.org/pdf/2306.16556v1.pdf | Inter-Rater Uncertainty Quantification in Medical Image Segmentation via Rater-Specific Bayesian Neural Networks | Automated medical image segmentation inherently involves a certain degree of uncertainty. One key factor contributing to this uncertainty is the ambiguity that can arise in determining the boundaries of a target region of interest, primarily due to variations in image appearance. On top of this, even among experts in t... | ['Hongwei Bran Li', 'JianGuo Zhang', 'Bjoern Menze', 'Benedikt Wiestler', 'Jan S. Kirschke', 'Zhiheng Zhangg', 'Yunhao Luo', 'Jing Luo', 'Hao Wang', 'Qingqiao Hu'] | 2023-06-28 | null | null | null | null | ['medical-image-segmentation'] | ['medical'] | [ 3.83423924e-01 3.47385645e-01 -1.97437182e-01 -7.18160152e-01
-1.55333722e+00 -6.56204462e-01 1.91474870e-01 1.98458061e-01
-4.91330653e-01 5.81221879e-01 4.48987812e-01 -2.56225169e-01
-6.07054941e-02 -1.91081896e-01 -7.74096906e-01 -4.80030507e-01
1.44093335e-01 5.93096673e-01 2.30294079e-01 3.03870052... | [14.539958953857422, -2.0592527389526367] |
951b2af8-1a00-46c9-a992-e9beb0f97d99 | scalable-knowledge-base-completion-with | 2110.12341 | null | https://arxiv.org/abs/2110.12341v1 | https://arxiv.org/pdf/2110.12341v1.pdf | Scalable knowledge base completion with superposition memories | We present Harmonic Memory Networks (HMem), a neural architecture for knowledge base completion that models entities as weighted sums of pairwise bindings between an entity's neighbors and corresponding relations. Since entities are modeled as aggregated neighborhoods, representations of unseen entities can be generate... | ['Paul Smolensky', 'Eric Rosen', 'Matthias Lalisse'] | 2021-10-24 | null | null | null | null | ['knowledge-base-completion', 'knowledge-base-completion'] | ['graphs', 'knowledge-base'] | [-2.84207642e-01 1.06220055e+00 -3.37226391e-01 -2.68841565e-01
-3.69287491e-01 -5.03705502e-01 3.39359671e-01 2.98837364e-01
-3.37286919e-01 1.15065157e+00 3.86413276e-01 1.02725439e-01
-4.06901300e-01 -1.47110653e+00 -1.04298592e+00 -2.41949007e-01
-7.05765843e-01 1.19185376e+00 2.78402805e-01 -4.00895268... | [8.90749454498291, 8.054883003234863] |
9aa9f7db-c006-42c9-b413-b07712a17b3a | colar-effective-and-efficient-online-action | 2203.01057 | null | https://arxiv.org/abs/2203.01057v2 | https://arxiv.org/pdf/2203.01057v2.pdf | Colar: Effective and Efficient Online Action Detection by Consulting Exemplars | Online action detection has attracted increasing research interests in recent years. Current works model historical dependencies and anticipate the future to perceive the action evolution within a video segment and improve the detection accuracy. However, the existing paradigm ignores category-level modeling and does n... | ['Dingwen Zhang', 'Junwei Han', 'Le Yang'] | 2022-03-02 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Yang_Colar_Effective_and_Efficient_Online_Action_Detection_by_Consulting_Exemplars_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Yang_Colar_Effective_and_Efficient_Online_Action_Detection_by_Consulting_Exemplars_CVPR_2022_paper.pdf | cvpr-2022-1 | ['online-action-detection'] | ['computer-vision'] | [-8.53018314e-02 -4.06088322e-01 -4.74959850e-01 -4.11236465e-01
-4.01705384e-01 -1.91298574e-01 6.64163113e-01 2.48081610e-01
-3.34002793e-01 2.77299464e-01 3.52870852e-01 1.38805479e-01
-1.18822731e-01 -7.75828958e-01 -4.11528915e-01 -6.31222427e-01
-3.31382930e-01 -1.83146149e-01 7.56407857e-01 -8.03974867... | [8.47673511505127, 0.578626275062561] |
4a12ee54-dee5-4409-a470-7efcd4b31fd7 | regularizing-disparity-estimation-via-multi | 2301.08140 | null | https://arxiv.org/abs/2301.08140v1 | https://arxiv.org/pdf/2301.08140v1.pdf | Regularizing disparity estimation via multi task learning with structured light reconstruction | 3D reconstruction is a useful tool for surgical planning and guidance. However, the lack of available medical data stunts research and development in this field, as supervised deep learning methods for accurate disparity estimation rely heavily on large datasets containing ground truth information. Alternative approach... | ['Stamatia Giannarou', 'Joseph Davids', 'Chi Xu', 'Joao Cartucho', 'Alistair Weld'] | 2023-01-19 | null | null | null | null | ['disparity-estimation'] | ['computer-vision'] | [ 4.78426188e-01 3.96645427e-01 -3.39325033e-02 -4.70114797e-01
-7.87257195e-01 -2.23731771e-01 5.93406379e-01 5.17645441e-02
-7.71408975e-01 9.17441428e-01 1.82876945e-01 -5.28679788e-01
1.11279823e-02 -5.46716511e-01 -7.87730575e-01 -9.60248232e-01
2.75401622e-01 3.97625893e-01 1.83094636e-01 -1.57295261... | [14.043069839477539, -2.960660219192505] |
a10a159a-85c2-443f-b33b-362ec4b52bd4 | dvqa-understanding-data-visualizations-via | 1801.08163 | null | http://arxiv.org/abs/1801.08163v2 | http://arxiv.org/pdf/1801.08163v2.pdf | DVQA: Understanding Data Visualizations via Question Answering | Bar charts are an effective way to convey numeric information, but today's
algorithms cannot parse them. Existing methods fail when faced with even minor
variations in appearance. Here, we present DVQA, a dataset that tests many
aspects of bar chart understanding in a question answering framework. Unlike
visual questio... | ['Brian Price', 'Kushal Kafle', 'Scott Cohen', 'Christopher Kanan'] | 2018-01-24 | dvqa-understanding-data-visualizations-via-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Kafle_DVQA_Understanding_Data_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Kafle_DVQA_Understanding_Data_CVPR_2018_paper.pdf | cvpr-2018-6 | ['chart-question-answering', 'chart-question-answering'] | ['computer-code', 'computer-vision'] | [-1.00238122e-01 -9.99586284e-03 -2.12000296e-01 -2.71507382e-01
-1.35582876e+00 -1.25982857e+00 5.31784594e-01 7.76046336e-01
1.20918088e-01 5.59180379e-01 4.87747818e-01 -1.02893186e+00
-1.31477460e-01 -9.41727340e-01 -6.06448472e-01 9.20286924e-02
3.91958430e-02 4.61872101e-01 4.10366744e-01 -4.43882614... | [11.177488327026367, 2.051896810531616] |
3d954392-92cd-46fe-9fcc-f075aea22c5f | is-one-annotation-enough-a-data-centric-image | 2207.06214 | null | https://arxiv.org/abs/2207.06214v3 | https://arxiv.org/pdf/2207.06214v3.pdf | Is one annotation enough? A data-centric image classification benchmark for noisy and ambiguous label estimation | High-quality data is necessary for modern machine learning. However, the acquisition of such data is difficult due to noisy and ambiguous annotations of humans. The aggregation of such annotations to determine the label of an image leads to a lower data quality. We propose a data-centric image classification benchmark ... | ['Reinhard Koch', 'Nina Volkmann', 'Anna Valros', 'Jenny Stracke', 'Matti Pastell', 'Mariusz Oszust', 'Rainer Kiko', 'Sabine Dippel', 'Claudius Zelenka', 'Vasco Grossmann', 'Lars Schmarje'] | 2022-07-13 | null | null | null | null | ['noise-estimation'] | ['medical'] | [ 4.79919940e-01 5.81288226e-02 -3.61441486e-02 -6.53697610e-01
-8.46423686e-01 -5.72537482e-01 5.80465734e-01 6.48989320e-01
-6.51191056e-01 5.48089147e-01 -1.35741979e-01 -3.23169008e-02
-3.00644547e-01 -5.72593510e-01 -6.37720466e-01 -7.78280973e-01
3.15590501e-01 6.67662740e-01 2.82142073e-01 3.07013333... | [9.47148323059082, 3.852074384689331] |
063b2911-f211-49df-8fb0-b0ce099283ab | 3dcfs-fast-and-robust-joint-3d-semantic | 2003.00535 | null | https://arxiv.org/abs/2003.00535v1 | https://arxiv.org/pdf/2003.00535v1.pdf | 3DCFS: Fast and Robust Joint 3D Semantic-Instance Segmentation via Coupled Feature Selection | We propose a novel fast and robust 3D point clouds segmentation framework via coupled feature selection, named 3DCFS, that jointly performs semantic and instance segmentation. Inspired by the human scene perception process, we design a novel coupled feature selection module, named CFSM, that adaptively selects and fuse... | ['Jianfeng Feng', 'xiangyang xue', 'Lili Chen', 'Liang Du', 'Hongkai Wen', 'Jiamao Li', 'Xiaolin Zhang', 'Jingang Tan'] | 2020-03-01 | null | null | null | null | ['3d-semantic-instance-segmentation'] | ['computer-vision'] | [-1.09080382e-01 -2.45628938e-01 3.29089873e-02 -5.36072791e-01
-5.12917757e-01 -4.14227992e-01 5.54949880e-01 1.18907422e-01
-4.08758044e-01 1.32305939e-02 -2.52364427e-01 -1.31864205e-01
-2.99862504e-01 -9.08421457e-01 -5.71471512e-01 -5.88502407e-01
2.15890184e-01 4.06930506e-01 6.33485019e-01 4.08862904... | [7.959840774536133, -3.2862131595611572] |
27936281-9a5b-4528-b618-c5bcade79f72 | neural-fine-tuning-search-for-few-shot | 2306.09295 | null | https://arxiv.org/abs/2306.09295v1 | https://arxiv.org/pdf/2306.09295v1.pdf | Neural Fine-Tuning Search for Few-Shot Learning | In few-shot recognition, a classifier that has been trained on one set of classes is required to rapidly adapt and generalize to a disjoint, novel set of classes. To that end, recent studies have shown the efficacy of fine-tuning with carefully crafted adaptation architectures. However this raises the question of: How ... | ['Timothy Hospedales', 'Da Li', 'Łukasz Dudziak', 'Panagiotis Eustratiadis'] | 2023-06-15 | null | null | null | null | ['architecture-search'] | ['methodology'] | [ 3.37083191e-01 -1.19000509e-01 -7.34231025e-02 -4.49255615e-01
-2.84306556e-01 -6.27002776e-01 5.22803247e-01 -3.20148826e-01
-5.62632561e-01 4.40711170e-01 7.01277284e-03 -1.84888497e-01
-2.51958340e-01 -5.76592982e-01 -7.02170670e-01 -5.37012815e-01
1.12568446e-01 4.37267184e-01 4.09595460e-01 -2.77161986... | [9.196602821350098, 3.05815052986145] |
8a707b50-c30e-4e53-a92b-199fc43860ea | frame-wise-action-representations-for-long | 2203.14957 | null | https://arxiv.org/abs/2203.14957v1 | https://arxiv.org/pdf/2203.14957v1.pdf | Frame-wise Action Representations for Long Videos via Sequence Contrastive Learning | Prior works on action representation learning mainly focus on designing various architectures to extract the global representations for short video clips. In contrast, many practical applications such as video alignment have strong demand for learning dense representations for long videos. In this paper, we introduce a... | ['Deng Cai', 'Chong Li', 'Fangyun Wei', 'Minghao Chen'] | 2022-03-28 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Chen_Frame-Wise_Action_Representations_for_Long_Videos_via_Sequence_Contrastive_Learning_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Chen_Frame-Wise_Action_Representations_for_Long_Videos_via_Sequence_Contrastive_Learning_CVPR_2022_paper.pdf | cvpr-2022-1 | ['video-alignment'] | ['computer-vision'] | [ 3.10416758e-01 -3.81906986e-01 -5.19071877e-01 -4.30514187e-01
-1.07138145e+00 -3.02598357e-01 7.34387219e-01 -1.51775062e-01
-3.98306072e-01 6.09649777e-01 6.70730770e-01 1.90361336e-01
2.22190679e-03 -4.30166870e-01 -9.60225880e-01 -7.13155866e-01
-1.75819263e-01 8.06456357e-02 1.68550193e-01 -5.98375686... | [8.65495491027832, 0.7047462463378906] |
