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f38c22b9-af7e-4d70-a966-a5b9f36d837d | extrinsic-factors-affecting-the-accuracy-of | 2305.18152 | null | https://arxiv.org/abs/2305.18152v1 | https://arxiv.org/pdf/2305.18152v1.pdf | Extrinsic Factors Affecting the Accuracy of Biomedical NER | Biomedical named entity recognition (NER) is a critial task that aims to identify structured information in clinical text, which is often replete with complex, technical terms and a high degree of variability. Accurate and reliable NER can facilitate the extraction and analysis of important biomedical information, whic... | ['Jungyeul Park', 'Yujie Song', 'Shengjie Zhang', 'Zhiyi Li'] | 2023-05-29 | null | null | null | null | ['named-entity-recognition-ner'] | ['natural-language-processing'] | [ 4.01876308e-02 1.70629755e-01 -1.29189461e-01 -2.22268343e-01
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2.47827485e-01 3.31676960e-01 -1.05874576e-01 -1.90503284... | [8.409204483032227, 8.833733558654785] |
2204caac-402b-426f-be12-4fe998416148 | user-friendly-image-editing-with-minimal-text | 2306.02717 | null | https://arxiv.org/abs/2306.02717v1 | https://arxiv.org/pdf/2306.02717v1.pdf | User-friendly Image Editing with Minimal Text Input: Leveraging Captioning and Injection Techniques | Recent text-driven image editing in diffusion models has shown remarkable success. However, the existing methods assume that the user's description sufficiently grounds the contexts in the source image, such as objects, background, style, and their relations. This assumption is unsuitable for real-world applications be... | ['Gayeong Lee', 'Seungryong Kim', 'Yunjey Choi', 'Junho Kim', 'Hyunsu Kim', 'Wooseok Jang', 'Sunwoo Kim'] | 2023-06-05 | null | null | null | null | ['prompt-engineering'] | ['natural-language-processing'] | [ 4.58051592e-01 -1.05715275e-01 -1.57214906e-02 -6.57454193e-01
-5.27617276e-01 -6.42024994e-01 8.22326720e-01 2.44798273e-01
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5.57666957e-01 2.07909733e-01 3.33461285e-01 -3.87835056... | [11.343694686889648, -0.2582493722438812] |
07376350-dc1e-421d-a207-ce9f42d79707 | a-novel-membership-inference-attack-against | 2210.08956 | null | https://arxiv.org/abs/2210.08956v1 | https://arxiv.org/pdf/2210.08956v1.pdf | A Novel Membership Inference Attack against Dynamic Neural Networks by Utilizing Policy Networks Information | Unlike traditional static deep neural networks (DNNs), dynamic neural networks (NNs) adjust their structures or parameters to different inputs to guarantee accuracy and computational efficiency. Meanwhile, it has been an emerging research area in deep learning recently. Although traditional static DNNs are vulnerable t... | ['Kai Chen', 'Ruigang Liang', 'Shenchen Zhu', 'Peizhuo Lv', 'Pan Li'] | 2022-10-17 | null | null | null | null | ['inference-attack', 'membership-inference-attack'] | ['adversarial', 'computer-vision'] | [-1.95270732e-01 -3.13721895e-01 -3.22279155e-01 -5.57399988e-01
-1.61674067e-01 -1.06397033e+00 4.27718282e-01 -3.94511998e-01
-7.39771843e-01 7.33586788e-01 -3.62171382e-01 -1.00960839e+00
-1.27522826e-01 -7.07902670e-01 -9.53445256e-01 -9.64776754e-01
-1.38856605e-01 2.09874995e-02 7.26851225e-01 -5.32202758... | [5.592456817626953, 7.815441608428955] |
ef64ad20-5f99-40eb-9aed-2b23d7039ece | distance-guided-ga-based-approach-to | 1901.05564 | null | http://arxiv.org/abs/1901.05564v1 | http://arxiv.org/pdf/1901.05564v1.pdf | Distance-Guided GA-Based Approach to Distributed Data-Intensive Web Service Composition | Distributed computing which uses Web services as fundamental elements,
enables high-speed development of software applications through composing many
interoperating, distributed, re-usable, and autonomous services. As a
fundamental challenge for service developers, service composition must fulfil
functional requirement... | ['Hui Ma', 'Soheila Sadeghiram', 'Gang Chen'] | 2019-01-16 | null | null | null | null | ['service-composition'] | ['miscellaneous'] | [ 2.01779500e-01 -5.20824790e-01 1.51183814e-01 -5.49318612e-01
-2.35235438e-01 -7.13157117e-01 3.78913671e-01 -1.80968925e-01
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-6.72564685e-01 -1.01025403e+00 9.67405513e-02 -1.06323326e+00
-1.28680140e-01 6.45032465e-01 5.09519100e-01 -4.43201423... | [8.587454795837402, 6.943497657775879] |
14a8fff7-9549-40ec-8a41-ac597d7600ca | aggregating-long-term-context-for-learning | 2009.00681 | null | https://arxiv.org/abs/2009.00681v4 | https://arxiv.org/pdf/2009.00681v4.pdf | Aggregating Long-Term Context for Learning Laparoscopic and Robot-Assisted Surgical Workflows | Analyzing surgical workflow is crucial for surgical assistance robots to understand surgeries. With the understanding of the complete surgical workflow, the robots are able to assist the surgeons in intra-operative events, such as by giving a warning when the surgeon is entering specific keys or high-risk phases. Deep ... | ['Hidekazu Iwaki', 'Yutong Ban', 'Thomas Ward', 'Taisei Kondo', 'Guy Rosman', 'Daniela Rus', 'Ozanan Meireles', 'Daniel Hashimoto'] | 2020-09-01 | null | null | null | null | ['surgical-phase-recognition'] | ['computer-vision'] | [ 1.61348134e-01 2.19424218e-01 -6.98743165e-01 -5.66821218e-01
-3.06921571e-01 -5.97290456e-01 3.24842513e-01 4.17271733e-01
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-5.29981554e-01 5.09912848e-01 -9.77991745e-02 1.14338443... | [14.068340301513672, -3.3631186485290527] |
d9f34b89-7a7f-4857-a753-34976e573eb9 | assisted-rtf-vector-based-binaural-direction | 2211.17202 | null | https://arxiv.org/abs/2211.17202v1 | https://arxiv.org/pdf/2211.17202v1.pdf | Assisted RTF-Vector-Based Binaural Direction of Arrival Estimation Exploiting a Calibrated External Microphone Array | Recently, a relative transfer function (RTF)-vector-based method has been proposed to estimate the direction of arrival (DOA) of a target speaker for a binaural hearing aid setup, assuming the availability of external microphones. This method exploits the external microphones to estimate the RTF vector corresponding to... | ['Simon Doclo', 'Daniel Fejgin'] | 2022-11-30 | null | null | null | null | ['direction-of-arrival-estimation'] | ['audio'] | [-2.59587973e-01 -4.27937865e-01 8.15794408e-01 1.62689596e-01
-1.06682062e+00 -6.32225096e-01 2.29808301e-01 6.05408438e-02
-2.07668096e-01 2.22737148e-01 5.62544107e-01 -2.94386327e-01
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-1.87453657e-01 -4.98920958e-03 1.93078190e-01 -3.96520272... | [15.136013984680176, 5.773771286010742] |
ad8fdb9b-d017-43f5-b0d2-dd7cc9c59805 | spatiotemporal-self-supervised-learning-for | 2303.16235 | null | https://arxiv.org/abs/2303.16235v1 | https://arxiv.org/pdf/2303.16235v1.pdf | Spatiotemporal Self-supervised Learning for Point Clouds in the Wild | Self-supervised learning (SSL) has the potential to benefit many applications, particularly those where manually annotating data is cumbersome. One such situation is the semantic segmentation of point clouds. In this context, existing methods employ contrastive learning strategies and define positive pairs by performin... | ['Mathieu Salzmann', 'Sabine Süsstrunk', 'Wei Ke', 'Tong Zhang', 'Yanhao Wu'] | 2023-03-28 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wu_Spatiotemporal_Self-Supervised_Learning_for_Point_Clouds_in_the_Wild_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wu_Spatiotemporal_Self-Supervised_Learning_for_Point_Clouds_in_the_Wild_CVPR_2023_paper.pdf | cvpr-2023-1 | ['point-cloud-segmentation'] | ['computer-vision'] | [ 2.09392533e-01 -2.15832323e-01 -3.41791391e-01 -4.62833524e-01
-8.22178245e-01 -7.12677598e-01 8.59833777e-01 4.99769598e-01
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-2.53913730e-01 7.73948193e-01 8.52730870e-01 -2.62835566... | [8.043432235717773, -2.953274965286255] |
8491e467-23b7-473f-9c96-45be4e4f0fbc | eben-extreme-bandwidth-extension-network-1 | 2210.1409 | null | https://arxiv.org/abs/2210.14090v2 | https://arxiv.org/pdf/2210.14090v2.pdf | EBEN: Extreme bandwidth extension network applied to speech signals captured with noise-resilient body-conduction microphones | In this paper, we present Extreme Bandwidth Extension Network (EBEN), a Generative Adversarial network (GAN) that enhances audio measured with body-conduction microphones. This type of capture equipment suppresses ambient noise at the expense of speech bandwidth, thereby requiring signal enhancement techniques to recov... | ['Éric Bavu', 'Véronique Zimpfer', 'Thomas Joubaud', 'Julien Hauret'] | 2022-10-25 | null | null | null | null | ['bandwidth-extension', 'bandwidth-extension'] | ['audio', 'speech'] | [ 5.22982657e-01 4.91486758e-01 1.85950473e-03 1.19887553e-01
-1.22048569e+00 -5.25524259e-01 5.98412044e-02 -7.34779894e-01
-4.51238714e-02 5.96331537e-01 6.21922910e-01 -1.33841947e-01
3.15947011e-02 -7.87830949e-01 -5.82978427e-01 -8.10002446e-01
-1.57553509e-01 -4.93156910e-01 -2.41611078e-01 -3.10682714... | [15.35477352142334, 6.078392028808594] |
bef2e5d2-07a1-4b41-bc37-081304740e51 | a-comparison-of-deep-saliency-map-generators | 2108.11767 | null | https://arxiv.org/abs/2108.11767v1 | https://arxiv.org/pdf/2108.11767v1.pdf | A Comparison of Deep Saliency Map Generators on Multispectral Data in Object Detection | Deep neural networks, especially convolutional deep neural networks, are state-of-the-art methods to classify, segment or even generate images, movies, or sounds. However, these methods lack of a good semantic understanding of what happens internally. The question, why a COVID-19 detector has classified a stack of lung... | ['Michael Arens', 'David Münch', 'Jens Bayer'] | 2021-08-26 | null | null | null | null | ['multispectral-object-detection'] | ['computer-vision'] | [ 4.73080248e-01 2.35365741e-02 -1.49261728e-01 -1.02563925e-01
-9.61712152e-02 -6.20079875e-01 4.96478379e-01 -8.06140453e-02
-3.03739667e-01 7.30325162e-01 -1.64908066e-01 -4.26784992e-01
-2.70028800e-01 -9.82939839e-01 -5.59339285e-01 -8.59263122e-01
5.48803449e-01 2.38419652e-01 6.61764324e-01 -4.03777599... | [10.004846572875977, 1.8953365087509155] |
ad71ca70-1d96-4a81-8be8-beea89a2e643 | stress-rules-from-surface-forms-experiments | null | null | https://aclanthology.org/2021.icon-main.76 | https://aclanthology.org/2021.icon-main.76.pdf | Stress Rules from Surface Forms: Experiments with Program Synthesis | Learning linguistic generalizations from only a few examples is a challenging task. Recent work has shown that program synthesis – a method to learn rules from data in the form of programs in a domain-specific language – can be used to learn phonological rules in highly data-constrained settings. In this paper, we use ... | ['Dipti Sharma', 'Monojit Choudhury', 'Partho Sarthi', 'Saujas Vaduguru'] | null | null | null | null | icon-2021-12 | ['program-synthesis'] | ['computer-code'] | [ 3.04139763e-01 -1.15936190e-01 -5.52156925e-01 -6.88852251e-01
-5.71127415e-01 -8.52013111e-01 4.29106474e-01 3.81475210e-01
-6.07837915e-01 6.03112221e-01 2.65614390e-01 -8.74322474e-01
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2.73535401e-03 2.44032487e-01 2.73488998e-01 -2.85605401... | [10.663908004760742, 9.260122299194336] |
a4759fde-8384-4ca9-ad65-ebaf307a528a | flood-prediction-using-machine-learning-1 | 2208.01234 | null | https://arxiv.org/abs/2208.01234v1 | https://arxiv.org/pdf/2208.01234v1.pdf | Flood Prediction Using Machine Learning Models | Floods are one of nature's most catastrophic calamities which cause irreversible and immense damage to human life, agriculture, infrastructure and socio-economic system. Several studies on flood catastrophe management and flood forecasting systems have been conducted. The accurate prediction of the onset and progressio... | ['Tanvir Rahman', 'Meherin Hossain Nushra', 'Ipshita Ishrar', 'Ishadie Namir', 'Maisha Farzana', 'Miah Mohammad Asif Syeed'] | 2022-08-02 | null | null | null | null | ['machine-learning', 'machine-learning'] | ['methodology', 'miscellaneous'] | [-7.15370430e-03 -2.35856146e-01 8.25101361e-02 -4.12164241e-01