6dd8a36a-7736-43fa-a8e8-4581cd98e015 | forecasting-with-economic-news | 2203.15686 | null | https://arxiv.org/abs/2203.15686v1 | https://arxiv.org/pdf/2203.15686v1.pdf | Forecasting with Economic News | The goal of this paper is to evaluate the informational content of sentiment extracted from news articles about the state of the economy. We propose a fine-grained aspect-based sentiment analysis that has two main characteristics: 1) we consider only the text in the article that is semantically dependent on a term of i... | ['Sebastiano Manzan', 'Sergio Consoli', 'Luca Barbaglia'] | 2022-03-29 | null | null | null | null | ['aspect-based-sentiment-analysis'] | ['natural-language-processing'] | [-4.85442966e-01 -1.88341096e-01 -9.04726505e-01 -3.71533781e-01
-6.53381288e-01 -8.02287161e-01 1.03246820e+00 5.44330776e-01
-3.73034328e-01 5.50908685e-01 1.25431252e+00 -7.06927717e-01
3.61154266e-02 -9.42051113e-01 -4.27325964e-01 -3.05741102e-01
4.05077189e-01 1.94412753e-01 -3.53212714e-01 -6.94777906... | [4.493825912475586, 4.401335716247559] |
087114d8-7d06-4e0c-9e2b-a27ee7a355d1 | video-object-segmentation-with-language | 1803.08006 | null | http://arxiv.org/abs/1803.08006v3 | http://arxiv.org/pdf/1803.08006v3.pdf | Video Object Segmentation with Language Referring Expressions | Most state-of-the-art semi-supervised video object segmentation methods rely
on a pixel-accurate mask of a target object provided for the first frame of a
video. However, obtaining a detailed segmentation mask is expensive and
time-consuming. In this work we explore an alternative way of identifying a
target object, na... | ['Anna Rohrbach', 'Anna Khoreva', 'Bernt Schiele'] | 2018-03-21 | null | null | null | null | ['referring-expression-segmentation'] | ['computer-vision'] | [ 3.25765163e-01 -7.20706163e-03 -4.24236327e-01 -4.85672981e-01
-7.98421562e-01 -7.17975199e-01 7.37564504e-01 1.55317396e-01
-6.01087391e-01 5.35212159e-01 -2.94341385e-01 -6.31490126e-02
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1.90018844e-02 6.49309397e-01 8.18031251e-01 6.58726841... | [9.161745071411133, -0.16196516156196594] |
a067e9d5-d5ff-4640-8383-168389eb9a62 | case-base-neural-networks-survival-analysis | 2301.06535 | null | https://arxiv.org/abs/2301.06535v3 | https://arxiv.org/pdf/2301.06535v3.pdf | Case-Base Neural Networks: survival analysis with time-varying, higher-order interactions | Neural network-based survival methods can model data-driven covariate interactions. While these methods can provide better predictive performance than regression-based approaches, not all can model time-varying interactions and complex baseline hazards. To address this, we propose Case-Base Neural Networks (CBNNs) as a... | ['Sahir Bhatnagar', 'Robert Sladek', 'Maxime Turgeon', 'Jesse Islam'] | 2023-01-16 | null | null | null | null | ['survival-analysis'] | ['miscellaneous'] | [-1.44749269e-01 -2.93741018e-01 -5.08955300e-01 -7.65360117e-01
-9.20624733e-01 4.45901938e-02 4.61578578e-01 3.44939053e-01
-4.62059110e-01 1.13949168e+00 3.97828668e-01 -8.44780922e-01
-4.09033775e-01 -9.29773986e-01 -6.59345567e-01 -5.75430393e-01
-6.67840540e-01 4.74390566e-01 -2.54629493e-01 -2.09388867... | [7.821594715118408, 5.654816150665283] |
e6b01ecf-5026-4324-8efa-18464ea4d436 | deepsat-a-learning-framework-for-satellite | 1509.03602 | null | http://arxiv.org/abs/1509.03602v1 | http://arxiv.org/pdf/1509.03602v1.pdf | DeepSat - A Learning framework for Satellite Imagery | Satellite image classification is a challenging problem that lies at the
crossroads of remote sensing, computer vision, and machine learning. Due to the
high variability inherent in satellite data, most of the current object
classification approaches are not suitable for handling satellite datasets. The
progress of sat... | ['Supratik Mukhopadhyay', 'Manohar Karki', 'Sangram Ganguly', 'Saikat Basu', 'Robert DiBiano', 'Ramakrishna Nemani'] | 2015-09-11 | null | null | null | null | ['satellite-image-classification'] | ['computer-vision'] | [ 3.20792735e-01 -2.96217471e-01 -1.05256885e-01 -5.73766887e-01
-4.14251536e-01 -1.56614646e-01 5.64328134e-01 -4.53706719e-02
-4.74084407e-01 6.75694048e-01 -1.71249136e-02 -2.47565463e-01
-5.82456887e-01 -1.22557437e+00 -3.71807218e-01 -1.09414041e+00
-4.46783602e-01 3.80762249e-01 3.22228819e-02 -4.33515459... | [9.670945167541504, -1.4671649932861328] |
81cd1083-0108-43a7-8d76-d389046bb68a | stickypillars-robust-feature-matching-on | 2002.03983 | null | https://arxiv.org/abs/2002.03983v3 | https://arxiv.org/pdf/2002.03983v3.pdf | StickyPillars: Robust and Efficient Feature Matching on Point Clouds using Graph Neural Networks | Robust point cloud registration in real-time is an important prerequisite for many mapping and localization algorithms. Traditional methods like ICP tend to fail without good initialization, insufficient overlap or in the presence of dynamic objects. Modern deep learning based registration approaches present much bette... | ['Horst-Michael Gross', 'Kai Fischer', 'Martin Simon', 'Florian Oelsner', 'Stefan Milz', 'Patrick Maeder'] | 2020-02-10 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Fischer_StickyPillars_Robust_and_Efficient_Feature_Matching_on_Point_Clouds_Using_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Fischer_StickyPillars_Robust_and_Efficient_Feature_Matching_on_Point_Clouds_Using_CVPR_2021_paper.pdf | cvpr-2021-1 | ['3d-feature-matching'] | ['computer-vision'] | [-2.41503865e-01 -2.86899775e-01 8.62052143e-02 -4.14045185e-01
-1.15218031e+00 -2.64411718e-01 7.04070985e-01 3.55946034e-01
-7.07455695e-01 3.50242406e-01 -2.89189070e-01 -4.07659151e-02
-2.15089738e-01 -8.37000072e-01 -9.66138661e-01 -3.97694558e-01
-1.77949399e-01 1.38326943e+00 5.01844585e-01 -4.12615895... | [7.557592868804932, -2.522120475769043] |
1af913e9-0b08-4fb1-8131-03b7b6b9e2aa | y-net-multi-scale-feature-aggregation-network | 2003.13912 | null | https://arxiv.org/abs/2003.13912v1 | https://arxiv.org/pdf/2003.13912v1.pdf | Y-net: Multi-scale feature aggregation network with wavelet structure similarity loss function for single image dehazing | Single image dehazing is the ill-posed two-dimensional signal reconstruction problem. Recently, deep convolutional neural networks (CNN) have been successfully used in many computer vision problems. In this paper, we propose a Y-net that is named for its structure. This network reconstructs clear images by aggregating ... | ['Yi-Chang James Tsai', 'Chao-Han Huck Yang', 'Hao-Hsiang Yang'] | 2020-03-31 | null | null | null | null | ['wavelet-structure-similarity-loss'] | ['computer-vision'] | [ 1.56444609e-01 -3.90300035e-01 4.07773316e-01 -3.20003569e-01
-6.23675227e-01 5.10001667e-02 2.17397869e-01 -5.22859395e-01
-1.89318955e-01 7.64448643e-01 1.99477062e-01 7.35551119e-02
-3.07615489e-01 -7.42144763e-01 -6.37239099e-01 -1.02173603e+00
-1.68286953e-02 -4.15010482e-01 1.23587005e-01 -1.56541139... | [11.12200927734375, -2.342129945755005] |
ff7ae8db-ba27-4faa-93cb-746ad5363f5a | listening-to-sounds-of-silence-for-speech | 2010.12013 | null | https://arxiv.org/abs/2010.12013v1 | https://arxiv.org/pdf/2010.12013v1.pdf | Listening to Sounds of Silence for Speech Denoising | We introduce a deep learning model for speech denoising, a long-standing challenge in audio analysis arising in numerous applications. Our approach is based on a key observation about human speech: there is often a short pause between each sentence or word. In a recorded speech signal, those pauses introduce a series o... | ['Changxi Zheng', 'Carl Vondrick', 'Yuko Ishiwaka', 'Rundi Wu', 'Ruilin Xu'] | 2020-10-22 | null | http://proceedings.neurips.cc/paper/2020/hash/6d7d394c9d0c886e9247542e06ebb705-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/6d7d394c9d0c886e9247542e06ebb705-Paper.pdf | neurips-2020-12 | ['speech-denoising'] | ['speech'] | [ 5.50573945e-01 1.31122479e-02 2.10610136e-01 -2.98482090e-01
-1.20597494e+00 -7.00762272e-01 4.91217226e-01 1.39160857e-01
-5.49459338e-01 4.16870862e-01 4.45421100e-01 -1.54593036e-01
1.34710595e-02 -4.44695711e-01 -8.04716825e-01 -1.03064728e+00
-2.16025546e-01 -7.32975006e-02 2.11062491e-01 -4.59580064... | [15.141100883483887, 5.810478210449219] |
b0e0d881-1841-4b2a-a04a-5dffe3a8e54e | inferturbo-a-scalable-system-for-boosting | 2307.00228 | null | https://arxiv.org/abs/2307.00228v1 | https://arxiv.org/pdf/2307.00228v1.pdf | InferTurbo: A Scalable System for Boosting Full-graph Inference of Graph Neural Network over Huge Graphs | GNN inference is a non-trivial task, especially in industrial scenarios with giant graphs, given three main challenges, i.e., scalability tailored for full-graph inference on huge graphs, inconsistency caused by stochastic acceleration strategies (e.g., sampling), and the serious redundant computation issue. To address... | ['Jun Zhou', 'Zhiqiang Zhang', 'Lin Wang', 'Binbin Hu', 'Miao Tao', 'Yang Li', 'Zhiyang Hu', 'Xianzheng Song', 'Dalong Zhang'] | 2023-07-01 | null | null | null | null | ['philosophy'] | ['miscellaneous'] | [-1.79790616e-01 9.27110761e-02 -1.36534736e-01 -1.75015181e-01
4.31565475e-03 -4.09774601e-01 2.02401340e-01 5.63123152e-02
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-3.40261638e-01 -1.59985864e+00 -5.85595608e-01 -6.78665459e-01
-3.76349390e-01 1.03933299e+00 5.85555077e-01 -1.56727508... | [7.021779537200928, 5.846648216247559] |
bdb2d339-498e-415c-8948-cd90ba5b660f | efficient-transformer-based-speech | 2206.11703 | null | https://arxiv.org/abs/2206.11703v1 | https://arxiv.org/pdf/2206.11703v1.pdf | Efficient Transformer-based Speech Enhancement Using Long Frames and STFT Magnitudes | The SepFormer architecture shows very good results in speech separation. Like other learned-encoder models, it uses short frames, as they have been shown to obtain better performance in these cases. This results in a large number of frames at the input, which is problematic; since the SepFormer is transformer-based, it... | ['Timo Gerkmann', 'Tal Peer', 'Danilo de Oliveira'] | 2022-06-23 | null | null | null | null | ['speech-separation'] | ['speech'] | [ 3.06994826e-01 -5.80277182e-02 3.15560460e-01 -2.01446503e-01
-1.11431575e+00 -2.74552047e-01 4.13679391e-01 5.29143326e-02
-5.63676238e-01 6.52072430e-01 4.95549262e-01 -4.96130258e-01
-4.42072153e-02 -3.78077745e-01 -3.16663653e-01 -8.39664459e-01
-7.88136572e-02 -3.93857688e-01 2.54116505e-01 2.10769325... | [15.147876739501953, 5.953787803649902] |
6ccc7787-a210-4dd6-b6f1-df2c145a31f8 | semeval-2021-task-7-hahackathon-detecting-and | null | null | https://aclanthology.org/2021.semeval-1.9 | https://aclanthology.org/2021.semeval-1.9.pdf | SemEval 2021 Task 7: HaHackathon, Detecting and Rating Humor and Offense | SemEval 2021 Task 7, HaHackathon, was the first shared task to combine the previously separate domains of humor detection and offense detection. We collected 10,000 texts from Twitter and the Kaggle Short Jokes dataset, and had each annotated for humor and offense by 20 annotators aged 18-70. Our subtasks were binary h... | ['Walid Magdy', 'Adam Lopez', 'Luis Chiruzzo', 'Steven Wilson', 'J. A. Meaney'] | 2021-08-01 | null | null | null | semeval-2021 | ['humor-detection'] | ['natural-language-processing'] | [-3.64026278e-01 1.23370670e-01 5.54173216e-02 1.07863313e-02
-6.20445311e-01 -6.28265977e-01 7.59774566e-01 2.25093573e-01
-3.64157081e-01 8.44727576e-01 6.97875857e-01 -2.57952243e-01
5.02451360e-01 -4.85971004e-01 -1.92032039e-01 -2.21757933e-01