1.03449516e-01 -3.40131313e-01 2.19748512e-01 9.29476678e-01
-3.00481528e-01 1.06605148e+00 3.31641972e-01 -1.00049961e+00
-3.25654030e-01 -1.40934229e+00 7.52990544e-02 -6.56450093e-01
-6.07205331e-01 2.54977465e-01 1.62875131e-01 -6.62914991... | [9.311513900756836, -1.2035691738128662] |
06c8cccd-16da-40b5-bf0e-dd15f7ab40d1 | ranking-vs-classifying-measuring-knowledge | 2102.06145 | null | https://arxiv.org/abs/2102.06145v1 | https://arxiv.org/pdf/2102.06145v1.pdf | Ranking vs. Classifying: Measuring Knowledge Base Completion Quality | Knowledge base completion (KBC) methods aim at inferring missing facts from the information present in a knowledge base (KB) by estimating the likelihood of candidate facts. In the prevailing evaluation paradigm, models do not actually decide whether a new fact should be accepted or not but are solely judged on the pos... | ['Benjamin Roth', 'Martin Schmitt', 'Marina Speranskaya'] | 2021-02-02 | null | https://openreview.net/forum?id=3pcecaCEK- | https://openreview.net/pdf?id=3pcecaCEK- | akbc-2020-6 | ['knowledge-base-completion', 'knowledge-base-completion'] | ['graphs', 'knowledge-base'] | [-1.04744770e-01 4.72169608e-01 -4.85020787e-01 -4.58215147e-01
-9.43778157e-01 -5.84541321e-01 7.48415530e-01 5.05109191e-01
-7.81871259e-01 1.24313450e+00 4.77252752e-01 -2.70379096e-01
-3.36139143e-01 -1.16522169e+00 -9.85032856e-01 -3.28475296e-01
5.97397313e-02 1.04981363e+00 4.08897460e-01 -3.70982498... | [9.390646934509277, 8.341694831848145] |
e94cd481-1b20-4b6c-8382-7fd527514b76 | variable-selection-for-nonlinear-cox | 2211.09287 | null | https://arxiv.org/abs/2211.09287v1 | https://arxiv.org/pdf/2211.09287v1.pdf | Variable selection for nonlinear Cox regression model via deep learning | Variable selection problem for the nonlinear Cox regression model is considered. In survival analysis, one main objective is to identify the covariates that are associated with the risk of experiencing the event of interest. The Cox proportional hazard model is being used extensively in survival analysis in studying th... | ['Kexuan Li'] | 2022-11-17 | null | null | null | null | ['variable-selection', 'survival-analysis'] | ['methodology', 'miscellaneous'] | [ 1.24494240e-01 -4.73297745e-01 -6.82933629e-01 -7.08392859e-01
-8.38155389e-01 2.71653742e-01 1.69054836e-01 6.27099216e-01
-5.14039397e-01 1.10288894e+00 1.19415475e-02 -2.83638388e-01
-2.51049966e-01 -8.81241143e-01 -2.41886958e-01 -1.15447855e+00
-3.66177708e-01 5.42510808e-01 -4.79333520e-01 -3.96421887... | [7.775826454162598, 5.407729148864746] |
22616729-4dbd-4f4f-89f2-55746b8cae6a | counterfactual-learning-with-multioutput-deep | 2211.11119 | null | https://arxiv.org/abs/2211.11119v1 | https://arxiv.org/pdf/2211.11119v1.pdf | Counterfactual Learning with Multioutput Deep Kernels | In this paper, we address the challenge of performing counterfactual inference with observational data via Bayesian nonparametric regression adjustment, with a focus on high-dimensional settings featuring multiple actions and multiple correlated outcomes. We present a general class of counterfactual multi-task deep ker... | ['Ioanna Manolopoulou', 'Gianluca Baio', 'Alberto Caron'] | 2022-11-20 | null | null | null | null | ['counterfactual-inference'] | ['miscellaneous'] | [ 8.84863734e-02 1.11251399e-01 -4.65929598e-01 -2.11057156e-01
-7.49217629e-01 -1.33537203e-01 9.46004272e-01 -1.62209481e-01
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-6.68865561e-01 -6.72426820e-01 -9.29794431e-01 -6.41026318e-01
-5.76620817e-01 6.17830813e-01 -5.04262269e-01 5.30004203... | [8.029699325561523, 5.375897407531738] |
9ec48bff-37f5-48f8-a050-42c31e2330ed | et-bert-a-contextualized-datagram | 2202.06335 | null | https://arxiv.org/abs/2202.06335v2 | https://arxiv.org/pdf/2202.06335v2.pdf | ET-BERT: A Contextualized Datagram Representation with Pre-training Transformers for Encrypted Traffic Classification | Encrypted traffic classification requires discriminative and robust traffic representation captured from content-invisible and imbalanced traffic data for accurate classification, which is challenging but indispensable to achieve network security and network management. The major limitation of existing solutions is tha... | ['Jing Yu', 'Junzheng Shi', 'Zhen Li', 'Gaopeng Gou', 'Gang Xiong', 'Xinjie Lin'] | 2022-02-13 | null | null | null | null | ['traffic-classification'] | ['miscellaneous'] | [ 1.69242844e-01 -4.73089010e-01 -6.94801867e-01 -3.56626958e-01
-9.88420129e-01 -7.55526483e-01 4.36939031e-01 -3.60051364e-01
-4.03729826e-02 7.83382416e-01 -1.15710631e-01 -1.02659738e+00
-5.74143194e-02 -8.43333781e-01 -7.93593466e-01 -5.96125782e-01
-4.20903899e-02 4.19352919e-01 2.37294853e-01 -3.30786675... | [5.0724992752075195, 7.244496822357178] |
1f25364d-dedc-41fa-baf1-f173991a3353 | structure-guided-multi-modal-pre-trained | 2307.03591 | null | https://arxiv.org/abs/2307.03591v1 | https://arxiv.org/pdf/2307.03591v1.pdf | Structure Guided Multi-modal Pre-trained Transformer for Knowledge Graph Reasoning | Multimodal knowledge graphs (MKGs), which intuitively organize information in various modalities, can benefit multiple practical downstream tasks, such as recommendation systems, and visual question answering. However, most MKGs are still far from complete, which motivates the flourishing of MKG reasoning models. Recen... | ['Xinwang Liu', 'Meng Liu', 'Lingyuan Meng', 'Yue Liu', 'Sihang Zhou', 'Ke Liang'] | 2023-07-06 | null | null | null | null | ['visual-question-answering-1', 'knowledge-graphs', 'recommendation-systems', 'question-answering'] | ['computer-vision', 'knowledge-base', 'miscellaneous', 'natural-language-processing'] | [-6.35684803e-02 2.49745086e-01 -3.29924107e-01 -1.19392946e-01
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5.25810003e-01 2.78872252e-01 2.06045166e-01 -4.67233777... | [10.63102912902832, 1.8644695281982422] |
90ce792f-2794-46f7-8b5d-e00112d92a2a | chatgpt-needs-spade-sustainability-privacy | 2305.03123 | null | https://arxiv.org/abs/2305.03123v1 | https://arxiv.org/pdf/2305.03123v1.pdf | ChatGPT Needs SPADE (Sustainability, PrivAcy, Digital divide, and Ethics) Evaluation: A Review | ChatGPT is another large language model (LLM) inline but due to its performance and ability to converse effectively, it has gained a huge popularity amongst research as well as industrial community. Recently, many studies have been published to show the effectiveness, efficiency, integration, and sentiments of chatGPT ... | ['Kapal Dev', 'Parus Khuwaja', 'Sunder Ali Khowaja'] | 2023-04-13 | null | null | null | null | ['ethics'] | ['miscellaneous'] | [-3.21408093e-01 4.53560591e-01 -1.39661446e-01 -1.05623029e-01
-5.43463156e-02 -6.22680008e-01 9.35825646e-01 2.39557907e-01
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-1.90795198e-01 -3.72148812e-01 -1.30554382e-02 -4.21474665e-01
2.29472533e-01 1.86119094e-01 9.79599282e-02 -2.50516385... | [10.320940017700195, 7.263548851013184] |
df935477-aa1c-4964-9e19-017938b60da0 | mapping-and-cleaning-open-commonsense | 2306.12766 | null | https://arxiv.org/abs/2306.12766v1 | https://arxiv.org/pdf/2306.12766v1.pdf | Mapping and Cleaning Open Commonsense Knowledge Bases with Generative Translation | Structured knowledge bases (KBs) are the backbone of many know\-ledge-intensive applications, and their automated construction has received considerable attention. In particular, open information extraction (OpenIE) is often used to induce structure from a text. However, although it allows high recall, the extracted kn... | ['Simon Razniewski', 'Julien Romero'] | 2023-06-22 | null | null | null | null | ['open-information-extraction'] | ['natural-language-processing'] | [ 8.36505815e-02 9.28857386e-01 -3.85644644e-01 -1.26672044e-01
-7.51741290e-01 -6.74037337e-01 5.41545153e-01 2.86556423e-01
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-2.19802380e-01 -1.28320205e+00 -8.88111413e-01 -4.36499238e-01
4.18249547e-01 8.69618297e-01 9.17163119e-02 -3.33934277... | [9.371479988098145, 8.412247657775879] |
6a1f322e-2798-45e4-87fd-a46843234343 | formulating-neural-sentence-ordering-as-the | null | null | https://aclanthology.org/2021.inlg-1.13 | https://aclanthology.org/2021.inlg-1.13.pdf | Formulating Neural Sentence Ordering as the Asymmetric Traveling Salesman Problem | The task of Sentence Ordering refers to rearranging a set of given sentences in a coherent ordering. Prior work (Prabhumoye et al., 2020) models this as an optimal graph traversal (with sentences as nodes, and edges as local constraints) using topological sorting. However, such an approach has major limitations – it ca... | ['Harsh Jhamtani', 'Vishal Keswani'] | null | null | null | null | inlg-acl-2021-8 | ['sentence-ordering'] | ['natural-language-processing'] | [ 4.90679175e-01 2.89310277e-01 -1.33735970e-01 -5.81689477e-01
-3.23870540e-01 -7.55148530e-01 5.25885224e-01 7.02502668e-01
-4.20084059e-01 6.35163724e-01 9.73149016e-02 -6.87568009e-01
-5.78288794e-01 -1.00384963e+00 -7.82252610e-01 -2.39285529e-01
-3.97875726e-01 6.93804860e-01 5.25069356e-01 -4.03375357... | [10.973098754882812, 8.867088317871094] |
54942f83-75a5-4a13-8ecb-195254d5d2ba | when-machine-unlearning-jeopardizes-privacy | 2005.02205 | null | https://arxiv.org/abs/2005.02205v2 | https://arxiv.org/pdf/2005.02205v2.pdf | When Machine Unlearning Jeopardizes Privacy | The right to be forgotten states that a data owner has the right to erase their data from an entity storing it. In the context of machine learning (ML), the right to be forgotten requires an ML model owner to remove the data owner's data from the training set used to build the ML model, a process known as machine unlea... | ['Zhikun Zhang', 'Yang Zhang', 'Tianhao Wang', 'Min Chen', 'Michael Backes', 'Mathias Humbert'] | 2020-05-05 | null | null | null | null | ['membership-inference-attack'] | ['computer-vision'] | [ 4.35567260e-01 4.65061754e-01 -4.19201761e-01 -2.00733870e-01
-7.29848504e-01 -1.31373072e+00 2.74348557e-01 2.61306226e-01
-3.66344959e-01 7.91241050e-01 -3.29861879e-01 -8.99132729e-01
2.40622610e-01 -9.23185349e-01 -1.32823217e+00 -9.50057924e-01
5.51680103e-02 2.21249089e-02 -8.73802826e-02 5.33805847... | [5.897848129272461, 7.106816291809082] |
e6ab5cc8-2455-4717-9940-0171b0912a7a | biomedical-ner-using-novel-schema-and-distant | null | null | https://aclanthology.org/2022.bionlp-1.15 | https://aclanthology.org/2022.bionlp-1.15.pdf | Biomedical NER using Novel Schema and Distant Supervision | Biomedical Named Entity Recognition (BMNER) is one of the most important tasks in the field of biomedical text mining. Most work so far on this task has not focused on identification of discontinuous and overlapping entities, even though they are present in significant fractions in real-life biomedical datasets. In thi... | ['Kamalakar Karlapalem', 'Veera Raghavendra Chikka', 'Alok Kar', 'Anshita Khandelwal'] | null | null | null | null | bionlp-acl-2022-5 | ['medical-named-entity-recognition'] | ['natural-language-processing'] | [ 2.08883733e-01 5.33081651e-01 -2.82925963e-01 -3.90127271e-01
-7.07967281e-01 -2.10899487e-01 4.49032784e-01 8.30663562e-01
-1.09609509e+00 1.10485148e+00 3.84668022e-01 -1.64317921e-01
-3.76856737e-02 -5.04026532e-01 -6.41580939e-01 -5.43807685e-01
-8.40691626e-02 6.58102334e-01 2.82807916e-01 1.71648003... | [8.566058158874512, 8.791389465332031] |