2.31319457e-01 7.04396546e-01 1.38091400e-01 -7.38997221... | [8.876900672912598, 11.075698852539062] |
7d7c8261-234f-43e1-bea6-c7342b47c0e6 | distributed-deep-reinforcement-learning-learn | 1801.02852 | null | http://arxiv.org/abs/1801.02852v2 | http://arxiv.org/pdf/1801.02852v2.pdf | Distributed Deep Reinforcement Learning: Learn how to play Atari games in 21 minutes | We present a study in Distributed Deep Reinforcement Learning (DDRL) focused
on scalability of a state-of-the-art Deep Reinforcement Learning algorithm
known as Batch Asynchronous Advantage ActorCritic (BA3C). We show that using
the Adam optimization algorithm with a batch size of up to 2048 is a viable
choice for carr... | ['Adam Jędrych', 'Igor Adamski', 'Kamil Kaczmarek', 'Henryk Michalewski', 'Tomasz Grel', 'Robert Adamski'] | 2018-01-09 | null | null | null | null | ['2048'] | ['playing-games'] | [-6.13025129e-01 3.33911568e-01 9.00969803e-02 -8.59919935e-02
-5.83516896e-01 -3.61554712e-01 5.57762861e-01 4.01519299e-01
-1.28288317e+00 9.66755450e-01 -2.25895017e-01 -6.32423341e-01
1.27072290e-01 -6.62404120e-01 -7.94142008e-01 -9.72930491e-01
-5.03811955e-01 6.85762346e-01 2.55063772e-02 -2.08841175... | [4.0098042488098145, 1.7035598754882812] |
1dc3be7a-8f61-48da-b7a2-17438663cad2 | embedding-synthetic-off-policy-experience-for | 2212.01375 | null | https://arxiv.org/abs/2212.01375v1 | https://arxiv.org/pdf/2212.01375v1.pdf | Embedding Synthetic Off-Policy Experience for Autonomous Driving via Zero-Shot Curricula | ML-based motion planning is a promising approach to produce agents that exhibit complex behaviors, and automatically adapt to novel environments. In the context of autonomous driving, it is common to treat all available training data equally. However, this approach produces agents that do not perform robustly in safety... | ['Shimon Whiteson', 'Payam Nikdel', "Matthew O'Kelly", 'Aman Sinha', 'Supratik Paul', 'Sirish Srinivasan', 'Eli Bronstein'] | 2022-12-02 | null | null | null | null | ['motion-planning'] | ['robots'] | [ 1.99530482e-01 5.77958286e-01 5.52776121e-02 -3.36110294e-01
-6.84270799e-01 -6.70678794e-01 5.81186593e-01 8.33584741e-02
-7.85293102e-01 8.72065246e-01 -1.89553034e-02 -4.57403183e-01
-5.80640957e-02 -9.11044002e-01 -1.05492306e+00 -4.03527677e-01
-1.31077051e-01 9.90236521e-01 5.69983482e-01 -7.19259381... | [5.015110492706299, 1.1888312101364136] |
1b088b97-30d2-4f35-bec2-0e57dfcc0eef | deep-label-distribution-learning-with-label | 1611.01731 | null | http://arxiv.org/abs/1611.01731v2 | http://arxiv.org/pdf/1611.01731v2.pdf | Deep Label Distribution Learning with Label Ambiguity | Convolutional Neural Networks (ConvNets) have achieved excellent recognition
performance in various visual recognition tasks. A large labeled training set
is one of the most important factors for its success. However, it is difficult
to collect sufficient training images with precise labels in some domains such
as appa... | ['Xin Geng', 'Chen-Wei Xie', 'Bin-Bin Gao', 'Chao Xing', 'Jianxin Wu'] | 2016-11-06 | null | null | null | null | ['head-pose-estimation'] | ['computer-vision'] | [ 7.54966512e-02 5.81927858e-02 -3.59266967e-01 -1.01691806e+00
-6.22002780e-01 -2.55263537e-01 1.75442925e-04 7.65251368e-02
-7.20266521e-01 6.39065742e-01 -1.41063586e-01 2.32991651e-01
-5.49425595e-02 -3.48404557e-01 -5.05953610e-01 -9.56298232e-01
3.75747949e-01 7.53181517e-01 -2.73605622e-03 5.39131343... | [13.581055641174316, 0.8592618107795715] |
a42faa4c-18f8-4a67-9d74-be9279418c6f | alibabas-submission-for-the-wmt-2020-ape | null | null | https://aclanthology.org/2020.wmt-1.84 | https://aclanthology.org/2020.wmt-1.84.pdf | Alibaba’s Submission for the WMT 2020 APE Shared Task: Improving Automatic Post-Editing with Pre-trained Conditional Cross-Lingual BERT | The goal of Automatic Post-Editing (APE) is basically to examine the automatic methods for correcting translation errors generated by an unknown machine translation (MT) system. This paper describes Alibaba’s submissions to the WMT 2020 APE Shared Task for the English-German language pair. We design a two-stage trainin... | ['Yu Zhao', 'Yangbin Shi', 'Xin Ge', 'Jun Lu', 'Yuqi Zhang', 'Kai Fan', 'Ke Wang', 'Jiayi Wang'] | null | null | null | null | wmt-emnlp-2020-11 | ['automatic-post-editing', 'automatic-post-editing'] | ['computer-vision', 'natural-language-processing'] | [ 3.90410781e-01 2.57674873e-01 -5.55343702e-02 -2.95506030e-01
-1.51692545e+00 -6.28049135e-01 7.97000229e-01 -1.88839629e-01
-6.78234279e-01 9.93281364e-01 6.06295057e-02 -6.33555830e-01
6.49455845e-01 -1.22447275e-01 -1.35462248e+00 -2.80282944e-01
2.89557487e-01 8.01748216e-01 -5.64322956e-02 -3.24866921... | [11.649972915649414, 10.283778190612793] |
39e2777e-7a8b-468d-a743-202490015910 | a-medical-semantic-assisted-transformer-for | 2208.10358 | null | https://arxiv.org/abs/2208.10358v1 | https://arxiv.org/pdf/2208.10358v1.pdf | A Medical Semantic-Assisted Transformer for Radiographic Report Generation | Automated radiographic report generation is a challenging cross-domain task that aims to automatically generate accurate and semantic-coherence reports to describe medical images. Despite the recent progress in this field, there are still many challenges at least in the following aspects. First, radiographic images are... | ['Luping Zhou', 'Xiu Li', 'Lei Wang', 'Mingkang Tang', 'Zhanyu Wang'] | 2022-08-22 | null | null | null | null | ['medical-report-generation'] | ['medical'] | [ 4.76170510e-01 2.77255058e-01 -2.35869139e-01 -5.15505970e-01
-1.39661539e+00 -4.22006473e-02 5.45469224e-01 2.42624223e-01
-3.93058360e-02 8.61026406e-01 8.36958885e-01 -5.23749851e-02
-1.66922826e-02 -7.16891110e-01 -8.39674473e-01 -6.42691255e-01
2.41320640e-01 3.43575537e-01 4.96238023e-02 -1.40289351... | [15.030987739562988, -1.4269849061965942] |
fd4578de-d370-4776-a669-aba24638923c | unveiling-the-three-dimensional-spin-texture | 2101.12630 | null | https://arxiv.org/abs/2101.12630v1 | https://arxiv.org/pdf/2101.12630v1.pdf | Unveiling the three-dimensional spin texture of skyrmion tubes | Magnetic skyrmions are stable topological solitons with complex non-coplanar spin structures. Their nanoscopic size and the low electric currents required to initiate and control their motion has opened a new field of research, skyrmionics, that aims at using skyrmions as information carriers for data storage and manip... | ['Axel Lubk', 'Bernd Rellinghaus', 'Bernd Büchner', 'Rafal E. Dunin-Borkowski', 'Marcus Schmidt', 'András Kovács', 'Ulrich K. Rößler', 'Sebastian Schneider', 'Daniel Wolf'] | 2021-01-29 | null | null | null | null | ['electron-tomography'] | ['medical'] | [ 2.58181363e-01 -4.24682721e-02 -4.21516076e-02 -1.42839879e-01
2.83785313e-02 -2.97972083e-01 7.26487637e-01 -5.23814797e-01
-4.57572252e-01 9.08475578e-01 -2.44841608e-03 -2.11215064e-01
-2.28550911e-01 -8.11974466e-01 -6.35118246e-01 -1.50046909e+00
-4.47007269e-02 1.11068869e+00 4.79389220e-01 -4.85489786... | [5.498984336853027, 4.828834056854248] |
a51eb797-d75b-4244-b3c3-560a39817b0b | biomedical-named-entity-recognition-at-scale | 2011.06315 | null | https://arxiv.org/abs/2011.06315v1 | https://arxiv.org/pdf/2011.06315v1.pdf | Biomedical Named Entity Recognition at Scale | Named entity recognition (NER) is a widely applicable natural language processing task and building block of question answering, topic modeling, information retrieval, etc. In the medical domain, NER plays a crucial role by extracting meaningful chunks from clinical notes and reports, which are then fed to downstream t... | ['David Talby', 'Veysel Kocaman'] | 2020-11-12 | null | null | null | null | ['medical-named-entity-recognition'] | ['natural-language-processing'] | [-2.36253858e-01 1.93998173e-01 -1.44228652e-01 -3.97222102e-01
-9.93907630e-01 -2.66144007e-01 1.39504477e-01 9.15702105e-01
-9.21429694e-01 8.97270441e-01 3.75101537e-01 -5.41230321e-01
3.96674536e-02 -9.40751791e-01 -5.10759234e-01 -6.05615795e-01
-2.29411557e-01 5.17791092e-01 2.24154428e-01 9.11395811... | [8.505779266357422, 8.826972007751465] |
c76839f5-f3f4-454e-9699-3d51280483ca | domain-independent-abstract-generation-for | null | null | https://aclanthology.org/P13-1137 | https://aclanthology.org/P13-1137.pdf | Domain-Independent Abstract Generation for Focused Meeting Summarization | null | ['Lu Wang', 'Claire Cardie'] | 2013-08-01 | null | null | null | acl-2013-8 | ['meeting-summarization'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.2955522537231445, 3.680513381958008] |
0db46695-abd6-4924-83b6-9b182babef55 | scalable-bilevel-optimization-for-generating | 2304.10912 | null | https://arxiv.org/abs/2304.10912v1 | https://arxiv.org/pdf/2304.10912v1.pdf | Scalable Bilevel Optimization for Generating Maximally Representative OPF Datasets | New generations of power systems, containing high shares of renewable energy resources, require improved data-driven tools which can swiftly adapt to changes in system operation. Many of these tools, such as ones using machine learning, rely on high-quality training datasets to construct probabilistic models. Such mode... | ['Samuel Chevalier', 'Ignasi Ventura Nadal'] | 2023-04-21 | null | null | null | null | ['bilevel-optimization'] | ['methodology'] | [-2.61450291e-01 -3.49607795e-01 -5.74025214e-01 -2.89431930e-01
-6.82675064e-01 -7.86950588e-01 2.07457051e-01 2.72367865e-01
2.80246586e-01 1.34444416e+00 -3.05323780e-01 -4.52552021e-01
-6.68890238e-01 -8.38255167e-01 -1.92694709e-01 -9.74610209e-01
-3.64092231e-01 8.39208722e-01 -2.12254092e-01 -2.57915556... | [5.772492408752441, 2.625206470489502] |
9c1240a4-b90e-4382-a016-a1a9c22224a9 | cross-domain-contract-element-extraction-with | 2105.06083 | null | https://arxiv.org/abs/2105.06083v1 | https://arxiv.org/pdf/2105.06083v1.pdf | Cross-Domain Contract Element Extraction with a Bi-directional Feedback Clause-Element Relation Network | Contract element extraction (CEE) is the novel task of automatically identifying and extracting legally relevant elements such as contract dates, payments, and legislation references from contracts. Automatic methods for this task view it as a sequence labeling problem and dramatically reduce human labor. However, as c... | ['Maarten de Rijke', 'Hongsong Li', 'Xiaozhong Liu', 'Zhumin Chen', 'Pengjie Ren', 'Zhaochun Ren', 'Hongye Song', 'Zihan Wang'] | 2021-05-13 | null | null | null | null | ['cross-domain-named-entity-recognition'] | ['natural-language-processing'] | [ 2.86495447e-01 1.65162906e-01 -4.91681427e-01 -6.83627784e-01
-1.04017055e+00 -9.06028390e-01 4.62611973e-01 -2.28470396e-02
-4.73005205e-01 7.93695211e-01 4.76398855e-01 -5.40680647e-01
1.04966305e-01 -8.82416904e-01 -7.23213851e-01 -2.36925468e-01
9.37854573e-02 8.27859759e-01 1.79652900e-01 -4.04245883... | [9.337051391601562, 8.881819725036621] |
51f50f2f-3ef4-46e3-9043-f5b728a757f9 | deep-speech-2-end-to-end-speech-recognition | 1512.02595 | null | http://arxiv.org/abs/1512.02595v1 | http://arxiv.org/pdf/1512.02595v1.pdf | Deep Speech 2: End-to-End Speech Recognition in English and Mandarin | We show that an end-to-end deep learning approach can be used to recognize
either English or Mandarin Chinese speech--two vastly different languages.