6c0e7527-9753-4e80-a2af-a4f0e02fb590 | deep-attention-recurrent-q-network | 1512.01693 | null | http://arxiv.org/abs/1512.01693v1 | http://arxiv.org/pdf/1512.01693v1.pdf | Deep Attention Recurrent Q-Network | A deep learning approach to reinforcement learning led to a general learner
able to train on visual input to play a variety of arcade games at the human
and superhuman levels. Its creators at the Google DeepMind's team called the
approach: Deep Q-Network (DQN). We present an extension of DQN by "soft" and
"hard" attent... | ['Anastasiia Ignateva', 'Aleksandr Fedorov', 'Mikhail Pavlov', 'Alexey Seleznev', 'Ivan Sorokin'] | 2015-12-05 | null | null | null | null | ['deep-attention', 'hard-attention', 'deep-attention'] | ['computer-vision', 'methodology', 'natural-language-processing'] | [-5.20179272e-01 1.30921677e-01 5.84977381e-02 1.95839763e-01
-6.47069886e-02 -4.77040052e-01 6.54747486e-01 -1.99008554e-01
-8.30955744e-01 7.13520229e-01 -1.41239971e-01 -5.06985307e-01
-2.88326830e-01 -8.42465401e-01 -5.01924217e-01 -4.61081445e-01
-2.83840299e-01 5.99491000e-01 3.50074410e-01 -8.65999281... | [3.737582206726074, 1.4860949516296387] |
173e5804-c59f-4ee4-b353-b66ff57fcd31 | neural-video-portrait-relighting-in-real-time | 2104.00484 | null | https://arxiv.org/abs/2104.00484v1 | https://arxiv.org/pdf/2104.00484v1.pdf | Neural Video Portrait Relighting in Real-time via Consistency Modeling | Video portraits relighting is critical in user-facing human photography, especially for immersive VR/AR experience. Recent advances still fail to recover consistent relit result under dynamic illuminations from monocular RGB stream, suffering from the lack of video consistency supervision. In this paper, we propose a n... | ['Lan Xu', 'Jingyi Yu', 'Minye Wu', 'Qixuan Zhang', 'Longwen Zhang'] | 2021-04-01 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Zhang_Neural_Video_Portrait_Relighting_in_Real-Time_via_Consistency_Modeling_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Zhang_Neural_Video_Portrait_Relighting_in_Real-Time_via_Consistency_Modeling_ICCV_2021_paper.pdf | iccv-2021-1 | ['single-image-portrait-relighting'] | ['computer-code'] | [ 3.89344990e-01 -5.55883825e-01 -2.69495286e-02 -5.41376829e-01
-5.32256961e-01 -4.90588814e-01 3.75311852e-01 -8.03163052e-01
3.09526408e-03 6.93499625e-01 1.26712859e-01 1.11019410e-01
1.73034862e-01 -5.47907889e-01 -1.23456359e+00 -5.80416322e-01
4.47117269e-01 -1.87773392e-01 -1.47545293e-01 -2.15384632... | [11.408105850219727, -1.1214675903320312] |
d8a7486a-da7d-45d1-9a5e-bf41f8d5cdf9 | extractive-text-summarization-using-neural | 1802.10137 | null | http://arxiv.org/abs/1802.10137v1 | http://arxiv.org/pdf/1802.10137v1.pdf | Extractive Text Summarization using Neural Networks | Text Summarization has been an extensively studied problem. Traditional
approaches to text summarization rely heavily on feature engineering. In
contrast to this, we propose a fully data-driven approach using feedforward
neural networks for single document summarization. We train and evaluate the
model on standard DUC ... | ['Aakash Sinha', 'Akshay Gahlot', 'Abhishek Yadav'] | 2018-02-27 | null | null | null | null | ['extractive-document-summarization'] | ['natural-language-processing'] | [ 4.81544226e-01 3.68755937e-01 -1.44159883e-01 -4.84829158e-01
-8.20736527e-01 -4.59553659e-01 6.27207816e-01 6.06271327e-01
-5.37068725e-01 1.01461339e+00 9.39648330e-01 -1.21346608e-01
7.22077163e-03 -5.46225905e-01 -6.31843328e-01 -2.52483368e-01
7.43504763e-02 5.50308228e-01 1.74249545e-01 -2.23165810... | [12.520356178283691, 9.506135940551758] |
646fd3d1-5f7b-4b79-9a6e-621c399ee198 | hierarchical-neural-representation-of-dreamed | 1611.0952 | null | http://arxiv.org/abs/1611.09520v2 | http://arxiv.org/pdf/1611.09520v2.pdf | Hierarchical Neural Representation of Dreamed Objects Revealed by Brain Decoding with Deep Neural Network Features | Dreaming is generally thought to be generated by spontaneous brain activity
during sleep with patterns common to waking experience. This view is supported
by a recent study demonstrating that dreamed objects can be predicted from
brain activity during sleep using statistical decoders trained with
stimulus-induced brain... | [] | 2017-01-23 | null | null | null | null | ['brain-decoding', 'brain-decoding'] | ['medical', 'miscellaneous'] | [-6.40551895e-02 -9.66672450e-02 1.49044335e-01 -6.85401440e-01
-3.24682623e-01 -2.44951501e-01 6.24729514e-01 -3.75011981e-01
-5.99402547e-01 5.45605958e-01 9.35915470e-01 3.51113677e-01
-1.18087389e-01 -5.37720382e-01 -4.78368074e-01 -7.58033812e-01
-1.85267314e-01 4.28041071e-01 -3.54605615e-01 -2.30206195... | [10.636307716369629, 2.489307165145874] |
508078e9-b738-4d37-ac1c-ad3938fd9858 | probabilistic-deep-learning-with-generalised | null | null | https://openreview.net/forum?id=L_jGauvvbu0 | https://openreview.net/pdf?id=L_jGauvvbu0 | Probabilistic Deep Learning with Generalised Variational Inference | We study probabilistic Deep Learning methods through the lens of Approximate Bayesian Inference. In particular, we examine Bayesian Neural Networks (BNNs), which usually suffer from multiple ill-posed assumptions such as prior and likelihood misspecification. In this direction, we investigate a recently proposed approx... | ['Brooks Paige', 'Theo Damoulas', 'Giorgos Felekis'] | 2021-11-22 | null | null | null | pproximateinference-aabi-symposium-2022-2 | ['probabilistic-deep-learning'] | ['computer-vision'] | [ 9.88421496e-03 1.80540860e-01 3.04556876e-01 -4.85527426e-01
-7.00240195e-01 -2.42508367e-01 9.08967793e-01 -3.34363610e-01
-4.93650436e-01 1.26109707e+00 -6.10359572e-02 -2.16795683e-01
-4.87754673e-01 -7.39744067e-01 -1.01235390e+00 -8.08858752e-01
1.02145746e-01 6.90971613e-01 -7.14289024e-02 4.70561147... | [7.0425333976745605, 3.882415294647217] |
99c25dd2-aedf-4859-a13f-dcfddb066030 | automated-machine-learning-for-remaining | 2306.12215 | null | https://arxiv.org/abs/2306.12215v1 | https://arxiv.org/pdf/2306.12215v1.pdf | Automated Machine Learning for Remaining Useful Life Predictions | Being able to predict the remaining useful life (RUL) of an engineering system is an important task in prognostics and health management. Recently, data-driven approaches to RUL predictions are becoming prevalent over model-based approaches since no underlying physical knowledge of the engineering system is required. Y... | ['Marco F. Huber', 'Marius Lindauer', 'Peter Zeiler', 'Fabian Mauthe', 'Marc-André Zöller'] | 2023-06-21 | null | null | null | null | ['automl', 'management'] | ['methodology', 'miscellaneous'] | [-2.28751986e-03 2.81564087e-01 1.86091930e-01 -3.70517820e-01
-9.77510393e-01 -2.23675177e-01 2.86956877e-01 6.64922655e-01
9.63609517e-02 1.00862026e+00 -4.50407892e-01 -7.57732272e-01
-5.09582639e-01 -8.19653571e-01 -5.49339294e-01 -5.89440703e-01
7.83300698e-02 9.72093046e-01 4.76308942e-01 -2.42553473... | [6.7730937004089355, 2.666893243789673] |
ea2c4870-1d1a-4fc7-b4ca-e13d644c3023 | a-whisper-transformer-for-audio-captioning | 2305.0969 | null | https://arxiv.org/abs/2305.09690v1 | https://arxiv.org/pdf/2305.09690v1.pdf | A Whisper transformer for audio captioning trained with synthetic captions and transfer learning | The field of audio captioning has seen significant advancements in recent years, driven by the availability of large-scale audio datasets and advancements in deep learning techniques. In this technical report, we present our approach to audio captioning, focusing on the use of a pretrained speech-to-text Whisper model ... | ['Radosław Winiecki', 'Jürgen Kieslich', 'Adam Hájek', 'Marek Kadlčík'] | 2023-05-15 | null | null | null | null | ['audio-captioning'] | ['audio'] | [ 4.07599211e-01 2.59692639e-01 1.83818787e-02 -5.84430456e-01
-1.58691430e+00 -4.97174501e-01 4.62915629e-01 -1.78910613e-01
1.24330848e-01 6.00236833e-01 8.43878329e-01 7.25399032e-02
3.09982568e-01 -5.71584851e-02 -7.72930682e-01 -2.72667587e-01
-2.54163593e-01 6.73589647e-01 1.55282277e-03 -1.71431810... | [15.285457611083984, 4.852954864501953] |
ced4b667-2576-4099-893f-2d7db6389499 | exploration-by-random-network-distillation | 1810.12894 | null | http://arxiv.org/abs/1810.12894v1 | http://arxiv.org/pdf/1810.12894v1.pdf | Exploration by Random Network Distillation | We introduce an exploration bonus for deep reinforcement learning methods
that is easy to implement and adds minimal overhead to the computation
performed. The bonus is the error of a neural network predicting features of
the observations given by a fixed randomly initialized neural network. We also
introduce a method ... | ['Yuri Burda', 'Amos Storkey', 'Oleg Klimov', 'Harrison Edwards'] | 2018-10-30 | null | https://openreview.net/forum?id=H1lJJnR5Ym | https://openreview.net/pdf?id=H1lJJnR5Ym | iclr-2019 | ['montezumas-revenge'] | ['playing-games'] | [-2.08648145e-01 5.69039762e-01 -8.51731971e-02 -6.47318875e-03
-5.27494967e-01 -5.17818511e-01 6.15085304e-01 -2.85876095e-01
-9.70297039e-01 1.16491163e+00 -3.83538097e-01 -3.27368677e-01
-1.22134872e-01 -7.87834466e-01 -8.22795391e-01 -6.47918284e-01
-6.11368716e-01 6.59519851e-01 6.06519245e-02 -7.56822467... | [3.838038682937622, 1.6538687944412231] |
5dd85be7-19b8-4dc3-83b4-a643100e8ef4 | m-text-kg-a-library-for-multi-source | 2207.11442 | null | https://arxiv.org/abs/2207.11442v2 | https://arxiv.org/pdf/2207.11442v2.pdf | $μ\text{KG}$: A Library for Multi-source Knowledge Graph Embeddings and Applications | This paper presents $\mu\text{KG}$, an open-source Python library for representation learning over knowledge graphs. $\mu\text{KG}$ supports joint representation learning over multi-source knowledge graphs (and also a single knowledge graph), multiple deep learning libraries (PyTorch and TensorFlow2), multiple embeddin... | ['Wei Hu', 'Zequn Sun', 'Xindi Luo'] | 2022-07-23 | null | null | null | null | ['graph-question-answering', 'knowledge-graph-embeddings', 'knowledge-graph-embeddings', 'entity-typing'] | ['graphs', 'graphs', 'methodology', 'natural-language-processing'] | [-3.87195796e-01 3.91347796e-01 -5.24493456e-01 -1.31194547e-01
-6.07919157e-01 -5.70943236e-01 8.32389966e-02 8.98209691e-01
-3.38516682e-01 6.15231514e-01 7.42693394e-02 -6.19177341e-01
-7.81950057e-01 -1.38360786e+00 -5.80862999e-01 -3.27912867e-01
-6.42262578e-01 6.66456342e-01 4.11600798e-01 -2.88080841... | [8.777631759643555, 7.902659893035889] |
817e28ca-35f7-4977-bb6a-52c17ec89edd | broad-context-language-modeling-as-reading | 1610.08431 | null | http://arxiv.org/abs/1610.08431v3 | http://arxiv.org/pdf/1610.08431v3.pdf | Broad Context Language Modeling as Reading Comprehension | Progress in text understanding has been driven by large datasets that test
particular capabilities, like recent datasets for reading comprehension
(Hermann et al., 2015). We focus here on the LAMBADA dataset (Paperno et al.,
2016), a word prediction task requiring broader context than the immediate
sentence. We view LA... | ['David Mcallester', 'Zewei Chu', 'Kevin Gimpel', 'Hai Wang'] | 2016-10-26 | broad-context-language-modeling-as-reading-1 | https://aclanthology.org/E17-2009 | https://aclanthology.org/E17-2009.pdf | eacl-2017-4 | ['lambada'] | ['natural-language-processing'] | [ 6.62697017e-01 6.60518765e-01 -1.89678699e-01 -4.31750238e-01
-9.07327712e-01 -7.41033554e-01 8.68089974e-01 6.28515959e-01
-6.96235001e-01 5.33158123e-01 8.70025814e-01 -8.23945999e-01
-2.31301948e-01 -7.75009930e-01 -5.33045530e-01 -1.24069475e-01
3.34872514e-01 7.94757843e-01 1.72955304e-01 -6.52297616... | [11.152833938598633, 8.199649810791016] |