Because it replaces entire pipelines of hand-engineered components with neural
networks, end-to-end learning allows us to handle a diverse variety of speech
including noi... | ['Sherjil Ozair', 'Billy Jun', 'Jesse Engel', 'Erich Elsen', 'Jingdong Chen', 'Jared Casper', 'Greg Diamos', 'David Seetapun', 'Bo Xiao', 'Awni Hannun', 'Andrew Ng', 'Sharan Narang', 'Eric Battenberg', 'Christopher Fougner', 'Adam Coates', 'Zhiqian Wang', 'Zhenyao Zhu', 'Tony Han', 'Patrick LeGresley', 'Mike Chrzanowsk... | 2015-12-08 | null | null | null | null | ['noisy-speech-recognition', 'accented-speech-recognition'] | ['speech', 'speech'] | [-3.23258311e-01 -3.75989974e-01 4.50718671e-01 -4.05211210e-01
-1.23438776e+00 -8.83179426e-01 3.28856975e-01 -2.91632473e-01
-4.94328886e-01 2.35110179e-01 1.88910246e-01 -8.03321242e-01
3.65860939e-01 -3.26845467e-01 -5.51109731e-01 -4.99139279e-01
-9.10499468e-02 5.87563694e-01 2.57568300e-01 -1.06556527... | [14.289448738098145, 6.491180419921875] |
cf6ff13e-fdff-480d-985b-149cfaf47551 | hyphen-hyperbolic-hawkes-attention-for-text | null | null | https://aclanthology.org/2022.acl-short.69 | https://aclanthology.org/2022.acl-short.69.pdf | HYPHEN: Hyperbolic Hawkes Attention For Text Streams | Analyzing the temporal sequence of texts from sources such as social media, news, and parliamentary debates is a challenging problem as it exhibits time-varying scale-free properties and fine-grained timing irregularities. We propose a Hyperbolic Hawkes Attention Network (HYPHEN), which learns a data-driven hyperbolic ... | ['Sudheer Chava', 'Ritesh Soun', 'Sanchit Ahuja', 'Ramit Sawhney', 'Shivam Agarwal'] | null | null | null | null | acl-2022-5 | ['stock-price-prediction'] | ['time-series'] | [-6.20617509e-01 2.28976727e-01 1.53639823e-01 5.33468127e-02
-8.82982194e-01 -8.85092974e-01 1.06171262e+00 4.49430615e-01
-3.02018672e-01 5.24359763e-01 7.30692685e-01 -8.81471753e-01
-4.28772897e-01 -8.36762249e-01 -4.54514980e-01 -5.90656638e-01
-3.51081103e-01 8.32873642e-01 9.44363773e-02 -4.35296774... | [6.91347599029541, 3.4296388626098633] |
910cefb6-9832-4c47-b67b-b2a40f57e7be | polarmix-a-general-data-augmentation | 2208.00223 | null | https://arxiv.org/abs/2208.00223v1 | https://arxiv.org/pdf/2208.00223v1.pdf | PolarMix: A General Data Augmentation Technique for LiDAR Point Clouds | LiDAR point clouds, which are usually scanned by rotating LiDAR sensors continuously, capture precise geometry of the surrounding environment and are crucial to many autonomous detection and navigation tasks. Though many 3D deep architectures have been developed, efficient collection and annotation of large amounts of ... | ['Ling Shao', 'Shijian Lu', 'Kaiwen Cui', 'Dayan Guan', 'Jiaxing Huang', 'Aoran Xiao'] | 2022-07-30 | null | null | null | null | ['lidar-semantic-segmentation'] | ['computer-vision'] | [ 2.35525981e-01 -4.31982368e-01 1.92697309e-02 -7.14465082e-01
-5.82806706e-01 -8.03955913e-01 6.79874659e-01 3.07351023e-01
-3.45676988e-01 2.09027693e-01 -4.43880051e-01 -2.99547791e-01
3.79113629e-02 -9.23614204e-01 -9.61347342e-01 -5.14450967e-01
1.27750158e-01 1.17311323e+00 5.60494661e-01 -2.45573267... | [7.970095157623291, -2.9776296615600586] |
6068e0be-5164-4b47-aa96-d74560faf841 | cp3-unifying-point-cloud-completion-by | 2207.05359 | null | https://arxiv.org/abs/2207.05359v2 | https://arxiv.org/pdf/2207.05359v2.pdf | CP3: Unifying Point Cloud Completion by Pretrain-Prompt-Predict Paradigm | Point cloud completion aims to predict complete shape from its partial observation. Current approaches mainly consist of generation and refinement stages in a coarse-to-fine style. However, the generation stage often lacks robustness to tackle different incomplete variations, while the refinement stage blindly recovers... | ['Tong He', 'Yu Qiao', 'Yihao Liu', 'Yali Wang', 'Mingye Xu'] | 2022-07-12 | null | null | null | null | ['point-cloud-completion', 'point-cloud-generation'] | ['computer-vision', 'computer-vision'] | [ 3.98850322e-01 2.22414643e-01 -6.63758293e-02 -4.19920653e-01
-1.08389080e+00 -5.84795237e-01 7.91690528e-01 -7.97730535e-02
-7.46554807e-02 2.80162454e-01 2.71527059e-02 -8.96074995e-02
8.16620737e-02 -8.63761604e-01 -1.16353738e+00 -3.93838227e-01
4.97352749e-01 8.39446604e-01 3.60985726e-01 -1.98347494... | [8.243673324584961, -3.4876935482025146] |
78a0f5c4-53e2-4012-b88e-fee96efad54c | multi-scale-2d-representation-learning-for | 2111.02741 | null | https://arxiv.org/abs/2111.02741v1 | https://arxiv.org/pdf/2111.02741v1.pdf | Multi-scale 2D Representation Learning for weakly-supervised moment retrieval | Video moment retrieval aims to search the moment most relevant to a given language query. However, most existing methods in this community often require temporal boundary annotations which are expensive and time-consuming to label. Hence weakly supervised methods have been put forward recently by only using coarse vide... | ['Wensheng Zhang', 'Zhizhong Zhang', 'Yongqiang Tang', 'Rui Wu', 'Ding Li'] | 2021-11-04 | null | null | null | null | ['moment-retrieval'] | ['computer-vision'] | [-1.53492436e-01 -4.97059643e-01 -6.73709571e-01 -3.85157138e-01
-1.11453187e+00 -5.89952826e-01 7.63123751e-01 2.90144205e-01
-5.34686208e-01 3.11221510e-01 3.91774356e-01 3.27477872e-01
-3.96868177e-02 -4.12900388e-01 -7.24550426e-01 -5.82164347e-01
-4.01941508e-01 3.11492264e-01 7.07858443e-01 8.82129967... | [10.029834747314453, 0.6922467350959778] |
02ef1705-613f-444f-a114-51a556ca78e8 | using-motif-transitions-for-temporal-graph | 2306.11190 | null | https://arxiv.org/abs/2306.11190v1 | https://arxiv.org/pdf/2306.11190v1.pdf | Using Motif Transitions for Temporal Graph Generation | Graph generative models are highly important for sharing surrogate data and benchmarking purposes. Real-world complex systems often exhibit dynamic nature, where the interactions among nodes change over time in the form of a temporal network. Most temporal network generation models extend the static graph generation mo... | ['A. Erdem Sarıyüce', 'Penghang Liu'] | 2023-06-19 | null | null | null | null | ['benchmarking', 'benchmarking'] | ['miscellaneous', 'robots'] | [ 1.73703209e-01 -3.02451421e-02 -2.94122189e-01 1.04801014e-01
-2.59557292e-02 -7.50372469e-01 1.15357971e+00 2.42387041e-01
1.66394666e-01 7.34308362e-01 1.04330599e-01 -5.00072598e-01
-2.10288286e-01 -1.30872822e+00 -6.58165216e-01 -3.96939009e-01
-7.79357016e-01 6.57525778e-01 6.72042370e-01 -1.60411060... | [7.218002796173096, 5.876262187957764] |
dab6228e-38f7-4d51-9bf4-c18bb8708040 | deep-multi-task-model-for-sarcasm-detection | 2106.12488 | null | https://arxiv.org/abs/2106.12488v1 | https://arxiv.org/pdf/2106.12488v1.pdf | Deep Multi-Task Model for Sarcasm Detection and Sentiment Analysis in Arabic Language | The prominence of figurative language devices, such as sarcasm and irony, poses serious challenges for Arabic Sentiment Analysis (SA). While previous research works tackle SA and sarcasm detection separately, this paper introduces an end-to-end deep Multi-Task Learning (MTL) model, allowing knowledge interaction betwee... | ['Ahmed Khoumsi', 'Ismail Berrada', 'Nabil El Mamoun', 'Kabil Essefar', 'Abdellah El Mekki', 'Abdelkader El Mahdaouy'] | 2021-06-23 | null | https://aclanthology.org/2021.wanlp-1.42 | https://aclanthology.org/2021.wanlp-1.42.pdf | eacl-wanlp-2021-4 | ['arabic-sentiment-analysis'] | ['natural-language-processing'] | [-2.55359292e-01 1.26576900e-01 -5.65218478e-02 -5.59344351e-01
-8.43928277e-01 -2.76901990e-01 7.65774310e-01 6.13148883e-02
-4.66188520e-01 2.78052658e-01 4.79181081e-01 -1.21243797e-01
5.91108620e-01 -3.36191863e-01 -5.74169338e-01 -4.33026582e-01
4.14598018e-01 4.46734905e-01 -1.71903670e-01 -8.19104254... | [9.166670799255371, 10.507304191589355] |
5efddd97-016f-493e-8114-ecf75f593cab | consistent-rank-logits-for-ordinal-regression | 1901.07884 | null | https://arxiv.org/abs/1901.07884v7 | https://arxiv.org/pdf/1901.07884v7.pdf | Rank consistent ordinal regression for neural networks with application to age estimation | In many real-world prediction tasks, class labels include information about the relative ordering between labels, which is not captured by commonly-used loss functions such as multi-category cross-entropy. Recently, the deep learning community adopted ordinal regression frameworks to take such ordering information into... | ['Sebastian Raschka', 'Vahid Mirjalili', 'Wenzhi Cao'] | 2019-01-20 | null | null | null | null | ['age-and-gender-classification', 'gender-prediction'] | ['computer-vision', 'computer-vision'] | [ 2.23297656e-01 3.72106761e-01 -7.63269961e-01 -1.12820041e+00
-5.50833464e-01 -1.60606995e-01 6.63173556e-01 3.98820460e-01
-3.49811286e-01 9.96681154e-01 -9.67050809e-03 1.53025938e-02
-8.68655205e-01 -6.65894806e-01 -5.39625585e-01 -4.63149846e-01
-3.36260617e-01 7.24591792e-01 -3.65367174e-01 2.70707846... | [9.177706718444824, 4.004767417907715] |
78329304-3910-41f3-9417-c85f065387f1 | temporal-variation-measure-analysis-an | 2212.08369 | null | https://arxiv.org/abs/2212.08369v1 | https://arxiv.org/pdf/2212.08369v1.pdf | Temporal Variation Measure Analysis: An Improved Second-Order Difference Plot | In this study, an improved second-order difference plot is proposed to analyze the variability of heart rate variability. Although the variation of physiological status of cardiovascular system can be shown graphically by the second-order difference plot, the descriptive ability of existing indicators for this plot is ... | ['Ning Cai', 'Chen Diao'] | 2022-12-16 | null | null | null | null | ['heart-rate-variability'] | ['medical'] | [-2.37105876e-01 -4.19931859e-01 -1.13852561e-01 -2.90504456e-01
2.10714340e-01 -2.50279933e-01 -7.25991875e-02 2.82148659e-01
3.53625938e-02 6.34375036e-01 -7.73139969e-02 -4.66987997e-01
-5.33308864e-01 -4.28973377e-01 2.60246068e-01 -7.78465450e-01
-2.99859762e-01 -2.83354878e-01 -7.82794785e-03 -1.51619166... | [14.02756404876709, 3.0535356998443604] |
a72eb98b-798b-4ecb-8982-9b9c65c2b395 | improving-arabic-diacritization-through | null | null | https://aclanthology.org/D15-1152 | https://aclanthology.org/D15-1152.pdf | Improving Arabic Diacritization through Syntactic Analysis | null | ['Salam Khalifa', 'Anas Shahrour', 'Nizar Habash'] | 2015-09-01 | null | null | null | emnlp-2015-9 | ['morphological-tagging'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.2474775314331055, 3.6305480003356934] |
86dfc48c-b248-4f1d-bfd7-c6aff9c0efbd | multi-task-deep-neural-networks-in-automated | 1705.04802 | null | http://arxiv.org/abs/1705.04802v2 | http://arxiv.org/pdf/1705.04802v2.pdf | Multi-task Deep Neural Networks in Automated Protein Function Prediction | In recent years, deep learning algorithms have outperformed the state-of-the
art methods in several areas thanks to the efficient methods for training and
for preventing overfitting, advancement in computer hardware, the availability
of vast amount data. The high performance of multi-task deep neural networks in
drug d... | [] | 2017-05-28 | null | null | null | null | ['protein-function-prediction'] | ['medical'] | [-1.82843357e-01 -1.23603776e-01 1.60111949e-01 -4.75955874e-01
-3.64495575e-01 -2.14452431e-01 -8.32602680e-02 3.36398035e-01
-3.80953014e-01 1.16317952e+00 -5.41678891e-02 -2.92009741e-01
-3.74533594e-01 -9.96577561e-01 -9.53674495e-01 -1.08615756e+00
-2.06527710e-01 4.70377803e-01 3.39273006e-01 -3.52411062... | [4.879788875579834, 5.649763107299805] |
8ea73c2b-4b19-4b48-8b6f-748200dd01df | unsupervised-domain-adaptation-for-semantic-2 | 2109.08912 | null | https://arxiv.org/abs/2109.08912v1 | https://arxiv.org/pdf/2109.08912v1.pdf | Unsupervised Domain Adaptation for Semantic Segmentation via Low-level Edge Information Transfer | Unsupervised domain adaptation for semantic segmentation aims to make models trained on synthetic data (source domain) adapt to real images (target domain). Previous feature-level adversarial learning methods only consider adapting models on the high-level semantic features. However, the large domain gap between source... | ['Bo Du', 'Yonghao Xu', 'Chen Wu', 'Hongruixuan Chen'] | 2021-09-18 | null | null | null | null | ['synthetic-to-real-translation'] | ['computer-vision'] | [ 7.02035189e-01 4.89991397e-01 -2.23926693e-01 -5.28850198e-01
-7.20007956e-01 -4.12459046e-01 4.21869844e-01 -1.92986771e-01
-2.91070819e-01 7.49193549e-01 4.82804440e-02 6.30190670e-02
3.11027050e-01 -1.06201315e+00 -9.84608114e-01 -5.37113607e-01
3.41186613e-01 3.90124023e-01 5.05848408e-01 -1.45811498... | [9.76917552947998, 1.4436546564102173] |
ef5326dc-088c-418e-bda5-76f7edc64d81 | using-natural-language-processing-and-2 | 2306.09737 | null | https://arxiv.org/abs/2306.09737v1 | https://arxiv.org/pdf/2306.09737v1.pdf | Using Natural Language Processing and Networks to Automate Structured Literature Reviews: An Application to Farmers Climate Change Adaptation | The fast-growing number of research articles makes it problematic for scholars to keep track of the new findings related to their areas of expertise. Furthermore, linking knowledge across disciplines in rapidly developing fields becomes challenging for complex topics like climate change that demand interdisciplinary so... | ['Tatiana Filatova', 'Sofia Gil-Clavel'] | 2023-06-16 | null | null | null | null | ['text-summarization'] | ['natural-language-processing'] | [ 4.02188867e-01 1.39553964e-01 -5.97586334e-01 1.84751943e-01
1.79316834e-01 -8.44877243e-01 7.34968901e-01 1.15290654e+00
-5.09862363e-01 6.03642166e-01 5.64415216e-01 -1.02447522e+00
-5.69392383e-01 -1.06632054e+00 -4.89290416e-01 -5.21203242e-02
4.86831255e-02 2.50742841e-03 -1.59804404e-01 -3.24640900... | [9.51965618133545, 8.125582695007324] |
00143df5-d47f-489f-a7a2-45bb3a62eb9c | quantization-aware-and-tensor-compressed | 2306.01076 | null | https://arxiv.org/abs/2306.01076v2 | https://arxiv.org/pdf/2306.01076v2.pdf | Quantization-Aware and Tensor-Compressed Training of Transformers for Natural Language Understanding | Fine-tuned transformer models have shown superior performances in many natural language tasks. However, the large model size prohibits deploying high-performance transformer models on resource-constrained devices. This paper proposes a quantization-aware tensor-compressed training approach to reduce the model size, ari... | ['Zheng Zhang', 'Siegfried Kunzmann', 'Samridhi Choudhary', 'Zi Yang'] | 2023-06-01 | null | null | null | null | ['quantization'] | ['methodology'] | [ 6.56293556e-02 1.38843851e-02 -2.19168603e-01 -7.14041829e-01
-8.80500555e-01 -3.34452927e-01 3.11990678e-01 1.75279915e-01
-4.01614547e-01 2.46792987e-01 4.31896038e-02 -7.13854432e-01
-2.66848300e-02 -9.20424402e-01 -9.47283864e-01 -4.48052377e-01
-8.48356113e-02 8.00851166e-01 1.64009005e-01 -8.53598956... | [8.727134704589844, 3.5949342250823975] |
66281cc1-9f78-4e5c-b9bc-66920761fc83 | action-recognition-with-multi-stream-motion | 2306.07576 | null | https://arxiv.org/abs/2306.07576v1 | https://arxiv.org/pdf/2306.07576v1.pdf | Action Recognition with Multi-stream Motion Modeling and Mutual Information Maximization | Action recognition has long been a fundamental and intriguing problem in artificial intelligence. The task is challenging due to the high dimensionality nature of an action, as well as the subtle motion details to be considered. Current state-of-the-art approaches typically learn from articulated motion sequences in th... | ['Kui Ren', 'Zhibo Wang', 'Shuang Wu', 'Beibei Zhang', 'Yingda Lyu', 'Zhenguang Liu', 'Haipeng Chen', 'Yuheng Yang'] | 2023-06-13 | null | null | null | null | ['action-recognition-in-videos'] | ['computer-vision'] | [ 2.89564699e-01 -3.13225299e-01 -4.62266654e-01 -2.18762368e-01
-6.33433342e-01 -3.96024227e-01 7.00695515e-01 -2.77600914e-01
-5.67322493e-01 4.55154955e-01 6.72323823e-01 3.71744037e-02
-1.01749681e-01 -4.69330519e-01 -7.59176910e-01 -8.72212350e-01
-2.61884868e-01 5.12815006e-02 5.98472543e-02 -3.43787044... | [7.922214984893799, 0.25923222303390503] |
28223de2-87a1-4456-8d62-280cd68a50bf | dcp-nas-discrepant-child-parent-neural | 2306.15390 | null | https://arxiv.org/abs/2306.15390v1 | https://arxiv.org/pdf/2306.15390v1.pdf | DCP-NAS: Discrepant Child-Parent Neural Architecture Search for 1-bit CNNs | Neural architecture search (NAS) proves to be among the effective approaches for many tasks by generating an application-adaptive neural architecture, which is still challenged by high computational cost and memory consumption. At the same time, 1-bit convolutional neural networks (CNNs) with binary weights and activat... | ['Guodong Guo', 'Tian Wang', 'Baochang Zhang', "Li'an Zhuo", 'Xianbin Cao', 'Sheng Xu', 'Yanjing Li'] | 2023-06-27 | null | null | null | null | ['person-re-identification', 'architecture-search'] | ['computer-vision', 'methodology'] | [ 9.43092406e-02 -2.15023220e-01 -2.54560530e-01 -4.65514779e-01
-2.50359148e-01 -3.91329318e-01 4.11553979e-02 -3.51345628e-01
-7.41378605e-01 2.97930270e-01 -2.98332423e-01 -3.06400537e-01
-1.56464159e-01 -8.12463462e-01 -6.90068424e-01 -7.56970406e-01
2.08774254e-01 1.35801390e-01 7.25436881e-02 -2.20745474... | [8.629400253295898, 2.962913751602173] |
c906fa3a-2c49-40ea-b8e7-b5919e46166b | classification-of-brain-tumours-in-mr-images | 2105.14071 | null | https://arxiv.org/abs/2105.14071v2 | https://arxiv.org/pdf/2105.14071v2.pdf | Classification of Brain Tumours in MR Images using Deep Spatiospatial Models | A brain tumour is a mass or cluster of abnormal cells in the brain, which has the possibility of becoming life-threatening because of its ability to invade neighbouring tissues and also form metastases. An accurate diagnosis is essential for successful treatment planning and magnetic resonance imaging is the principal ... | ['Oliver Speck', 'Andreas Nürnberger', 'Faraz Ahmed Nizamani', 'Soumick Chatterjee'] | 2021-05-28 | null | null | null | null | ['tumour-classification'] | ['medical'] | [ 2.69617811e-02 1.42299771e-01 2.57590767e-02 -2.42865067e-02
-5.03151298e-01 -1.69112295e-01 8.66044581e-01 2.03605562e-01
-6.38123333e-01 8.02470803e-01 8.80068913e-02 -3.65893841e-01
-3.37840408e-01 -6.81933999e-01 -2.59637654e-01 -9.27312613e-01
-4.38217968e-01 5.69698870e-01 5.90219855e-01 3.82152461... | [14.716777801513672, -2.5765342712402344] |
ea812357-96c7-4b1c-9fa3-2d7624beb179 | openeds2020-open-eyes-dataset | 2005.03876 | null | https://arxiv.org/abs/2005.03876v1 | https://arxiv.org/pdf/2005.03876v1.pdf | OpenEDS2020: Open Eyes Dataset | We present the second edition of OpenEDS dataset, OpenEDS2020, a novel dataset of eye-image sequences captured at a frame rate of 100 Hz under controlled illumination, using a virtual-reality head-mounted display mounted with two synchronized eye-facing cameras. The dataset, which is anonymized to remove any personally... | ['Cristina Palmero', 'Sachin S. Talathi', 'Kapil Krishnakumar', 'Karsten Behrendt', 'Abhishek Sharma', 'Oleg V. Komogortsev'] | 2020-05-08 | null | null | null | null | ['eye-tracking'] | ['computer-vision'] | [ 3.01263571e-01 2.21669912e-01 -1.02186464e-01 -4.33663189e-01
-4.06183034e-01 -5.66653192e-01 2.22665414e-01 -4.63782936e-01
-3.41878384e-01 5.27555525e-01 -2.48372741e-02 -1.46725371e-01
2.55852491e-01 4.47482802e-02 -6.67788446e-01 -4.87336278e-01
6.65137619e-02 -2.30380014e-01 3.98567140e-01 4.05784696... | [14.106330871582031, 0.0832081064581871] |
6a7a2062-acb2-4b2f-b0bd-5ae585c21c25 | learning-video-instance-segmentation-with | 2012.03911 | null | https://arxiv.org/abs/2012.03911v1 | https://arxiv.org/pdf/2012.03911v1.pdf | Learning Video Instance Segmentation with Recurrent Graph Neural Networks | Most existing approaches to video instance segmentation comprise multiple modules that are heuristically combined to produce the final output. Formulating a purely learning-based method instead, which models both the temporal aspect as well as a generic track management required to solve the video instance segmentation... | ['Michael Felsberg', 'Martin Danelljan', 'Emil Brissman', 'Joakim Johnander'] | 2020-12-07 | null | null | null | null | ['video-instance-segmentation'] | ['computer-vision'] | [ 3.14067602e-01 8.78814384e-02 -3.41680825e-01 -2.13664234e-01
-7.06784189e-01 -5.50086498e-01 4.26227361e-01 4.07146886e-02
-3.56092930e-01 7.34423637e-01 -2.00534746e-01 -3.17720592e-01
-1.75637290e-01 -5.60061157e-01 -9.62534487e-01 -4.32803214e-01
1.64806750e-02 2.95199364e-01 5.71161807e-01 2.05460548... | [8.96119499206543, -0.02590038999915123] |
622fabcc-b3ad-4edf-81b3-5a2a86e2c9ba | time-series-forecasting-using-manifold | 2110.03625 | null | https://arxiv.org/abs/2110.03625v4 | https://arxiv.org/pdf/2110.03625v4.pdf | Time Series Forecasting Using Manifold Learning | We address a three-tier numerical framework based on manifold learning for the forecasting of high-dimensional time series. At the first step, we embed the time series into a reduced low-dimensional space using a nonlinear manifold learning algorithm such as Locally Linear Embedding and Diffusion Maps. At the second st... | ['Ioannis Kevrekidis', 'Constantinos Siettos', 'Ronen Talmon', 'Panagiotis Papaioannou'] | 2021-10-07 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [-2.76947647e-01 -8.45214948e-02 4.40682203e-01 1.41732305e-01