44e602a5-46e9-436b-97b8-5c215e8dd03c | temporal-sub-sampling-of-audio-feature | 2007.02676 | null | https://arxiv.org/abs/2007.02676v1 | https://arxiv.org/pdf/2007.02676v1.pdf | Temporal Sub-sampling of Audio Feature Sequences for Automated Audio Captioning | Audio captioning is the task of automatically creating a textual description for the contents of a general audio signal. Typical audio captioning methods rely on deep neural networks (DNNs), where the target of the DNN is to map the input audio sequence to an output sequence of words, i.e. the caption. Though, the leng... | ['Tuomas Virtanen', 'Khoa Nguyen', 'Konstantinos Drossos'] | 2020-07-06 | null | null | null | null | ['audio-captioning'] | ['audio'] | [ 6.52685642e-01 1.84557319e-01 3.01986188e-01 -3.03069919e-01
-8.83423686e-01 -6.33175135e-01 8.31076980e-01 3.43176544e-01
-5.19975245e-01 7.46625364e-01 5.98957539e-01 -4.89888750e-02
1.36992440e-01 -5.23714304e-01 -1.03848445e+00 -4.94959593e-01
-1.53116316e-01 4.55608010e-01 2.09926814e-01 -9.63318273... | [15.284660339355469, 4.910427093505859] |
2d213761-7a97-4017-a6f6-a2bc549d2651 | comparison-of-uncertainty-quantification-with | 2211.06233 | null | https://arxiv.org/abs/2211.06233v1 | https://arxiv.org/pdf/2211.06233v1.pdf | Comparison of Uncertainty Quantification with Deep Learning in Time Series Regression | Increasingly high-stakes decisions are made using neural networks in order to make predictions. Specifically, meteorologists and hedge funds apply these techniques to time series data. When it comes to prediction, there are certain limitations for machine learning models (such as lack of expressiveness, vulnerability o... | ['Matias Valdenegro-Toro', 'Levente Foldesi'] | 2022-11-11 | null | null | null | null | ['time-series-regression'] | ['time-series'] | [-1.04110010e-01 5.07202804e-01 -3.03194135e-01 -7.12458253e-01
-2.32716039e-01 -5.31307161e-01 7.47895777e-01 1.96638212e-01
-4.54736799e-01 1.36569619e+00 4.21991587e-01 -8.39938104e-01
-4.83726114e-01 -8.80574226e-01 -5.88491917e-01 -3.82718295e-01
3.43667232e-02 1.99603528e-01 -1.35451883e-01 7.95901567... | [7.320659160614014, 4.055183410644531] |
a762c775-45e6-4faa-9e0c-943c2ac640c1 | light-weight-deep-extreme-multilabel | 2304.11045 | null | https://arxiv.org/abs/2304.11045v1 | https://arxiv.org/pdf/2304.11045v1.pdf | Light-weight Deep Extreme Multilabel Classification | Extreme multi-label (XML) classification refers to the task of supervised multi-label learning that involves a large number of labels. Hence, scalability of the classifier with increasing label dimension is an important consideration. In this paper, we develop a method called LightDXML which modifies the recently devel... | ['Pawan Kumar', 'Bamdev Mishra', 'Pratik Jawanpuria', 'Arpan Dasgupta', 'Istasis Mishra'] | 2023-04-20 | null | null | null | null | ['multi-label-learning'] | ['methodology'] | [ 2.39264607e-01 -6.81021884e-02 -4.31882173e-01 -6.10593259e-01
-9.44493175e-01 -4.45312232e-01 5.04977465e-01 6.11782312e-01
-5.66078484e-01 7.73327351e-01 9.59474780e-03 -5.09223342e-01
-3.12227011e-01 -7.56017327e-01 -2.15923786e-01 -8.03196251e-01
1.32858664e-01 6.57976329e-01 1.58089213e-02 3.09047818... | [9.518860816955566, 4.382863521575928] |
efee30ae-ab21-46c7-9772-2d1b75c59c1c | spcnet-stepwise-point-cloud-completion | 2209.01746 | null | https://arxiv.org/abs/2209.01746v1 | https://arxiv.org/pdf/2209.01746v1.pdf | SPCNet: Stepwise Point Cloud Completion Network | How will you repair a physical object with large missings? You may first recover its global yet coarse shape and stepwise increase its local details. We are motivated to imitate the above physical repair procedure to address the point cloud completion task. We propose a novel stepwise point cloud completion network (SP... | ['Mingqiang Wei', 'Fu Lee Wang', 'Weiming Wang', 'Jun Wang', 'Zhe Zhu', 'Xuequan Lu', 'Honghua Chen', 'Fei Hu'] | 2022-09-05 | null | null | null | null | ['point-cloud-completion'] | ['computer-vision'] | [-1.42534629e-01 1.36200354e-01 1.59104660e-01 -1.32241979e-01
-5.32294273e-01 -2.30813906e-01 2.66965896e-01 1.18568614e-02
1.35740250e-01 3.12371165e-01 -1.81372330e-01 -4.09230053e-01
-2.26412207e-01 -7.86130011e-01 -1.20509529e+00 -5.03549755e-01
-8.48334432e-02 5.31222463e-01 3.39870185e-01 -6.79768100... | [8.420385360717773, -3.6807920932769775] |
58e265b0-3067-4113-a328-734f2b129624 | condenseunet-a-memory-efficient-condensely | 2004.02249 | null | https://arxiv.org/abs/2004.02249v1 | https://arxiv.org/pdf/2004.02249v1.pdf | CondenseUNet: A Memory-Efficient Condensely-Connected Architecture for Bi-ventricular Blood Pool and Myocardium Segmentation | With the advent of Cardiac Cine Magnetic Resonance (CMR) Imaging, there has been a paradigm shift in medical technology, thanks to its capability of imaging different structures within the heart without ionizing radiation. However, it is very challenging to conduct pre-operative planning of minimally invasive cardiac p... | ['Cristian A. Linte', 'S. M. Kamrul Hasan'] | 2020-04-05 | null | null | null | null | ['cardiac-segmentation'] | ['medical'] | [ 2.39513367e-01 1.08527869e-01 1.62837952e-01 -2.13950202e-01
-3.22346538e-01 -5.98327458e-01 1.14630848e-01 3.28441858e-01
-5.95262885e-01 6.91292882e-01 -2.37771302e-01 -6.84777915e-01
3.61752138e-02 -7.03134239e-01 -9.29293111e-02 -7.37699807e-01
-3.44544828e-01 7.80445158e-01 2.45092154e-01 3.62764537... | [14.205362319946289, -2.4651436805725098] |
915e9abd-84a8-4eef-93b3-9bc5e1664fff | seqnet-learning-descriptors-for-sequence | 2102.11603 | null | https://arxiv.org/abs/2102.11603v2 | https://arxiv.org/pdf/2102.11603v2.pdf | SeqNet: Learning Descriptors for Sequence-based Hierarchical Place Recognition | Visual Place Recognition (VPR) is the task of matching current visual imagery from a camera to images stored in a reference map of the environment. While initial VPR systems used simple direct image methods or hand-crafted visual features, recent work has focused on learning more powerful visual features and further im... | ['Michael Milford', 'Sourav Garg'] | 2021-02-23 | null | null | null | null | ['sequential-place-learning', 'sequential-place-recognition'] | ['robots', 'robots'] | [ 3.98007989e-01 -4.64762956e-01 -9.90277305e-02 -3.73922437e-01
-7.74353266e-01 -7.14098036e-01 9.24811244e-01 3.66626769e-01
-6.59163177e-01 3.18618715e-01 4.66002971e-02 1.05588436e-01
-1.18132465e-01 -8.12990665e-01 -7.60751069e-01 -5.38501263e-01
-2.95188963e-01 3.39194387e-01 7.61032462e-01 -3.50772530... | [7.7754364013671875, -1.8471144437789917] |
ed3ba94b-c61e-45f6-8025-a3673263ca43 | the-spectacl-of-nonconvex-clustering-a | 1907.0068 | null | https://arxiv.org/abs/1907.00680v1 | https://arxiv.org/pdf/1907.00680v1.pdf | The SpectACl of Nonconvex Clustering: A Spectral Approach to Density-Based Clustering | When it comes to clustering nonconvex shapes, two paradigms are used to find the most suitable clustering: minimum cut and maximum density. The most popular algorithms incorporating these paradigms are Spectral Clustering and DBSCAN. Both paradigms have their pros and cons. While minimum cut clusterings are sensitive t... | ['Sibylle Hess', 'Wouter Duivesteijn', 'Katharina Morik', 'Philipp Honysz'] | 2019-07-01 | null | null | null | null | ['clustering-algorithms-evaluation'] | ['methodology'] | [-3.51660311e-01 -3.70174080e-01 -2.42654830e-01 -2.21695676e-01
-6.99053645e-01 -6.65973604e-01 3.70618820e-01 7.93427378e-02
-2.75535792e-01 6.21924758e-01 -1.71332911e-01 -1.64193645e-01
-5.67752063e-01 -8.14463913e-01 -2.33646438e-01 -9.81351733e-01
-1.89645104e-02 8.26628923e-01 4.51727957e-01 2.27478430... | [7.539121627807617, 4.52930212020874] |
f507ba6f-a197-40d7-8c26-6247725c8789 | scibertsum-extractive-summarization-for | 2201.08495 | null | https://arxiv.org/abs/2201.08495v1 | https://arxiv.org/pdf/2201.08495v1.pdf | SciBERTSUM: Extractive Summarization for Scientific Documents | The summarization literature focuses on the summarization of news articles. The news articles in the CNN-DailyMail are relatively short documents with about 30 sentences per document on average. We introduce SciBERTSUM, our summarization framework designed for the summarization of long documents like scientific papers ... | ['C Lee Giles', 'Athar Sefid'] | 2022-01-21 | null | null | null | null | ['extractive-summarization'] | ['natural-language-processing'] | [-1.47015467e-01 4.77412164e-01 -2.35900789e-01 -1.68119624e-01
-1.18421042e+00 -5.82621455e-01 7.22596288e-01 7.20915079e-01
-3.69588554e-01 9.79446054e-01 1.36317265e+00 -4.43455055e-02
5.94967455e-02 -5.16043901e-01 -8.79816175e-01 -4.45996255e-01
1.28452899e-02 1.01296656e-01 6.18424118e-02 -1.37590319... | [12.557252883911133, 9.520431518554688] |
f92237e4-52f4-4a89-94c7-76829eb2d622 | xtreme-s-evaluating-cross-lingual-speech | 2203.10752 | null | https://arxiv.org/abs/2203.10752v3 | https://arxiv.org/pdf/2203.10752v3.pdf | XTREME-S: Evaluating Cross-lingual Speech Representations | We introduce XTREME-S, a new benchmark to evaluate universal cross-lingual speech representations in many languages. XTREME-S covers four task families: speech recognition, classification, speech-to-text translation and retrieval. Covering 102 languages from 10+ language families, 3 different domains and 4 task familie... | ['Michael Auli', 'Melvin Johnson', 'Jason Riesa', 'Sebastian Ruder', 'Orhan Firat', 'Jonathan H. Clark', 'Simran Khanuja', 'Vera Axelrod', 'Daan van Esch', 'Mihir Kale', 'Clara Rivera', 'Ye Jia', 'Colin Cherry', 'Anton Lozhkov', 'Patrick von Platen', 'Min Ma', 'Yu Zhang', 'Ankur Bapna', 'Alexis Conneau'] | 2022-03-21 | null | null | null | null | ['speech-to-text-translation'] | ['natural-language-processing'] | [ 1.54320508e-01 -2.18986962e-02 -4.49413836e-01 -5.60812354e-01
-1.64541972e+00 -7.72236049e-01 7.07357764e-01 -2.57826984e-01
-3.80036205e-01 6.24433994e-01 6.08710647e-01 -1.00795007e+00
5.04007399e-01 -1.74481377e-01 -6.00174487e-01 -4.04259801e-01
3.90648752e-01 7.12540209e-01 -1.27371162e-01 -2.74142504... | [14.39675521850586, 6.9865217208862305] |
8decee2c-5128-4e11-9b63-dfc2cbef93f2 | exploring-softly-masked-language-modelling | 2305.0353 | null | https://arxiv.org/abs/2305.03530v2 | https://arxiv.org/pdf/2305.03530v2.pdf | Exploring Softly Masked Language Modelling for Controllable Symbolic Music Generation | This document presents some early explorations of applying Softly Masked Language Modelling (SMLM) to symbolic music generation. SMLM can be seen as a generalisation of masked language modelling (MLM), where instead of each element of the input set being either known or unknown, each element can be known, unknown or pa... | ['Bob L. T. Sturm', 'Nicolas Jonason'] | 2023-05-05 | null | null | null | null | ['music-generation', 'music-generation'] | ['audio', 'music'] | [ 5.63145459e-01 5.34144819e-01 -3.71118218e-01 -2.22156495e-01
-9.84760761e-01 -6.00911081e-01 7.85870135e-01 -6.15208328e-01
1.48366675e-01 8.38781714e-01 4.96588945e-01 -2.95288473e-01
1.24998584e-01 -4.13990200e-01 -8.50538850e-01 -3.54097307e-01
-1.09003022e-01 4.26007658e-01 -1.25473440e-01 -2.47319683... | [15.693034172058105, 5.7549519538879395] |
2d4b1647-1d0b-44f3-9840-4dd52d8ffe31 | incorporating-external-knowledge-into-machine | 1909.02745 | null | https://arxiv.org/abs/1909.02745v1 | https://arxiv.org/pdf/1909.02745v1.pdf | Incorporating External Knowledge into Machine Reading for Generative Question Answering | Commonsense and background knowledge is required for a QA model to answer many nontrivial questions. Different from existing work on knowledge-aware QA, we focus on a more challenging task of leveraging external knowledge to generate answers in natural language for a given question with context. In this paper, we propo... | ['Ming Yan', 'Jiangnan Xia', 'Bin Bi', 'Chen Wu', 'Chenliang Li', 'Wei Wang'] | 2019-09-06 | incorporating-external-knowledge-into-machine-1 | https://aclanthology.org/D19-1255 | https://aclanthology.org/D19-1255.pdf | ijcnlp-2019-11 | ['generative-question-answering'] | ['natural-language-processing'] | [ 0.27264056 0.6811645 -0.0829123 -0.14374126 -1.0907495 -0.84546375