-6.43068016e-01 -3.30285668e-01 9.49698091e-01 -1.27855614e-01
-2.58419424e-01 6.41079307e-01 2.23900944e-01 -3.90313178e-01
-5.57416201e-01 -5.30087411e-01 -4.82642263e-01 -8.94190788e-01
-7.71336019e-01 4.57040668e-01 -4.69051898e-01 -9.87312049... | [6.605464935302734, 3.534614086151123] |
e87869f2-1b8b-4262-9899-0aa93a1113c9 | bridging-the-language-gap-knowledge-injected | 2304.03159 | null | https://arxiv.org/abs/2304.03159v1 | https://arxiv.org/pdf/2304.03159v1.pdf | Bridging the Language Gap: Knowledge Injected Multilingual Question Answering | Question Answering (QA) is the task of automatically answering questions posed by humans in natural languages. There are different settings to answer a question, such as abstractive, extractive, boolean, and multiple-choice QA. As a popular topic in natural language processing tasks, extractive question answering task ... | ['Jianyong Wang', 'Ning Liu', 'Zhenyu Li', 'Zhengyan Zhang', 'Xiuxing Li', 'Zhichao Duan'] | 2023-04-06 | null | null | null | null | ['cross-lingual-transfer'] | ['natural-language-processing'] | [-1.20851271e-01 3.39972042e-02 -9.82714221e-02 -3.67996275e-01
-1.11407554e+00 -8.48815501e-01 6.62081301e-01 1.20327778e-01
-4.76670831e-01 9.14556265e-01 2.45435953e-01 -5.71362793e-01
-1.18968189e-02 -9.52560544e-01 -9.11947668e-01 -1.01833425e-01
4.84659046e-01 6.62249565e-01 5.27112722e-01 -7.88000643... | [10.749554634094238, 8.08152961730957] |
f6339727-539f-4e4e-ae63-a4f12481c272 | customer-lifetime-value-prediction-using | 1703.02596 | null | http://arxiv.org/abs/1703.02596v3 | http://arxiv.org/pdf/1703.02596v3.pdf | Customer Lifetime Value Prediction Using Embeddings | We describe the Customer LifeTime Value (CLTV) prediction system deployed at
ASOS.com, a global online fashion retailer. CLTV prediction is an important
problem in e-commerce where an accurate estimate of future value allows
retailers to effectively allocate marketing spend, identify and nurture high
value customers an... | ['Roberto Pagliari', 'C. H. Bryan Liu', 'Benjamin Paul Chamberlain', 'Marc Peter Deisenroth', 'Angelo Cardoso'] | 2017-03-07 | null | null | null | null | ['value-prediction'] | ['computer-code'] | [-4.99003798e-01 -1.12369314e-01 -5.08484244e-01 -9.52153265e-01
-4.82790232e-01 -5.40633559e-01 4.13550287e-01 6.65305853e-01
-3.57796878e-01 1.91989332e-01 3.90710145e-01 1.54067948e-01
-1.77493691e-01 -1.06173146e+00 -5.37737906e-01 -3.95793676e-01
-1.90994740e-01 1.03957009e+00 -4.63710874e-01 -8.37867141... | [9.763537406921387, 5.999914646148682] |
2a2e1b5a-52b2-4c2c-b8e4-f270e146e347 | odim-an-efficient-method-to-detect-outliers | 2301.04257 | null | https://arxiv.org/abs/2301.04257v1 | https://arxiv.org/pdf/2301.04257v1.pdf | ODIM: an efficient method to detect outliers via inlier-memorization effect of deep generative models | Identifying whether a given sample is an outlier or not is an important issue in various real-world domains. This study aims to solve the unsupervised outlier detection problem where training data contain outliers, but any label information about inliers and outliers is not given. We propose a powerful and efficient le... | ['Yongdai Kim', 'Kunwoong Kim', 'Jongjin Lee', 'Jaesung Hwang', 'Dongha Kim'] | 2023-01-11 | null | null | null | null | ['memorization'] | ['natural-language-processing'] | [-3.01343977e-01 -3.81467462e-01 2.88024962e-01 -2.73101032e-01
-6.39470279e-01 -1.00243457e-01 4.47772682e-01 4.73255575e-01
-3.59377712e-01 5.26844680e-01 4.29285131e-02 9.30207819e-02
-1.01480372e-02 -7.18647480e-01 -1.08241570e+00 -7.37924576e-01
-2.35898942e-02 5.95591784e-01 -4.11873870e-02 2.17008740... | [7.663820266723633, 2.453977346420288] |
aa3478e2-cee7-4221-ac31-6ebfd1ade8b9 | data-driven-real-time-short-term-prediction | 2211.09814 | null | https://arxiv.org/abs/2211.09814v1 | https://arxiv.org/pdf/2211.09814v1.pdf | Data-driven Real-time Short-term Prediction of Air Quality: Comparison of ES, ARIMA, and LSTM | Air pollution is a worldwide issue that affects the lives of many people in urban areas. It is considered that the air pollution may lead to heart and lung diseases. A careful and timely forecast of the air quality could help to reduce the exposure risk for affected people. In this paper, we use a data-driven approach ... | ['Sabri Pllana', 'Iryna Talamanova'] | 2022-11-16 | null | null | null | null | ['air-pollution-prediction', 'time-series-prediction'] | ['miscellaneous', 'time-series'] | [-2.40126979e-02 -5.18119931e-01 1.67509973e-01 -1.47688761e-01
-4.53660816e-01 -9.23089162e-02 3.94745797e-01 2.25613624e-01
-4.33263242e-01 1.06401777e+00 2.60169923e-01 -7.53660500e-01
-4.12336946e-01 -1.30671322e+00 -2.17751861e-01 -7.14432657e-01
3.74974906e-02 -1.64750755e-01 2.99162030e-01 1.48258001... | [6.278886795043945, 2.598992109298706] |
07624be1-53d5-4821-88db-23a7378303f2 | champion-solution-for-the-wsdm2023-toloka-vqa | 2301.09045 | null | https://arxiv.org/abs/2301.09045v2 | https://arxiv.org/pdf/2301.09045v2.pdf | Champion Solution for the WSDM2023 Toloka VQA Challenge | In this report, we present our champion solution to the WSDM2023 Toloka Visual Question Answering (VQA) Challenge. Different from the common VQA and visual grounding (VG) tasks, this challenge involves a more complex scenario, i.e. inferring and locating the object implicitly specified by the given interrogative questi... | ['Tong Lu', 'Wenhai Wang', 'Guo Chen', 'Zhe Chen', 'Shengyi Gao'] | 2023-01-22 | null | null | null | null | ['visual-grounding'] | ['computer-vision'] | [-4.24132682e-02 2.13783532e-01 9.29451361e-02 -4.63564754e-01
-1.35375977e+00 -1.16674757e+00 6.13988698e-01 -1.52091354e-01
-3.89738590e-01 3.85838121e-01 4.06470507e-01 -6.11279011e-01
2.40745485e-01 -3.51762891e-01 -1.07627547e+00 -3.53797495e-01
6.18004382e-01 6.48901284e-01 2.72203714e-01 -2.19815552... | [10.865164756774902, 1.704764723777771] |
eef6c159-f48f-436f-b90b-032e4b2c17fb | designing-accurate-emulators-for-scientific | 2005.02328 | null | https://arxiv.org/abs/2005.02328v1 | https://arxiv.org/pdf/2005.02328v1.pdf | Designing Accurate Emulators for Scientific Processes using Calibration-Driven Deep Models | Predictive models that accurately emulate complex scientific processes can achieve exponential speed-ups over numerical simulators or experiments, and at the same time provide surrogates for improving the subsequent analysis. Consequently, there is a recent surge in utilizing modern machine learning (ML) methods, such ... | ['Peer-Timo Bremer', 'Jayaraman J. Thiagarajan', 'Jim Gaffney', 'Gemma Anderson', 'Brian Spears', 'Rushil Anirudh', 'Bindya Venkatesh'] | 2020-05-05 | null | null | null | null | ['small-data'] | ['computer-vision'] | [-1.27847821e-01 -4.05593038e-01 -1.72717616e-01 -4.02946949e-01
-1.03021419e+00 -4.97141153e-01 5.61436892e-01 4.84807551e-01
-3.33829015e-01 9.91985440e-01 -2.81640708e-01 -6.00350142e-01
-5.76698422e-01 -7.89639950e-01 -9.18929517e-01 -9.11020696e-01
-1.02124937e-01 7.41970062e-01 -3.65236431e-01 1.08651035... | [7.136688709259033, 3.899250030517578] |
44f74b93-e3b5-46cb-a0e6-e819ae1cd3b5 | evaluating-semantic-models-with-word-sentence | 1603.07253 | null | http://arxiv.org/abs/1603.07253v2 | http://arxiv.org/pdf/1603.07253v2.pdf | Evaluating semantic models with word-sentence relatedness | Semantic textual similarity (STS) systems are designed to encode and evaluate
the semantic similarity between words, phrases, sentences, and documents. One
method for assessing the quality or authenticity of semantic information
encoded in these systems is by comparison with human judgments. A data set for
evaluating s... | ['Kimberly Glasgow', 'Mark Chevillet', 'Matthew Roos', 'Amy Haufler', 'Michael Wolmetz'] | 2016-03-23 | null | null | null | null | ['implicatures'] | ['natural-language-processing'] | [ 2.11690560e-01 4.99646366e-02 1.38084158e-01 -6.75796688e-01
-5.85724175e-01 -7.14342058e-01 8.00962210e-01 1.03969908e+00
-8.30404699e-01 1.64573789e-01 1.07193458e+00 -1.35826483e-01
-3.43400747e-01 -4.60067928e-01 7.11764321e-02 -1.49297729e-01
4.57167566e-01 5.93007088e-01 9.35327560e-02 -4.60981935... | [10.613570213317871, 9.063237190246582] |
88f10a62-3c92-42e1-a55e-f56b9c8865d0 | a-universality-individuality-integration | 2204.06185 | null | https://arxiv.org/abs/2204.06185v1 | https://arxiv.org/pdf/2204.06185v1.pdf | A Universality-Individuality Integration Model for Dialog Act Classification | Dialog Act (DA) reveals the general intent of the speaker utterance in a conversation. Accurately predicting DAs can greatly facilitate the development of dialog agents. Although researchers have done extensive research on dialog act classification, the feature information of classification has not been fully considere... | ['Ma Yinglong', 'Gao Pengfei'] | 2022-04-13 | null | null | null | null | ['dialog-act-classification', 'dialogue-act-classification'] | ['natural-language-processing', 'natural-language-processing'] | [-3.36702108e-01 -1.57043740e-01 -3.91816527e-01 -5.48300266e-01
8.51643011e-02 -4.26716983e-01 8.08400095e-01 7.87975267e-02
-9.58359614e-02 4.55619693e-01 9.16518748e-01 -6.40950054e-02
1.12667114e-01 -3.99634451e-01 4.78521824e-01 -7.22511828e-01
3.39244664e-01 2.95083135e-01 4.47451413e-01 -8.24884772... | [12.707733154296875, 7.748104095458984] |
1ee26e62-7687-4c2a-9165-79e37d0b0986 | capabilities-of-gpt-4-on-medical-challenge | 2303.13375 | null | https://arxiv.org/abs/2303.13375v2 | https://arxiv.org/pdf/2303.13375v2.pdf | Capabilities of GPT-4 on Medical Challenge Problems | Large language models (LLMs) have demonstrated remarkable capabilities in natural language understanding and generation across various domains, including medicine. We present a comprehensive evaluation of GPT-4, a state-of-the-art LLM, on medical competency examinations and benchmark datasets. GPT-4 is a general-purpos... | ['Eric Horvitz', 'Dean Carignan', 'Scott Mayer McKinney', 'Nicholas King', 'Harsha Nori'] | 2023-03-20 | null | null | null | null | ['memorization'] | ['natural-language-processing'] | [ 1.81312293e-01 6.62564218e-01 -4.49390352e-01 -5.00505507e-01