0.70775545 0.3415755 -0.39602652 1.2839886 0.66903895 -0.49102792
-0.09988374 -1.3975536 -0.80639714 0.0225477 0.6347822 0.71248937
0.57716274 -0.7562965 0.38919726 -0.02134978 -1.3930149 0.6250641
1.5080576 0.720081 0.2... | [10.952467918395996, 8.005026817321777] |
38f18a19-cc1b-403e-bf28-013011418539 | provably-consistent-partial-label-learning | 2007.08929 | null | https://arxiv.org/abs/2007.08929v2 | https://arxiv.org/pdf/2007.08929v2.pdf | Provably Consistent Partial-Label Learning | Partial-label learning (PLL) is a multi-class classification problem, where each training example is associated with a set of candidate labels. Even though many practical PLL methods have been proposed in the last two decades, there lacks a theoretical understanding of the consistency of those methods-none of the PLL m... | ['Masashi Sugiyama', 'Xin Geng', 'Lei Feng', 'Jiaqi Lv', 'Bo Han', 'Bo An', 'Miao Xu', 'Gang Niu'] | 2020-07-17 | null | http://proceedings.neurips.cc/paper/2020/hash/7bd28f15a49d5e5848d6ec70e584e625-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/7bd28f15a49d5e5848d6ec70e584e625-Paper.pdf | neurips-2020-12 | ['partial-label-learning'] | ['methodology'] | [ 3.00495207e-01 1.70141205e-01 -2.88883835e-01 -5.79214275e-01
-7.36721933e-01 -4.99159575e-01 4.67209995e-01 3.61785650e-01
-1.88284650e-01 9.65946198e-01 -4.82875258e-01 -1.37913048e-01
-5.73223829e-01 -7.07976639e-01 -5.70582688e-01 -7.34342813e-01
2.07063094e-01 7.81778038e-01 4.20534134e-01 2.05026239... | [9.11936092376709, 4.114560127258301] |
50343b90-6f76-4c5d-a375-2c6f877c3b8b | document-embedding-with-paragraph-vectors | 1507.07998 | null | http://arxiv.org/abs/1507.07998v1 | http://arxiv.org/pdf/1507.07998v1.pdf | Document Embedding with Paragraph Vectors | Paragraph Vectors has been recently proposed as an unsupervised method for
learning distributed representations for pieces of texts. In their work, the
authors showed that the method can learn an embedding of movie review texts
which can be leveraged for sentiment analysis. That proof of concept, while
encouraging, was... | ['Christopher Olah', 'Quoc V. Le', 'Andrew M. Dai'] | 2015-07-29 | null | null | null | null | ['document-embedding'] | ['methodology'] | [-2.07918063e-01 2.17272088e-01 -5.20612061e-01 -5.14511645e-01
-7.33298063e-01 -8.21764827e-01 1.05808485e+00 7.75737464e-01
-5.40052712e-01 3.44737262e-01 9.80593145e-01 -2.98173726e-01
-3.86331975e-02 -7.19066560e-01 -3.49639863e-01 -7.13973820e-01
2.60660708e-01 3.44416618e-01 1.19063750e-01 -2.44600713... | [10.492572784423828, 8.619662284851074] |
d66ab527-8644-4d77-b9db-4e263035db9b | speech-enhancement-and-dereverberation-with | 2208.0583 | null | https://arxiv.org/abs/2208.05830v2 | https://arxiv.org/pdf/2208.05830v2.pdf | Speech Enhancement and Dereverberation with Diffusion-based Generative Models | In this work, we build upon our previous publication and use diffusion-based generative models for speech enhancement. We present a detailed overview of the diffusion process that is based on a stochastic differential equation and delve into an extensive theoretical examination of its implications. Opposed to usual con... | ['Timo Gerkmann', 'Bunlong Lay', 'Jean-Marie Lemercier', 'Simon Welker', 'Julius Richter'] | 2022-08-11 | null | null | null | null | ['speech-dereverberation'] | ['speech'] | [ 2.66058594e-01 1.48598075e-01 5.12094557e-01 -1.41291603e-01
-1.03222442e+00 -4.12063628e-01 7.09782779e-01 -2.26542249e-01
-6.18714929e-01 7.73788393e-01 2.42186308e-01 -3.09934258e-01
-1.13293469e-01 -5.13537467e-01 -3.84921193e-01 -1.03054512e+00
-1.87595077e-02 1.90157354e-01 3.85417879e-01 -3.45445842... | [15.07546615600586, 5.960217475891113] |
b0416241-4377-4426-8f14-0cdefa16c6f9 | unifying-event-detection-and-captioning-as | 2207.08625 | null | https://arxiv.org/abs/2207.08625v1 | https://arxiv.org/pdf/2207.08625v1.pdf | Unifying Event Detection and Captioning as Sequence Generation via Pre-Training | Dense video captioning aims to generate corresponding text descriptions for a series of events in the untrimmed video, which can be divided into two sub-tasks, event detection and event captioning. Unlike previous works that tackle the two sub-tasks separately, recent works have focused on enhancing the inter-task asso... | ['Qin Jin', 'Yuqing Song', 'Qi Zhang'] | 2022-07-18 | null | null | null | null | ['dense-video-captioning'] | ['computer-vision'] | [ 4.06960875e-01 -1.24583971e-02 -5.59411198e-02 -4.19405758e-01
-8.90069544e-01 -4.46286827e-01 6.82667255e-01 3.80580984e-02
-2.90003628e-01 6.86019838e-01 6.46320820e-01 1.64545834e-01
2.47694641e-01 -4.52467710e-01 -7.69901335e-01 -3.26981664e-01
-1.57655790e-01 2.35561490e-01 5.13390422e-01 8.28767195... | [10.374902725219727, 0.6835125684738159] |
ec0f3ddd-d9e1-4f8c-aad9-a061fc2cfed9 | cntn-cyclic-noise-tolerant-network-for-gait | 2210.0691 | null | https://arxiv.org/abs/2210.06910v1 | https://arxiv.org/pdf/2210.06910v1.pdf | CNTN: Cyclic Noise-tolerant Network for Gait Recognition | Gait recognition aims to identify individuals by recognizing their walking patterns. However, an observation is made that most of the previous gait recognition methods degenerate significantly due to two memorization effects, namely appearance memorization and label noise memorization. To address the problem, for the f... | ['Liang Wang', 'Chunshui Cao', 'Yan Huang', 'Hongyuan Yu', 'Weichen Yu'] | 2022-10-13 | null | null | null | null | ['gait-recognition'] | ['computer-vision'] | [ 3.22321773e-01 -2.71700889e-01 -1.22404940e-01 -2.03183472e-01
2.15217341e-02 2.03654140e-01 1.39535233e-01 -1.42469302e-01
-5.04261732e-01 8.51729751e-01 -6.11222014e-02 3.33088487e-01
-2.81993505e-02 -1.06852710e+00 -4.87643540e-01 -1.08838487e+00
-8.80901664e-02 4.20812011e-01 3.77731442e-01 -2.75678456... | [14.305439949035645, 1.4132035970687866] |
cfe4e510-f1c3-438d-950c-9dd9db962446 | multi-modal-visual-tracking-review-and | 2012.04176 | null | https://arxiv.org/abs/2012.04176v1 | https://arxiv.org/pdf/2012.04176v1.pdf | Multi-modal Visual Tracking: Review and Experimental Comparison | Visual object tracking, as a fundamental task in computer vision, has drawn much attention in recent years. To extend trackers to a wider range of applications, researchers have introduced information from multiple modalities to handle specific scenes, which is a promising research prospect with emerging methods and be... | ['Huchuan Lu', 'Dong Wang', 'Pengyu Zhang'] | 2020-12-08 | null | null | null | null | ['rgb-t-tracking'] | ['computer-vision'] | [-1.55234262e-01 -7.79669046e-01 -3.97929758e-01 -2.33347621e-02
-5.91037393e-01 -8.75460863e-01 4.25802618e-01 -3.85667920e-01
-3.19440573e-01 2.98315257e-01 -1.14778928e-01 -1.40480384e-01
1.93693534e-01 -2.91605622e-01 -4.52870965e-01 -8.39368403e-01
1.51483014e-01 1.44110739e-01 8.46347034e-01 6.86321482... | [6.448585033416748, -2.112894296646118] |
ba9a8208-a68d-4e47-adca-18ed0c32093d | optimal-copula-transport-for-clustering | 1509.08144 | null | http://arxiv.org/abs/1509.08144v2 | http://arxiv.org/pdf/1509.08144v2.pdf | Optimal Copula Transport for Clustering Multivariate Time Series | This paper presents a new methodology for clustering multivariate time series
leveraging optimal transport between copulas. Copulas are used to encode both
(i) intra-dependence of a multivariate time series, and (ii) inter-dependence
between two time series. Then, optimal copula transport allows us to define two
distan... | ['Philippe Donnat', 'Gautier Marti', 'Frank Nielsen'] | 2015-09-27 | null | null | null | null | ['clustering-multivariate-time-series'] | ['time-series'] | [-1.02384582e-01 -7.28778005e-01 1.18463649e-03 -2.39217162e-01
-4.31563586e-01 -1.09163141e+00 5.33063054e-01 6.19480908e-01
-2.83318788e-01 6.07208550e-01 -2.33523846e-01 -4.34188038e-01
-7.38869190e-01 -8.04711699e-01 -4.29582685e-01 -8.00169170e-01
-8.16887319e-01 4.22576725e-01 3.28831375e-01 -2.24269480... | [7.187014102935791, 3.4126603603363037] |
9f0c6942-7f8c-4e95-bf34-6c63cfdd3758 | data-augmentation-for-diverse-voice | 2305.10684 | null | https://arxiv.org/abs/2305.10684v1 | https://arxiv.org/pdf/2305.10684v1.pdf | Data Augmentation for Diverse Voice Conversion in Noisy Environments | Voice conversion (VC) models have demonstrated impressive few-shot conversion quality on the clean, native speech populations they're trained on. However, when source or target speech accents, background noise conditions, or microphone characteristics differ from training, quality voice conversion is not guaranteed. Th... | ['William Yang Wang', 'Amr El Abbadi', 'Michael Saxon', 'Avani Tanna'] | 2023-05-18 | null | null | null | null | ['voice-conversion', 'voice-conversion'] | ['audio', 'speech'] | [ 2.19772175e-01 -8.39975774e-02 2.34294668e-01 -2.49240786e-01
-1.01401877e+00 -6.92010283e-01 4.63076681e-01 -3.87448072e-01
-4.67102975e-01 7.49023259e-01 6.39670491e-01 -5.42303026e-01
2.49214709e-01 -2.76538163e-01 -4.58755255e-01 -4.35756505e-01
2.36015230e-01 1.02650084e-01 -1.97363257e-01 -4.81975585... | [14.893218040466309, 6.430174827575684] |
8ecb12b5-61b0-4316-abb5-bfc92dd7823f | taming-contrast-maximization-for-learning | 2303.05214 | null | https://arxiv.org/abs/2303.05214v1 | https://arxiv.org/pdf/2303.05214v1.pdf | Taming Contrast Maximization for Learning Sequential, Low-latency, Event-based Optical Flow | Event cameras have recently gained significant traction since they open up new avenues for low-latency and low-power solutions to complex computer vision problems. To unlock these solutions, it is necessary to develop algorithms that can leverage the unique nature of event data. However, the current state-of-the-art is... | ['Guido C. H. E. de Croon', 'Christophe De Wagter', 'Kirk Y. W. Scheper', 'Federico Paredes-Vallés'] | 2023-03-09 | null | null | null | null | ['event-based-optical-flow'] | ['computer-vision'] | [ 4.17766809e-01 -3.78374845e-01 -1.52371913e-01 -3.32694888e-01
-6.15348577e-01 -4.91606534e-01 7.55397379e-01 -1.31136496e-02
-5.04697025e-01 5.17634273e-01 2.15930864e-01 -2.08536893e-01
-1.36682943e-01 -5.19274056e-01 -6.92748845e-01 -4.51928794e-01
-7.63391703e-02 1.50554866e-01 6.36644065e-01 2.06497595... | [8.596031188964844, -1.2110975980758667] |
084f425b-76b2-45f6-a4ce-048df0d2b0b3 | quantum-annealing-for-single-image-super | 2304.08924 | null | https://arxiv.org/abs/2304.08924v1 | https://arxiv.org/pdf/2304.08924v1.pdf | Quantum Annealing for Single Image Super-Resolution | This paper proposes a quantum computing-based algorithm to solve the single image super-resolution (SISR) problem. One of the well-known classical approaches for SISR relies on the well-established patch-wise sparse modeling of the problem. Yet, this field's current state of affairs is that deep neural networks (DNNs) ... | ['Luc van Gool', 'Suryansh Kumar', 'Han Yao Choong'] | 2023-04-18 | null | null | null | null | ['image-super-resolution', 'image-enhancement', 'combinatorial-optimization'] | ['computer-vision', 'computer-vision', 'methodology'] | [ 8.40748131e-01 -1.89722255e-01 1.76683038e-01 -1.31650001e-01
-8.80026460e-01 -5.48905171e-02 5.00201404e-01 -1.42989233e-01
-5.89981437e-01 6.33287787e-01 -2.12452784e-01 -8.52761939e-02
-2.65536338e-01 -1.04969716e+00 -4.20541644e-01 -1.11079431e+00
1.84188709e-01 3.69687676e-01 2.19746120e-02 -8.14206302... | [5.574599742889404, 4.959182262420654] |
d5f0d699-c168-41d7-ac0f-544cccaba33b | credit-card-fraud-detection-using-asexual | 2306.01008 | null | https://arxiv.org/abs/2306.01008v1 | https://arxiv.org/pdf/2306.01008v1.pdf | Credit Card Fraud Detection Using Asexual Reproduction Optimization | As the number of credit card users has increased, detecting fraud in this domain has become a vital issue. Previous literature has applied various supervised and unsupervised machine learning methods to find an effective fraud detection system. However, some of these methods require an enormous amount of time to achiev... | ['Mohammadreza Fani Sani', 'Ramin Yavari', 'Nila Bahrambeik', 'Mohammad Reza Sadeghi Moghadam', 'Taha Mansouri', 'Anahita Farhang Ghahfarokhi'] | 2023-05-31 | null | null | null | null | ['fraud-detection'] | ['miscellaneous'] | [ 2.80313641e-01 -1.76949829e-01 -1.10804755e-02 -1.97498336e-01
2.14637965e-01 -4.57027815e-02 3.37725669e-01 6.62058175e-01
-6.95358276e-01 9.40427899e-01 -6.18659854e-01 -6.47334382e-02
-1.59500733e-01 -1.30786502e+00 -3.31688970e-01 -6.43170118e-01
1.39208645e-01 6.40939236e-01 4.94286232e-02 -1.57273009... | [8.145827293395996, 4.739491939544678] |
05a1777c-0f77-444a-b73f-563f588fc97f | unfolding-the-alternating-optimization-for | 2010.02631 | null | https://arxiv.org/abs/2010.02631v4 | https://arxiv.org/pdf/2010.02631v4.pdf | Unfolding the Alternating Optimization for Blind Super Resolution | Previous methods decompose blind super resolution (SR) problem into two sequential steps: \textit{i}) estimating blur kernel from given low-resolution (LR) image and \textit{ii}) restoring SR image based on estimated kernel. This two-step solution involves two independently trained models, which may not be well compati... | ['Tieniu Tan', 'Liang Wang', 'Shang Li', 'Yan Huang', 'Zhengxiong Luo'] | 2020-10-06 | null | http://proceedings.neurips.cc/paper/2020/hash/3d2d8ccb37df977cb6d9da15b76c3f3a-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/3d2d8ccb37df977cb6d9da15b76c3f3a-Paper.pdf | neurips-2020-12 | ['burst-image-super-resolution'] | ['computer-vision'] | [ 1.94919154e-01 -3.17803651e-01 2.13240102e-01 -2.08674669e-01
-9.02383864e-01 -5.05568862e-01 1.38883218e-01 -6.61065519e-01
-2.43354470e-01 8.17645371e-01 1.97471797e-01 -3.01544756e-01
-1.25139989e-02 -5.38696170e-01 -7.40176558e-01 -7.69940972e-01
4.26234424e-01 -2.06991121e-01 2.03651935e-01 1.41640872... | [11.414156913757324, -2.5814127922058105] |
ac62ae3e-90f1-49a3-aad7-8dc01dacb988 | vidm-video-implicit-diffusion-models | 2212.00235 | null | https://arxiv.org/abs/2212.00235v1 | https://arxiv.org/pdf/2212.00235v1.pdf | VIDM: Video Implicit Diffusion Models | Diffusion models have emerged as a powerful generative method for synthesizing high-quality and diverse set of images. In this paper, we propose a video generation method based on diffusion models, where the effects of motion are modeled in an implicit condition manner, i.e. one can sample plausible video motions accor... | ['Vishal M. Patel', 'Kangfu Mei'] | 2022-12-01 | null | null | null | null | ['video-generation'] | ['computer-vision'] | [ 2.04485282e-01 -2.57550418e-01 -3.26904766e-02 1.23164080e-01
-3.43045384e-01 -5.82431555e-01 8.88626873e-01 -6.20827496e-01
-9.09726843e-02 8.34984303e-01 4.49106067e-01 1.21044025e-01
3.64961028e-02 -9.14870739e-01 -6.46004677e-01 -9.93907273e-01
1.77136227e-01 -1.27140999e-01 3.63642961e-01 -7.01482967... | [11.000083923339844, -0.5838242769241333] |
aec3bbc1-e929-4ccb-8bd3-9b33b592bf17 | image-to-image-translation-with-multi-path | 1905.12498 | null | https://arxiv.org/abs/1905.12498v1 | https://arxiv.org/pdf/1905.12498v1.pdf | Image-to-Image Translation with Multi-Path Consistency Regularization | Image translation across different domains has attracted much attention in both machine learning and computer vision communities. Taking the translation from source domain $\mathcal{D}_s$ to target domain $\mathcal{D}_t$ as an example, existing algorithms mainly rely on two kinds of loss for training: One is the discri... | ['Yijun Wang', 'Zhibo Chen', 'Jianxin Lin', 'Tao Qin', 'Yingce Xia'] | 2019-05-29 | null | null | null | null | ['face-to-face-translation'] | ['computer-vision'] | [ 2.47636676e-01 -1.55514166e-01 -2.13961340e-02 -4.66292083e-01
-8.17780495e-01 -5.91322184e-01 3.26897860e-01 -4.36778933e-01
-2.39758268e-01 7.61156261e-01 -2.37980694e-01 -3.64169598e-01
-4.42032591e-02 -1.08330512e+00 -8.85903418e-01 -7.35376537e-01
2.91784585e-01 5.01149595e-01 1.99496314e-01 -2.19073623... | [11.754684448242188, -0.3893105387687683] |
ea9e01eb-d2ca-4d64-9127-c1667bb94262 | performance-analysis-of-empirical-open | 2306.16575 | null | https://arxiv.org/abs/2306.16575v1 | https://arxiv.org/pdf/2306.16575v1.pdf | Performance Analysis of Empirical Open-Circuit Voltage Modeling in Lithium Ion Batteries, Part-3: Experimental Results | This paper is the third part of a series of papers about empirical approaches to open circuit voltage (OCV) modeling of lithium-ion batteries. The first part of the series proposed models to quantify various sources of uncertainties in the OCV models; and, the second part of the series presented systematic data collect... | ['Balakumar Balasingam', 'James Nguyen', 'Prarthana Pillai'] | 2023-06-28 | null | null | null | null | ['management'] | ['miscellaneous'] | [-4.90641952e-01 -6.74704611e-01 -3.11124951e-01 -2.67703116e-01
-4.92140472e-01 -5.82213640e-01 5.81600845e-01 8.91000271e-01
-2.57839411e-01 1.28055418e+00 -2.64629334e-01 -5.74977815e-01
-3.61199200e-01 -8.62986743e-01 -7.07701504e-01 -6.32294536e-01
2.07114935e-01 5.25082529e-01 4.37988639e-01 -5.18975891... | [6.326366424560547, 2.764009952545166] |
f2384093-09da-42ab-a588-314405185580 | contextualized-spatio-temporal-contrastive | 2112.05181 | null | https://arxiv.org/abs/2112.05181v2 | https://arxiv.org/pdf/2112.05181v2.pdf | Contextualized Spatio-Temporal Contrastive Learning with Self-Supervision | Modern self-supervised learning algorithms typically enforce persistency of instance representations across views. While being very effective on learning holistic image and video representations, such an objective becomes sub-optimal for learning spatio-temporally fine-grained features in videos, where scenes and insta... | ['Ting Liu', 'Hartwig Adam', 'Ming-Hsuan Yang', 'Florian Schroff', 'Boqing Gong', 'Yin Cui', 'Rui Qian', 'Liangzhe Yuan'] | 2021-12-09 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Yuan_Contextualized_Spatio-Temporal_Contrastive_Learning_With_Self-Supervision_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Yuan_Contextualized_Spatio-Temporal_Contrastive_Learning_With_Self-Supervision_CVPR_2022_paper.pdf | cvpr-2022-1 | ['action-localization', 'spatio-temporal-action-localization'] | ['computer-vision', 'computer-vision'] | [ 1.87627688e-01 -2.77755737e-01 -5.55835187e-01 -5.67022562e-01
-6.76285446e-01 -5.54209232e-01 1.00245655e+00 -2.64838561e-02
-3.75699401e-01 6.60550535e-01 5.65457821e-01 -7.11777583e-02
-2.45140553e-01 -5.97878397e-01 -1.08066726e+00 -5.11609435e-01
-3.17984641e-01 1.06049227e-02 1.63705349e-01 -1.24525778... | [8.783595085144043, 0.7800199389457703] |
6ff4c43a-3fe4-4b12-9d3a-2cd4821553b2 | inpaint-anything-segment-anything-meets-image | 2304.0679 | null | https://arxiv.org/abs/2304.06790v1 | https://arxiv.org/pdf/2304.06790v1.pdf | Inpaint Anything: Segment Anything Meets Image Inpainting | Modern image inpainting systems, despite the significant progress, often struggle with mask selection and holes filling. Based on Segment-Anything Model (SAM), we make the first attempt to the mask-free image inpainting and propose a new paradigm of ``clicking and filling'', which is named as Inpaint Anything (IA). The... | ['Zhibo Chen', 'Wenjun Zeng', 'Xin Jin', 'Jinming Liu', 'Ruoyu Feng', 'Runseng Feng', 'Tao Yu'] | 2023-04-13 | null | null | null | null | ['image-inpainting'] | ['computer-vision'] | [ 1.93543717e-01 -1.05223887e-01 7.61208981e-02 -6.27471432e-02
-7.15173364e-01 -5.01422107e-01 2.91233271e-01 -1.55334651e-01
-2.38495305e-01 6.44157052e-01 1.67797297e-01 -2.45408654e-01
3.25586140e-01 -5.18754959e-01 -7.73601532e-01 -5.19632041e-01
3.98462296e-01 2.53988534e-01 5.49693942e-01 -3.64504486... | [11.273577690124512, -0.6605263352394104] |
a2f5a488-6d42-42e3-9227-d2d2fb55233b | pac-prediction-sets-for-large-language-models | 2302.08703 | null | https://arxiv.org/abs/2302.08703v2 | https://arxiv.org/pdf/2302.08703v2.pdf | PAC Prediction Sets for Large Language Models of Code | Prediction sets have recently been shown to be a promising strategy for quantifying the uncertainty of deep neural networks in a way that provides theoretical guarantees. However, existing techniques have largely targeted settings where the space of labels is simple, so prediction sets can be arbitrary subsets of label... | ['Osbert Bastani', 'Stephen Mell', 'Adam Khakhar'] | 2023-02-17 | null | null | null | null | ['semantic-parsing'] | ['natural-language-processing'] | [ 3.03832471e-01 9.73363340e-01 -5.34717381e-01 -7.66648710e-01
-1.13467336e+00 -8.42599750e-01 2.11962268e-01 2.56723352e-03
2.34160602e-01 3.59164387e-01 -1.19200066e-01 -6.60937905e-01
1.50589466e-01 -1.19594991e+00 -1.57927132e+00 -3.89241338e-01
-2.69182384e-01 7.95309782e-01 2.77456224e-01 1.46679223... | [8.00947380065918, 7.612479209899902] |
830224fc-ccad-4cbb-9755-35a9dd4f574b | visual-abstraction-and-reasoning-through | 2303.04091 | null | https://arxiv.org/abs/2303.04091v3 | https://arxiv.org/pdf/2303.04091v3.pdf | Abstract Visual Reasoning Enabled by Language | While artificial intelligence (AI) models have achieved human or even superhuman performance in many well-defined applications, they still struggle to show signs of broad and flexible intelligence. The Abstraction and Reasoning Corpus (ARC), a visual intelligence benchmark introduced by Fran\c{c}ois Chollet, aims to as... | ['Roger Wattenhofer', 'Joël Mathys', 'Benjamin Estermann', 'Loic Houmard', 'Giacomo Camposampiero'] | 2023-03-07 | null | null | null | null | ['visual-reasoning', 'visual-reasoning'] | ['computer-vision', 'reasoning'] | [ 2.27876961e-01 3.50983977e-01 -5.51139005e-02 -2.19264373e-01
-2.59717643e-01 -7.17479587e-01 1.27707827e+00 -7.04989657e-02
-3.91778916e-01 3.57093155e-01 2.88888037e-01 -4.53829229e-01
-2.55935967e-01 -5.15751958e-01 -6.35987997e-01 -2.31608510e-01
1.12760983e-01 8.55435014e-01 2.00423211e-01 -3.12521279... | [10.586837768554688, 2.1928224563598633] |
7bec0dd0-b647-4bda-a8b3-3e62e2b54304 | autokge-searching-scoring-functions-for | 1904.11682 | null | https://arxiv.org/abs/1904.11682v3 | https://arxiv.org/pdf/1904.11682v3.pdf | AutoSF: Searching Scoring Functions for Knowledge Graph Embedding | Scoring functions (SFs), which measure the plausibility of triplets in knowledge graph (KG), have become the crux of KG embedding. Lots of SFs, which target at capturing different kinds of relations in KGs, have been designed by humans in recent years. However, as relations can exhibit complex patterns that are hard to... | ['Yongqi Zhang', 'Quanming Yao', 'Lei Chen', 'Wenyuan Dai'] | 2019-04-26 | null | null | null | null | ['link-property-prediction'] | ['graphs'] | [-1.87199712e-01 2.61314601e-01 -6.56322539e-01 -2.24290743e-01
-8.11623260e-02 -3.40383321e-01 4.49272811e-01 2.49293089e-01
1.07437529e-01 9.14598167e-01 1.58009365e-01 -3.36180329e-01
-7.70019889e-01 -1.17894816e+00 -6.59742117e-01 -5.17542124e-01
-2.66662419e-01 6.59492791e-01 5.57491601e-01 -3.21857125... | [8.805641174316406, 7.883284568786621] |