-1.41345119e+00 -6.58023894e-01 1.60525605e-01 4.61368859e-01
-5.27448058e-01 8.03710461e-01 4.19697225e-01 -1.28325522e+00
-6.82622313e-01 -5.86917818e-01 -1.11484790e+00 -1.56022862e-01
3.31671506e-01 7.44290531e-01 -1.79730237e-01 -1.82389483... | [8.822016716003418, 8.482512474060059] |
6b89b40f-29f2-462b-a50c-3194f3f4a381 | the-golden-ratio-of-learning-and-momentum | 2006.04751 | null | https://arxiv.org/abs/2006.04751v1 | https://arxiv.org/pdf/2006.04751v1.pdf | The Golden Ratio of Learning and Momentum | Gradient descent has been a central training principle for artificial neural networks from the early beginnings to today's deep learning networks. The most common implementation is the backpropagation algorithm for training feed-forward neural networks in a supervised fashion. Backpropagation involves computing the gra... | ['Stefan Jaeger'] | 2020-06-08 | null | null | null | null | ['handwritten-digit-recognition'] | ['computer-vision'] | [ 4.60083969e-02 -6.58285394e-02 -3.68146226e-02 -6.41317368e-01
2.88085639e-01 6.24129586e-02 3.07970196e-01 3.03913444e-01
-1.13796067e+00 8.30654383e-01 -3.90797943e-01 -3.01201105e-01
-4.31280315e-01 -8.97236288e-01 -4.60921586e-01 -8.48414302e-01
-9.84388068e-02 9.81526673e-02 4.55518246e-01 -3.23940277... | [7.99333381652832, 3.4375720024108887] |
128bc24c-c645-4db4-9cc8-8840459295ac | combining-contrastive-and-non-contrastive | 2211.01964 | null | https://arxiv.org/abs/2211.01964v1 | https://arxiv.org/pdf/2211.01964v1.pdf | Combining Contrastive and Non-Contrastive Losses for Fine-Tuning Pretrained Models in Speech Analysis | Embedding paralinguistic properties is a challenging task as there are only a few hours of training data available for domains such as emotional speech. One solution to this problem is to pretrain a general self-supervised speech representation model on large amounts of unlabeled speech. This pretrained model is then f... | ['Ngoc Thang Vu', 'Ching-Yi Chen', 'Florian Lux'] | 2022-10-21 | null | null | null | null | ['emotion-classification', 'emotion-classification'] | ['computer-vision', 'natural-language-processing'] | [ 2.85225362e-01 1.91862434e-01 1.09831719e-02 -8.87688696e-01
-1.17229760e+00 -6.33553267e-01 6.32061899e-01 -3.66739370e-02
-5.65002501e-01 4.87031609e-01 3.21016967e-01 -8.01861286e-02
2.62882918e-01 -3.48271012e-01 -4.90240276e-01 -6.14974916e-01
9.98004079e-02 4.70358044e-01 -5.84272854e-02 -2.96757102... | [13.79356575012207, 5.946377277374268] |
991ff2e1-241e-4815-bdf9-3939603caa9e | self-supervised-facial-action-unit-detection | 2303.05708 | null | https://arxiv.org/abs/2303.05708v1 | https://arxiv.org/pdf/2303.05708v1.pdf | Self-supervised Facial Action Unit Detection with Region and Relation Learning | Facial action unit (AU) detection is a challenging task due to the scarcity of manual annotations. Recent works on AU detection with self-supervised learning have emerged to address this problem, aiming to learn meaningful AU representations from numerous unlabeled data. However, most existing AU detection works with s... | ['Zhilei Liu', 'Juan Song'] | 2023-03-10 | null | null | null | null | ['action-unit-detection', 'facial-action-unit-detection'] | ['computer-vision', 'computer-vision'] | [ 2.10068852e-01 1.11625984e-01 -3.15068841e-01 -3.79645795e-01
-7.17220187e-01 2.36840406e-03 3.40493411e-01 3.17903459e-02
-1.88702986e-01 4.23881978e-01 2.91196167e-01 5.60111225e-01
2.12927774e-01 -6.65183783e-01 -5.15083969e-01 -1.07868099e+00
-2.32957061e-02 -7.85060301e-02 4.54986513e-01 -3.29309195... | [13.662312507629395, 1.5478315353393555] |
3e9bcf76-24f1-43df-8041-f5296bc01419 | look-beneath-the-surface-exploiting | 2306.04220 | null | https://arxiv.org/abs/2306.04220v3 | https://arxiv.org/pdf/2306.04220v3.pdf | Look Beneath the Surface: Exploiting Fundamental Symmetry for Sample-Efficient Offline RL | Offline reinforcement learning (RL) offers an appealing approach to real-world tasks by learning policies from pre-collected datasets without interacting with the environment. However, the performance of existing offline RL algorithms heavily depends on the scale and state-action space coverage of datasets. Real-world ... | ['Li Jiang', 'Youfang Lin', 'Han Wang', 'Shoucheng Song', 'Wenjia Zhang', 'Zhihao Wu', 'Xianyuan Zhan', 'Peng Cheng'] | 2023-06-07 | null | null | null | null | ['offline-rl'] | ['playing-games'] | [-1.22119859e-01 -6.93058819e-02 -6.54722154e-01 1.06019378e-01
-5.51958740e-01 -6.93565488e-01 6.32466137e-01 -5.40222526e-02
-2.36026078e-01 9.09496546e-01 1.59447566e-01 -4.32038307e-01
-4.01244491e-01 -4.46962297e-01 -7.18203545e-01 -8.75257134e-01
-4.72943097e-01 3.13168943e-01 -1.28491849e-01 -2.41242319... | [4.12369441986084, 2.1486754417419434] |
1f97c6b6-e824-429d-ad13-d923082f43f8 | unsupervised-multi-target-domain-adaptation-2 | 2007.07077 | null | https://arxiv.org/abs/2007.07077v4 | https://arxiv.org/pdf/2007.07077v4.pdf | Unsupervised Multi-Target Domain Adaptation Through Knowledge Distillation | Unsupervised domain adaptation (UDA) seeks to alleviate the problem of domain shift between the distribution of unlabeled data from the target domain w.r.t. labeled data from the source domain. While the single-target UDA scenario is well studied in the literature, Multi-Target Domain Adaptation (MTDA) remains largely ... | ['Atif Belal', 'Louis-Antoine Blais-Morin', 'Madhu Kiran', 'Le Thanh Nguyen-Meidine', 'Jose Dolz', 'Eric Granger'] | 2020-07-14 | null | null | null | null | ['multi-target-domain-adaptation'] | ['computer-vision'] | [ 1.99265182e-01 9.65032578e-02 -2.13955402e-01 -4.14172679e-01
-8.80434752e-01 -8.79223228e-01 5.39511800e-01 1.20274909e-01
-4.36767876e-01 5.99452496e-01 -1.96370795e-01 -2.23086700e-01
-1.30956873e-01 -8.08696032e-01 -7.08650172e-01 -7.96672761e-01
4.71987337e-01 7.63879001e-01 5.26238561e-01 -6.58258498... | [10.235393524169922, 2.8150651454925537] |
1a5fab11-6823-4ca4-aead-9c73deefab55 | option-optimization-algorithm-benchmarking-1 | 2211.11332 | null | https://arxiv.org/abs/2211.11332v1 | https://arxiv.org/pdf/2211.11332v1.pdf | OPTION: OPTImization Algorithm Benchmarking ONtology | Many optimization algorithm benchmarking platforms allow users to share their experimental data to promote reproducible and reusable research. However, different platforms use different data models and formats, which drastically complicates the identification of relevant datasets, their interpretation, and their intero... | ['Tome Eftimov', 'Panče Panov', 'Saso Džeroski', 'Carola Doerr', 'Diederick Vermetten', 'Ana Kostovska'] | 2022-11-21 | null | null | null | null | ['data-integration'] | ['knowledge-base'] | [-3.93596381e-01 -3.09116513e-01 -1.42710358e-01 -3.97286773e-01
-3.42594266e-01 -7.18554974e-01 2.33469814e-01 9.84517813e-01
-4.59864318e-01 5.09349108e-01 2.26692453e-01 -1.16472512e-01
-6.68973505e-01 -9.37272251e-01 -5.66511691e-01 -3.92896950e-01
1.23784505e-01 7.32435703e-01 3.44998866e-01 -4.07320172... | [9.166831016540527, 7.918874740600586] |
09f481af-491f-4bfb-9d51-c94a3d8b4e4b | deep-class-incremental-learning-a-survey | 2302.03648 | null | https://arxiv.org/abs/2302.03648v1 | https://arxiv.org/pdf/2302.03648v1.pdf | Deep Class-Incremental Learning: A Survey | Deep models, e.g., CNNs and Vision Transformers, have achieved impressive achievements in many vision tasks in the closed world. However, novel classes emerge from time to time in our ever-changing world, requiring a learning system to acquire new knowledge continually. For example, a robot needs to understand new inst... | ['Ziwei Liu', 'De-Chuan Zhan', 'Han-Jia Ye', 'Zhi-Hong Qi', 'Qi-Wei Wang', 'Da-Wei Zhou'] | 2023-02-07 | null | null | null | null | ['class-incremental-learning'] | ['computer-vision'] | [ 1.34454325e-01 -3.00153732e-01 -1.75436348e-01 -3.74605805e-01
-2.46823773e-01 -3.33445519e-01 5.37401140e-01 2.87356406e-01
-5.19037008e-01 7.30717719e-01 -2.74207771e-01 -2.28603169e-01
-2.47230032e-03 -7.92240620e-01 -7.65327334e-01 -7.41590023e-01
2.40322515e-01 3.31683785e-01 4.01022941e-01 1.05278246... | [9.824945449829102, 3.383453845977783] |
6a76052e-0d35-496a-a19c-060a1b020d76 | distributional-perturbation-for-efficient | null | null | https://openreview.net/forum?id=rGg-Qcyplgq | https://openreview.net/pdf?id=rGg-Qcyplgq | Distributional Perturbation for Efficient Exploration in Distributional Reinforcement Learning | Distributional reinforcement learning aims to learn distribution of return under stochastic environments. Since the learned distribution of return contains rich information about the stochasticity of the environment, previous studies have relied on descriptive statistics, such as standard deviation, for optimism in fac... | ['Jungwoo Lee', 'Kyungjae Lee', 'Heesoo Lee', 'Sungyeob Han', 'Tae Hyun Cho'] | 2021-09-29 | null | null | null | null | ['distributional-reinforcement-learning'] | ['methodology'] | [-2.67176390e-01 3.87280822e-01 -4.26528335e-01 -2.08475217e-01
-8.30436110e-01 -5.17387569e-01 4.45713699e-01 -7.82365501e-02
-8.82063508e-01 1.17399526e+00 1.78598881e-01 -5.47981203e-01
-5.50913215e-01 -8.75898421e-01 -6.05372667e-01 -1.11748147e+00
-2.71284074e-01 3.29148322e-01 -2.21110120e-01 -1.49653986... | [4.247776031494141, 2.5876049995422363] |
73dd2a3d-6bf7-4bd4-b792-a050eb6ebd46 | meta-learning-siamese-network-for-few-shot | 2302.03507 | null | https://arxiv.org/abs/2302.03507v2 | https://arxiv.org/pdf/2302.03507v2.pdf | Meta-Learning Siamese Network for Few-Shot Text Classification | Few-shot learning has been used to tackle the problem of label scarcity in text classification, of which meta-learning based methods have shown to be effective, such as the prototypical networks (PROTO). Despite the success of PROTO, there still exist three main problems: (1) ignore the randomness of the sampled suppor... | ['Aoying Zhou', 'Ming Gao', 'Minghui Qiu', 'Xiang Li', 'Yingnan Fu', 'Yuhe Wang', 'Chengcheng Han'] | 2023-02-05 | null | null | null | null | ['few-shot-text-classification'] | ['natural-language-processing'] | [ 8.93730298e-02 -1.48055911e-01 -4.49886948e-01 -2.38844469e-01
-6.48847997e-01 -3.31023112e-02 5.70312798e-01 6.55684397e-02
-5.69472134e-01 8.13727140e-01 4.88867797e-03 5.15962653e-02
-2.53214508e-01 -8.66480529e-01 -2.90423840e-01 -7.22864747e-01
2.86724448e-01 5.22070169e-01 2.77852237e-01 -1.34710386... | [10.166346549987793, 3.4900448322296143] |