3780563e-01f5-425d-8336-8c3cea03669d | lcd-learned-cross-domain-descriptors-for-2d | 1911.09326 | null | https://arxiv.org/abs/1911.09326v1 | https://arxiv.org/pdf/1911.09326v1.pdf | LCD: Learned Cross-Domain Descriptors for 2D-3D Matching | In this work, we present a novel method to learn a local cross-domain descriptor for 2D image and 3D point cloud matching. Our proposed method is a dual auto-encoder neural network that maps 2D and 3D input into a shared latent space representation. We show that such local cross-domain descriptors in the shared embeddi... | ['Sai-Kit Yeung', 'Binh-Son Hua', 'Quang-Hieu Pham', 'Gemma Roig', 'Mikaela Angelina Uy', 'Duc Thanh Nguyen'] | 2019-11-21 | null | null | null | null | ['3d-point-cloud-matching'] | ['computer-vision'] | [-1.33493185e-01 -3.43507230e-01 -2.39788294e-01 -4.80562299e-01
-1.28078282e+00 -6.54507041e-01 5.68497241e-01 -1.77660391e-01
-1.96846575e-01 2.77301311e-01 8.88627470e-02 7.45621845e-02
-2.08108842e-01 -8.12948644e-01 -9.29010808e-01 -5.95374703e-01
1.00743338e-01 5.97973228e-01 -1.89444683e-02 3.82797141... | [7.97990083694458, -2.949850082397461] |
c895c24b-352f-44ad-a396-3c831331e34e | sups-a-simulated-underground-parking-scenario | 2302.12966 | null | https://arxiv.org/abs/2302.12966v1 | https://arxiv.org/pdf/2302.12966v1.pdf | SUPS: A Simulated Underground Parking Scenario Dataset for Autonomous Driving | Automatic underground parking has attracted considerable attention as the scope of autonomous driving expands. The auto-vehicle is supposed to obtain the environmental information, track its location, and build a reliable map of the scenario. Mainstream solutions consist of well-trained neural networks and simultaneous... | ['Jian Pu', 'Taiping Zeng', 'xiangyang xue', 'Guang Chen', 'Yurong Cheng', 'Qi Chen', 'Jiawei Hou'] | 2023-02-25 | null | null | null | null | ['simultaneous-localization-and-mapping', 'unity'] | ['computer-vision', 'computer-vision'] | [-1.67131588e-01 -2.67468214e-01 -1.87027678e-01 -8.05265367e-01
-5.80945432e-01 -4.56387132e-01 4.65464085e-01 -1.56515595e-02
-8.06647241e-01 7.82824457e-01 -3.57280344e-01 -4.31590319e-01
2.48860300e-01 -9.38972175e-01 -9.17942107e-01 -3.29421937e-01
1.03475429e-01 8.25564921e-01 7.19064236e-01 -3.56339693... | [7.540064811706543, -2.143901824951172] |
31837529-047c-4f3c-a5f1-bafb90b4445b | partial-adversarial-domain-adaptation | 1808.04205 | null | http://arxiv.org/abs/1808.04205v1 | http://arxiv.org/pdf/1808.04205v1.pdf | Partial Adversarial Domain Adaptation | Domain adversarial learning aligns the feature distributions across the
source and target domains in a two-player minimax game. Existing domain
adversarial networks generally assume identical label space across different
domains. In the presence of big data, there is strong motivation of
transferring deep models from e... | ['Jian-Min Wang', 'Zhangjie Cao', 'Mingsheng Long', 'Lijia Ma'] | 2018-08-10 | partial-adversarial-domain-adaptation-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Zhangjie_Cao_Partial_Adversarial_Domain_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Zhangjie_Cao_Partial_Adversarial_Domain_ECCV_2018_paper.pdf | eccv-2018-9 | ['partial-domain-adaptation'] | ['methodology'] | [ 1.64562374e-01 1.25001565e-01 -2.76438802e-01 -4.97490525e-01
-9.67755675e-01 -1.23320413e+00 5.89278340e-01 -1.61024600e-01
-5.99146485e-01 1.12648845e+00 -1.55101065e-02 5.01618125e-02
2.50565499e-01 -1.05189776e+00 -9.03800547e-01 -6.97587252e-01
3.85956019e-01 8.51660728e-01 3.47303450e-01 -4.56896156... | [10.347396850585938, 3.128276824951172] |
56efd82b-76d4-40e5-8cfe-5e73a0d62ecf | monte-carlo-dropout-ensembles-for-robust | 2007.10114 | null | https://arxiv.org/abs/2007.10114v1 | https://arxiv.org/pdf/2007.10114v1.pdf | Monte Carlo Dropout Ensembles for Robust Illumination Estimation | Computational color constancy is a preprocessing step used in many camera systems. The main aim is to discount the effect of the illumination on the colors in the scene and restore the original colors of the objects. Recently, several deep learning-based approaches have been proposed to solve this problem and they ofte... | ['Alexandros Iosifidis', 'Moncef Gabbouj', 'Jenni Raitoharju', 'Jarno Nikkanen', 'Firas Laakom'] | 2020-07-20 | null | null | null | null | ['color-constancy'] | ['computer-vision'] | [-9.13218334e-02 -3.35483491e-01 3.49473268e-01 -5.47342837e-01
-8.57204139e-01 -4.30826694e-01 6.26953423e-01 7.42745697e-02
-7.31255770e-01 7.88286984e-01 -3.75132084e-01 1.96166277e-01
4.38085459e-02 -4.78918821e-01 -9.62447107e-01 -9.69052494e-01
3.66201788e-01 3.57872546e-01 2.82967567e-01 3.83291274... | [10.440155029296875, -2.4881718158721924] |
e4950f99-56b9-4263-86c2-992a9a63d5dd | enhanced-knowledge-graphs-using-typed | null | null | https://openreview.net/forum?id=W0-o-iIrHdf | https://openreview.net/pdf?id=W0-o-iIrHdf | Enhanced Knowledge Graphs Using Typed Entailment Graphs | Constructing knowledge graphs from open-domain corpora is a crucial stage in question answering. Most previous works are based on open information extraction methods, which extract relations by parsing sentences into triples <e1, r, e2>. These methods lack inference ability and are limited by corpus. When the query i... | ['Anonymous'] | 2022-01-20 | null | null | null | acl-arr-january-2022-1 | ['open-information-extraction'] | ['natural-language-processing'] | [-1.54316559e-01 5.97812474e-01 -2.49944851e-01 -1.73723862e-01
-7.74404466e-01 -9.15012777e-01 2.76088655e-01 5.66264510e-01
-8.85730460e-02 8.81150782e-01 2.47513756e-01 -6.83487296e-01
-4.32487756e-01 -1.45507884e+00 -7.91435361e-01 1.38450578e-01
1.41481265e-01 6.11202836e-01 1.02028286e+00 -6.66388929... | [10.473465919494629, 7.9604291915893555] |
69f8d708-e98e-424e-8b9c-54e4a20501d0 | group-invariant-tensor-train-networks-for | 2206.15051 | null | https://arxiv.org/abs/2206.15051v1 | https://arxiv.org/pdf/2206.15051v1.pdf | Group-invariant tensor train networks for supervised learning | Invariance has recently proven to be a powerful inductive bias in machine learning models. One such class of predictive or generative models are tensor networks. We introduce a new numerical algorithm to construct a basis of tensors that are invariant under the action of normal matrix representations of an arbitrary di... | ['Nick Vannieuwenhoven', 'Brent Sprangers'] | 2022-06-30 | null | null | null | null | ['tensor-networks'] | ['methodology'] | [ 3.21248591e-01 2.08135438e-03 -2.69616067e-01 -4.91985142e-01
-2.17204496e-01 -6.56969786e-01 7.95860529e-01 1.00534506e-01
-3.47937435e-01 5.99411249e-01 2.46646047e-01 -3.63766849e-01
-3.30270469e-01 -8.55889797e-01 -7.08189368e-01 -8.84369552e-01
-3.22609544e-01 7.78429747e-01 1.65134192e-01 -5.47105432... | [5.976195812225342, 5.038966655731201] |
e90f5619-b135-41d4-bb36-204f6aa986e4 | keyphrase-generation-beyond-the-boundaries-of | 2112.06776 | null | https://arxiv.org/abs/2112.06776v2 | https://arxiv.org/pdf/2112.06776v2.pdf | Keyphrase Generation Beyond the Boundaries of Title and Abstract | Keyphrase generation aims at generating important phrases (keyphrases) that best describe a given document. In scholarly domains, current approaches have largely used only the title and abstract of the articles to generate keyphrases. In this paper, we comprehensively explore whether the integration of additional infor... | ['Cornelia Caragea', 'Jishnu Ray Chowdhury', 'Krishna Garg'] | 2021-12-13 | null | null | null | null | ['keyphrase-generation'] | ['natural-language-processing'] | [ 1.53616190e-01 2.72952527e-01 -4.52884197e-01 2.79452175e-01
-1.13793862e+00 -8.09556007e-01 1.14748693e+00 4.40580815e-01
-1.89430609e-01 1.06253493e+00 1.02617025e+00 -1.89498454e-01
-1.03465892e-01 -8.63067210e-01 -1.12413704e+00 -3.52245152e-01
3.79368871e-01 2.37617806e-01 -1.75368667e-01 -3.73089284... | [12.29856014251709, 8.948966026306152] |
c78b6f65-a235-4327-b5b0-3b2cc28dd940 | batch-incremental-triplet-sampling-for | 2007.0561 | null | https://arxiv.org/abs/2007.05610v2 | https://arxiv.org/pdf/2007.05610v2.pdf | Batch-Incremental Triplet Sampling for Training Triplet Networks Using Bayesian Updating Theorem | Variants of Triplet networks are robust entities for learning a discriminative embedding subspace. There exist different triplet mining approaches for selecting the most suitable training triplets. Some of these mining methods rely on the extreme distances between instances, and some others make use of sampling. Howeve... | ['Fakhri Karray', 'Milad Sikaroudi', 'Mark Crowley', 'H. R. Tizhoosh', 'Benyamin Ghojogh'] | 2020-07-10 | null | null | null | null | ['histopathological-image-classification'] | ['medical'] | [ 2.54596531e-01 -2.02118888e-01 -2.89454609e-01 -5.71001232e-01
-7.13129103e-01 -2.18298301e-01 5.33496082e-01 3.46900105e-01
-4.43393528e-01 8.46571088e-01 -1.21604584e-01 -2.47042164e-01
-8.70881319e-01 -9.64751005e-01 -4.99960274e-01 -1.23462713e+00
3.09024360e-02 6.88336372e-01 1.48653015e-01 9.86478105... | [9.36251449584961, 3.44171404838562] |
11e98fa6-f83d-426f-8798-4eb04500de3e | husp-sp-faster-utility-mining-on-sequence | 2212.14255 | null | https://arxiv.org/abs/2212.14255v1 | https://arxiv.org/pdf/2212.14255v1.pdf | HUSP-SP: Faster Utility Mining on Sequence Data | High-utility sequential pattern mining (HUSPM) has emerged as an important topic due to its wide application and considerable popularity. However, due to the combinatorial explosion of the search space when the HUSPM problem encounters a low utility threshold or large-scale data, it may be time-consuming and memory-cos... | ['Philip S. Yu', 'Wensheng Gan', 'Zilin Du', 'Yuting Yang', 'Chunkai Zhang'] | 2022-12-29 | null | null | null | null | ['sequential-pattern-mining'] | ['natural-language-processing'] | [ 4.11952466e-01 -3.24540764e-01 -4.08149362e-01 -3.45874717e-03
-2.45360553e-01 -3.19074132e-02 -1.44111484e-01 2.27366641e-01
-4.10470217e-01 9.25754011e-01 -5.67185357e-02 -2.81022727e-01
-3.59643757e-01 -1.20439756e+00 -1.27421424e-01 -7.79536128e-01
-1.29359350e-01 2.94036210e-01 9.11070883e-01 9.75067690... | [8.299981117248535, 6.30908203125] |
b2c945aa-bd50-481c-9a9b-c62891e87f77 | rethinking-generalization-performance-of | 2110.11626 | null | https://arxiv.org/abs/2110.11626v1 | https://arxiv.org/pdf/2110.11626v1.pdf | Rethinking Generalization Performance of Surgical Phase Recognition with Expert-Generated Annotations | As the area of application of deep neural networks expands to areas requiring expertise, e.g., in medicine and law, more exquisite annotation processes for expert knowledge training are required. In particular, it is difficult to guarantee generalization performance in the clinical field in the case of expert knowledge... | ['Min-Kook Choi', 'Woo Jin Hyung', 'Sunghyun Park', 'Anwar H. Alfadhel', 'Ahmed A. Alwusaibie', 'Bokyung Park', 'Jiwon Lee', 'Seungbum Hong'] | 2021-10-22 | null | null | null | null | ['surgical-phase-recognition'] | ['computer-vision'] | [-2.80863456e-02 1.01175416e+00 -3.18215400e-01 -3.83529842e-01
-6.07490003e-01 -8.86323392e-01 -1.71267465e-01 3.51542711e-01
-6.96747899e-01 7.04303265e-01 1.86010256e-01 -8.36493552e-01
-3.20726484e-01 -4.95575398e-01 -6.31323576e-01 -5.46250701e-01
1.24664884e-02 6.77621901e-01 -1.88675076e-01 -9.94619876... | [14.938945770263672, -2.7776315212249756] |
7cdbffbc-3f0c-4398-85b6-8d3385d23879 | few-shot-class-incremental-learning | 2004.10956 | null | https://arxiv.org/abs/2004.10956v2 | https://arxiv.org/pdf/2004.10956v2.pdf | Few-Shot Class-Incremental Learning | The ability to incrementally learn new classes is crucial to the development of real-world artificial intelligence systems. In this paper, we focus on a challenging but practical few-shot class-incremental learning (FSCIL) problem. FSCIL requires CNN models to incrementally learn new classes from very few labelled samp... | ['Xiaoyu Tao', 'Xiaopeng Hong', 'Songlin Dong', 'Xing Wei', 'Yihong Gong', 'Xinyuan Chang'] | 2020-04-23 | few-shot-class-incremental-learning-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Tao_Few-Shot_Class-Incremental_Learning_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Tao_Few-Shot_Class-Incremental_Learning_CVPR_2020_paper.pdf | cvpr-2020-6 | ['few-shot-class-incremental-learning'] | ['methodology'] | [ 2.56907016e-01 1.14451274e-01 -1.67862087e-01 -3.44831854e-01