a56d87f1-84ab-41b2-b08f-9f9b8ef29428 | explore-the-power-of-synthetic-data-on-few | 2303.13221 | null | https://arxiv.org/abs/2303.13221v2 | https://arxiv.org/pdf/2303.13221v2.pdf | Explore the Power of Synthetic Data on Few-shot Object Detection | Few-shot object detection (FSOD) aims to expand an object detector for novel categories given only a few instances for training. The few training samples restrict the performance of FSOD model. Recent text-to-image generation models have shown promising results in generating high-quality images. How applicable these sy... | ['Rui Zhao', 'Xingyu Zeng', 'Kun Wang', 'Shaobo Lin'] | 2023-03-23 | null | null | null | null | ['few-shot-object-detection'] | ['computer-vision'] | [ 6.00951314e-01 9.24401730e-02 9.18359458e-02 -1.80349499e-01
-7.99255371e-01 -2.01176986e-01 6.82571471e-01 -1.64747193e-01
-3.64406198e-01 5.38050950e-01 -1.80686578e-01 3.17513824e-01
2.54599899e-01 -8.19484353e-01 -9.49434459e-01 -7.54552066e-01
4.99953628e-01 2.73892403e-01 8.75048220e-01 -8.87181833... | [9.586894989013672, 1.719116449356079] |
4b198f67-632c-4c27-acb6-3d5cb51a8c6a | cnn-based-density-estimation-and-crowd | 2003.12783 | null | https://arxiv.org/abs/2003.12783v1 | https://arxiv.org/pdf/2003.12783v1.pdf | CNN-based Density Estimation and Crowd Counting: A Survey | Accurately estimating the number of objects in a single image is a challenging yet meaningful task and has been applied in many applications such as urban planning and public safety. In the various object counting tasks, crowd counting is particularly prominent due to its specific significance to social security and de... | ['Junyu. Gao', 'Qi. Wang', 'Qingjie Liu', 'Yunhong Wang', 'Guangshuai Gao'] | 2020-03-28 | null | null | null | null | ['object-counting'] | ['computer-vision'] | [-3.84611964e-01 -4.94198620e-01 -2.83486992e-01 -3.65223467e-01
-1.89589322e-01 -1.14635609e-01 4.99188930e-01 8.00422803e-02
-6.84491992e-01 8.72266173e-01 1.14504591e-01 -4.31224614e-01
2.01918557e-01 -1.20037258e+00 -2.31341049e-01 -7.49544799e-01
8.87187570e-02 4.42732841e-01 5.72852552e-01 -9.08648893... | [8.424327850341797, -0.33595335483551025] |
1098a6ff-c3ed-4a0a-b90f-30a5522de70b | sad-segment-any-rgbd | 2305.14207 | null | https://arxiv.org/abs/2305.14207v1 | https://arxiv.org/pdf/2305.14207v1.pdf | SAD: Segment Any RGBD | The Segment Anything Model (SAM) has demonstrated its effectiveness in segmenting any part of 2D RGB images. However, SAM exhibits a stronger emphasis on texture information while paying less attention to geometry information when segmenting RGB images. To address this limitation, we propose the Segment Any RGBD (SAD) ... | ['Qifeng Chen', 'Ziwei Liu', 'Lingdong Kong', 'Yixuan Pei', 'Jingkang Yang', 'Xingyi Li', 'Kewei Wang', 'Yizheng Wu', 'Jun Cen'] | 2023-05-23 | null | null | null | null | ['panoptic-segmentation'] | ['computer-vision'] | [ 2.46494204e-01 2.14192078e-01 1.40950203e-01 -4.75382864e-01
-5.41716695e-01 -7.27759957e-01 4.80550587e-01 7.41371661e-02
-1.78286195e-01 -1.02779523e-01 9.24656540e-02 -3.99684280e-01
1.48620248e-01 -1.09074545e+00 -3.08888882e-01 -5.54685116e-01
4.10922378e-01 3.24350484e-02 3.36291343e-01 -1.66671753... | [8.851123809814453, -2.8830795288085938] |
026aa436-b7d1-4f1b-9207-0aacd4097c09 | not-all-languages-are-created-equal-in-llms | 2305.07004 | null | https://arxiv.org/abs/2305.07004v1 | https://arxiv.org/pdf/2305.07004v1.pdf | Not All Languages Are Created Equal in LLMs: Improving Multilingual Capability by Cross-Lingual-Thought Prompting | Large language models (LLMs) demonstrate impressive multilingual capability, but their performance varies substantially across different languages. In this work, we introduce a simple yet effective method, called cross-lingual-thought prompting (XLT), to systematically improve the multilingual capability of LLMs. Speci... | ['Furu Wei', 'Yan Xia', 'Ting Song', 'Wayne Xin Zhao', 'Dongdong Zhang', 'Tianyi Tang', 'Haoyang Huang'] | 2023-05-11 | null | null | null | null | ['open-domain-question-answering', 'arithmetic-reasoning', 'logical-reasoning'] | ['natural-language-processing', 'reasoning', 'reasoning'] | [-4.65112627e-01 -5.32771721e-02 -2.22781137e-01 -2.35605597e-01
-1.50224102e+00 -6.76189482e-01 9.24031138e-01 2.14340642e-01
-4.69808936e-01 7.33173013e-01 5.18698275e-01 -7.47989655e-01
6.38701320e-02 -7.79663801e-01 -7.59353220e-01 -8.25416744e-02
3.76026988e-01 5.02199173e-01 -8.80894717e-03 -6.63460612... | [10.988234519958496, 9.34571647644043] |
38b0a0f8-bec2-49bc-a384-2c042381dcd7 | on-the-optimality-of-batch-policy | 2104.02293 | null | https://arxiv.org/abs/2104.02293v1 | https://arxiv.org/pdf/2104.02293v1.pdf | On the Optimality of Batch Policy Optimization Algorithms | Batch policy optimization considers leveraging existing data for policy construction before interacting with an environment. Although interest in this problem has grown significantly in recent years, its theoretical foundations remain under-developed. To advance the understanding of this problem, we provide three resul... | ['Dale Schuurmans', 'Csaba Szepesvari', 'Lihong Li', 'Jincheng Mei', 'Bo Dai', 'Tor Lattimore', 'Yifan Wu', 'Chenjun Xiao'] | 2021-04-06 | null | null | null | null | ['value-prediction'] | ['computer-code'] | [ 5.82141094e-02 1.04046933e-01 -9.71020758e-01 -2.94019967e-01
-8.99594545e-01 -9.55180228e-01 3.57117563e-01 1.44390807e-01
-5.48782289e-01 1.14555025e+00 1.34393886e-01 -9.05679584e-01
-7.37061322e-01 -4.62919474e-01 -7.93654263e-01 -8.92173469e-01
-1.13503471e-01 6.10548079e-01 -2.12136477e-01 -3.33372094... | [4.516161918640137, 3.234903335571289] |
76d1b3ff-64df-4528-babd-e34c00f2e134 | multi-source-domain-adaptation-using-gradient | 2109.01503 | null | https://arxiv.org/abs/2109.01503v1 | https://arxiv.org/pdf/2109.01503v1.pdf | Multi-source Domain Adaptation Using Gradient Reversal Layer for Mitotic Cell Detection | This is a write-up of our method submitted to Mitosis Domain Generalization (MIDOG 2021) Challenge held in MICCAI2021 conference. | ['Satoshi Kondo'] | 2021-09-02 | null | null | null | null | ['cell-detection'] | ['computer-vision'] | [ 6.04133368e-01 5.67221761e-01 -3.37317705e-01 -8.31362545e-01
-5.27650476e-01 -3.77932131e-01 8.91927958e-01 -9.05352924e-03
-2.86217749e-01 1.39650691e+00 7.20919073e-02 -1.56581581e-01
1.42084241e-01 -1.81430817e-01 -7.55255997e-01 -7.18280524e-02
-4.06130821e-01 6.92140102e-01 3.58762681e-01 -4.58141029... | [10.203283309936523, 2.9693894386291504] |
86b09a6b-2903-4250-b486-b007559eb42e | real-time-spatio-temporal-action-localization | null | null | https://openaccess.thecvf.com/content/ACCV2020W/MMHAU/papers/Liu_Real-time_spatio-temporal_action_localization_via_learning_motion_representation_ACCVW_2020_paper.pdf | https://openaccess.thecvf.com/content/ACCV2020W/MMHAU/papers/Liu_Real-time_spatio-temporal_action_localization_via_learning_motion_representation_ACCVW_2020_paper.pdf | Real-time Spatio-temporal Action Localization via Learning Motion Representation | Abstract. Most state-of-the-art spatio-temporal (S-T) action localization methods explicitly use optical flow as auxiliary motion information. Although the combination of optical flow and RGB significantly improves the performance, optical flow estimation brings a large amount of computational cost and the whole networ... | ['and Qianqing Qin', 'Xing Xie', 'Liyu Lin', 'Zhigang Tu', 'Yuanzhong Liu'] | 2020-11-30 | null | null | null | accv-2020-11 | ['spatio-temporal-action-localization'] | ['computer-vision'] | [ 1.99702736e-02 -5.09938598e-01 -6.00125849e-01 -1.54019982e-01
-5.91593504e-01 -3.74106914e-01 4.54790890e-01 -5.82048059e-01
-7.44734526e-01 8.63210618e-01 2.09075928e-01 -1.04219720e-01
3.78549099e-02 -5.76658487e-01 -7.32000053e-01 -8.72934878e-01
-4.80149873e-02 -1.61172181e-01 5.55568039e-01 2.91251857... | [8.431056022644043, 0.3152414560317993] |
f2080d93-dd19-4165-97a7-b31336062ad4 | crackformer-transformer-network-for-fine | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Liu_CrackFormer_Transformer_Network_for_Fine-Grained_Crack_Detection_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Liu_CrackFormer_Transformer_Network_for_Fine-Grained_Crack_Detection_ICCV_2021_paper.pdf | CrackFormer: Transformer Network for Fine-Grained Crack Detection | Cracks are irregular line structures that are of interest in many computer vision applications. Crack detection (e.g., from pavement images) is a challenging task due to intensity in-homogeneity, topology complexity, low contrast and noisy background. The overall crack detection accuracy can be significantly affect... | ['Hui Kong', 'Chengzhong Xu', 'Christoph Mertz', 'Xiangyu Miao', 'Huajun Liu'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['crack-segmentation'] | ['computer-vision'] | [ 1.67577311e-01 -9.48671550e-02 5.01771688e-01 -1.63576871e-01
-1.02960885e+00 7.65198795e-03 3.22753079e-02 2.71249890e-01
-2.34784976e-01 2.77047038e-01 1.89559966e-01 2.63383061e-01
2.52373870e-02 -1.02086377e+00 -7.94019938e-01 -1.03644812e+00
6.42879680e-02 -8.48772302e-02 9.28563297e-01 -2.69054264... | [7.522474765777588, 1.4770376682281494] |
5e78c6a6-c478-4537-99b3-da4f5efea1c1 | pippi2021-an-approach-to-automated-diagnosis | 2211.02639 | null | https://arxiv.org/abs/2211.02639v1 | https://arxiv.org/pdf/2211.02639v1.pdf | PIPPI2021: An Approach to Automated Diagnosis and Texture Analysis of the Fetal Liver & Placenta in Fetal Growth Restriction | Fetal growth restriction (FGR) is a prevalent pregnancy condition characterised by failure of the fetus to reach its genetically predetermined growth potential. We explore the application of model fitting techniques, linear regression machine learning models, deep learning regression, and Haralick textured features fro... | ['Andrew Melbourne', 'Anna David', 'Sebastien Ourselin', 'Rosalind Aughwane', 'Kasia Maksym', 'Nada Mufti', 'Dimitra Flouri', 'Zhanchong Ou', 'Ashay Patel', 'Paula Ramirez Gilliland', 'Aya Mutaz Zeidan'] | 2022-11-01 | null | null | null | null | ['texture-classification'] | ['computer-vision'] | [ 1.09189022e-02 1.01902440e-01 -1.88509390e-01 -4.13899630e-01
-2.71523237e-01 -5.54830432e-01 2.45885670e-01 2.46450469e-01
-1.42745376e-01 2.84386873e-01 3.76653462e-03 -5.71261227e-01
-5.30732810e-01 -7.55256712e-01 -5.88195562e-01 -9.32121277e-01
-8.51232469e-01 5.71123600e-01 -4.46285754e-02 1.38024732... | [14.032720565795898, -2.374577522277832] |
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