-3.49143967e-02 -4.09076303e-01 5.63738227e-01 4.02711052e-03
-4.90562558e-01 1.07810867e+00 -1.01645410e-01 1.80132866e-01
-3.26786131e-01 -1.04076123e+00 -1.01113939e+00 -8.15795898e-01
-3.16850282e-02 3.40688109e-01 7.83527672e-01 1.16618304... | [9.824529647827148, 3.3752622604370117] |
f2b74072-a88f-4dc8-869d-13bb9902faa8 | just-noticeable-defocus-blur-detection-and | null | null | http://openaccess.thecvf.com/content_cvpr_2015/html/Shi_Just_Noticeable_Defocus_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Shi_Just_Noticeable_Defocus_2015_CVPR_paper.pdf | Just Noticeable Defocus Blur Detection and Estimation | We tackle a fundamental problem to detect and estimate just noticeable blur (JNB) caused by defocus that spans a small number of pixels in images. This type of blur is common during photo taking. Although it is not strong, the slight edge blurriness contains informative clues related to depth. We found existing blur de... | ['Jiaya Jia', 'Li Xu', 'Jianping Shi'] | 2015-06-01 | null | null | null | cvpr-2015-6 | ['defocus-blur-detection'] | ['computer-vision'] | [ 8.75066817e-02 -5.93600333e-01 9.29759070e-02 -4.13618147e-01
-2.00985238e-01 -5.55245757e-01 3.80554527e-01 -4.26468760e-01
1.20485783e-01 1.10883713e+00 9.22732472e-01 1.81146830e-01
-2.33577177e-01 -2.00591609e-01 -6.07332528e-01 -7.33606994e-01
-2.83083916e-02 -5.73241711e-01 2.81578302e-01 1.21613853... | [11.598541259765625, -2.750579357147217] |
62c1de37-13d7-4371-a445-a8485f325770 | a-simple-joint-model-for-improved-contextual | 1904.02306 | null | https://arxiv.org/abs/1904.02306v4 | https://arxiv.org/pdf/1904.02306v4.pdf | A Simple Joint Model for Improved Contextual Neural Lemmatization | English verbs have multiple forms. For instance, talk may also appear as talks, talked or talking, depending on the context. The NLP task of lemmatization seeks to map these diverse forms back to a canonical one, known as the lemma. We present a simple joint neural model for lemmatization and morphological tagging that... | ['Shijie Wu', 'Ryan Cotterell', 'Chaitanya Malaviya'] | 2019-04-04 | a-simple-joint-model-for-improved-contextual-1 | https://aclanthology.org/N19-1155 | https://aclanthology.org/N19-1155.pdf | naacl-2019-6 | ['morphological-tagging'] | ['natural-language-processing'] | [-2.02733666e-01 2.05790550e-01 -4.27850336e-01 -6.00281596e-01
-1.09203720e+00 -9.04921830e-01 4.11391497e-01 2.47688159e-01
-7.19025075e-01 8.08809996e-01 6.45831108e-01 -7.21646965e-01
4.36501950e-01 -5.33621550e-01 -6.26963139e-01 -4.38647091e-01
9.24155414e-02 7.20321119e-01 -2.15097025e-01 2.38797367... | [10.452674865722656, 10.068629264831543] |
cc7bbf74-fb6a-4410-9501-f636074ef708 | a-new-persian-text-summarization-approach | null | null | https://jipm.irandoc.ac.ir/browse.php?a_id=2842&sid=1&slc_lang=en | https://jipm.irandoc.ac.ir/article-1-2842-en.pdf | A New Persian Text Summarization Approach Based on Natural Language Processing and Graph Similarity | Abstract: A significant amount of available information is stored in textual
databases which contain a large collection of documents from different
sources (such as news, articles, books, emails and web pages). The
increasing visibility and importance of this class of information motivates
us to work on having bett... | ['Azadeh Mohebi', 'Abbas Ahmadi', 'Tayyebeh Hosseinikhah'] | 2017-02-01 | null | null | null | iranian-journal-of-onformation-processing-and | ['graph-similarity', 'extractive-document-summarization'] | ['graphs', 'natural-language-processing'] | [ 3.01985502e-01 2.02574432e-01 -1.78716972e-01 -1.03502296e-01
-5.25290072e-01 -5.18531442e-01 5.45464694e-01 1.19115615e+00
-5.86804211e-01 1.12389684e+00 8.80436420e-01 -2.07355712e-02
-3.38564932e-01 -8.22681248e-01 9.07797366e-02 -3.71529728e-01
1.23092219e-01 4.18977946e-01 6.32321775e-01 -5.18207312... | [12.302277565002441, 9.53227424621582] |
1fb3311b-43c5-4efd-9b5b-10de519f8e64 | k-means-for-unsupervised-instance | null | null | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4251338 | https://papers.ssrn.com/sol3/Delivery.cfm/456a55bb-5b72-49b6-be69-b5f39b85c44c-MECA.pdf?abstractid=4251338&mirid=1 | K-means for unsupervised instance segmentation using a self-supervised transformer | Instance segmentation is a fundamental task in computer vision that assigns every pixel to an
appropriate class and localizes objects into bounding boxes. However, collecting pixel-level segmentation labels is more resource- and time-consuming than collecting classification and detection
labels. Herein, we present a ... | ['Lee HongChul', 'Lee MinYoung', 'Park JaeEon', 'Lim SeongTaek'] | 2022-10-04 | null | null | null | pattern-recognition-2022-10 | ['single-object-discovery', 'object-discovery'] | ['computer-vision', 'computer-vision'] | [ 7.70776033e-01 4.20428783e-01 -4.15144622e-01 -6.51470900e-01
-1.23008239e+00 -8.43949616e-01 4.48062867e-01 1.86611339e-01
-6.17791116e-01 4.52422917e-01 -6.54839754e-01 -1.45430677e-02
1.90912113e-01 -5.97757101e-01 -8.97227764e-01 -6.67709053e-01
2.63585746e-01 1.15338266e+00 7.39357650e-01 5.94822049... | [9.499966621398926, 0.6302962899208069] |
5ef6f792-f667-4a15-a6a4-c12cf063c936 | moving-towards-a-functional-approach-in-the | null | null | https://aclanthology.org/2022.signlang-1.4 | https://aclanthology.org/2022.signlang-1.4.pdf | Moving towards a Functional Approach in the Flemish Sign Language Dictionary Making Process | This presentation will outline the dictionary making process of the new online Flemish Sign Language dictionary launched in 2019. First some necessary background information is provided, consisting of a brief history of Flemish Sign Language (VGT) lexicography. Then three phases in the development of the renewed dictio... | ['Hannes De Durpel', 'Thijs Vandamme', 'Sam Verstraete', 'Margot Janssens', 'Caro Brosens'] | null | null | null | null | signlang-lrec-2022-6 | ['instance-search'] | ['computer-vision'] | [-9.07090902e-02 1.04252204e-01 -5.10296285e-01 -1.35612860e-01
-5.89507341e-01 -8.58689070e-01 4.70914423e-01 1.57328218e-01
-7.52024472e-01 4.67248738e-01 1.16724360e+00 -5.19773543e-01
-1.76585764e-01 -1.74876943e-01 1.31172851e-01 -1.96170732e-01
2.93097526e-01 4.19381678e-01 5.01056649e-02 -5.35804033... | [9.139941215515137, -6.44369649887085] |
4b01517d-9abd-4fb5-ab47-7a3897b4e237 | hmd-amp-protein-language-powered-hierarchical | 2111.06023 | null | https://arxiv.org/abs/2111.06023v1 | https://arxiv.org/pdf/2111.06023v1.pdf | HMD-AMP: Protein Language-Powered Hierarchical Multi-label Deep Forest for Annotating Antimicrobial Peptides | Identifying the targets of an antimicrobial peptide is a fundamental step in studying the innate immune response and combating antibiotic resistance, and more broadly, precision medicine and public health. There have been extensive studies on the statistical and computational approaches to identify (i) whether a peptid... | ['Yu Li', 'Licheng Zong', 'Xingyu Fan', 'Zhihang Dong', 'Qinze Yu'] | 2021-11-11 | null | null | null | null | ['protein-language-model'] | ['medical'] | [ 7.77234554e-01 -6.15620434e-01 -4.28312927e-01 -2.73419559e-01
-7.48579323e-01 -9.46637094e-01 3.41539443e-01 6.14594698e-01
-3.48060131e-01 1.19348633e+00 -1.50361657e-01 -6.45127892e-01
7.87458792e-02 -6.12987995e-01 -8.69102299e-01 -1.16644609e+00
-1.10217638e-01 7.13219523e-01 -6.83611110e-02 3.63676362... | [4.807767868041992, 5.59494686126709] |
aa536d33-fc77-4d00-b580-e27d05ddc1f3 | a-report-on-the-automatic-evaluation-of | null | null | https://aclanthology.org/W16-0506 | https://aclanthology.org/W16-0506.pdf | A Report on the Automatic Evaluation of Scientific Writing Shared Task | null | ['Vidas Daudaravicius', 'Rafael E. Banchs', 'Elena Volodina', 'Courtney Napoles'] | 2016-06-01 | null | null | null | ws-2016-6 | ['grammatical-error-detection'] | ['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... | [-7.242435932159424, 3.730689764022827] |
d6105bc5-e59f-45cc-9dee-e42e80d8d4b1 | instructabsa-instruction-learning-for-aspect | 2302.08624 | null | https://arxiv.org/abs/2302.08624v5 | https://arxiv.org/pdf/2302.08624v5.pdf | InstructABSA: Instruction Learning for Aspect Based Sentiment Analysis | In this paper, we present InstructABSA, Aspect Based Sentiment Analysis (ABSA) using the instruction learning paradigm for the ABSA subtasks: Aspect Term Extraction (ATE), Aspect Term Sentiment Classification (ATSC), and Joint Task modeling. Our method introduces positive, negative, and neutral examples to each trainin... | ['Chitta Baral', 'Swaroop Mishra', 'Siddharth Goyal', 'Saurabh Arjun Sawant', 'Himanshu Gupta', 'Kevin Scaria'] | 2023-02-16 | null | null | null | null | ['term-extraction', 'aspect-extraction', 'aspect-based-sentiment-analysis'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 9.56456177e-03 1.84172675e-01 -4.70619202e-01 -4.70805705e-01
-1.13973010e+00 -5.95873177e-01 8.68712723e-01 2.40058929e-01
-2.13542148e-01 6.23995900e-01 1.72889724e-01 -5.28209865e-01
1.87831864e-01 -5.93361676e-01 -7.76475847e-01 -5.68491459e-01
2.77330637e-01 5.77819407e-01 1.96288973e-01 -5.23294389... | [11.46821403503418, 6.685230731964111] |
74e895d3-9342-4491-aa72-0707f20925d4 | regularizing-towards-soft-equivariance-under | 2306.00356 | null | https://arxiv.org/abs/2306.00356v1 | https://arxiv.org/pdf/2306.00356v1.pdf | Regularizing Towards Soft Equivariance Under Mixed Symmetries | Datasets often have their intrinsic symmetries, and particular deep-learning models called equivariant or invariant models have been developed to exploit these symmetries. However, if some or all of these symmetries are only approximate, which frequently happens in practice, these models may be suboptimal due to the ar... | ['Juho Lee', 'Hongseok Yang', 'Hyungi Lee', 'Hyunsu Kim'] | 2023-06-01 | null | null | null | null | ['motion-forecasting'] | ['computer-vision'] | [-1.16163723e-01 2.64597535e-01 -4.50895429e-01 -1.83199465e-01
-4.39016908e-01 -7.24071980e-01 8.82357895e-01 -3.66028398e-01
3.00145030e-01 5.31097233e-01 6.84777260e-01 3.39261852e-02
-1.87402844e-01 -8.21038783e-01 -1.17327738e+00 -5.24781168e-01
7.18478784e-02 6.64317846e-01 3.02195907e-01 -3.31728816... | [8.985289573669434, 2.38551664352417] |
16cfedef-1dc7-49e7-b0c3-1ec2c7756bf3 | a-machine-transliteration-tool-between-uzbek | 2205.09578 | null | https://arxiv.org/abs/2205.09578v1 | https://arxiv.org/pdf/2205.09578v1.pdf | A machine transliteration tool between Uzbek alphabets | Machine transliteration, as defined in this paper, is a process of automatically transforming written script of words from a source alphabet into words of another target alphabet within the same language, while preserving their meaning, as well as pronunciation. The main goal of this paper is to present a machine trans... | ['Carlos Gómez-Rodríguez', 'Elmurod Kuriyozov', 'Ulugbek Salaev'] | 2022-05-19 | null | null | null | null | ['transliteration'] | ['natural-language-processing'] | [ 2.20664725e-01 3.37652825e-02 1.13358758e-01 -2.73804367e-01
-3.60873073e-01 -1.15956366e+00 1.05721772e+00 -2.52119023e-02
-5.13781190e-01 1.09635079e+00 1.67782471e-01 -1.03124154e+00
-3.94364111e-02 -7.29131162e-01 -3.58745039e-01 -3.08977932e-01
5.21615744e-01 8.16809416e-01 1.87653765e-01 -5.66776156... | [10.60546588897705, 10.427474975585938] |
690fb0a9-fde2-4e4b-bb22-ee870902fe89 | cluster-induced-mask-transformers-for | 2307.04525 | null | https://arxiv.org/abs/2307.04525v1 | https://arxiv.org/pdf/2307.04525v1.pdf | Cluster-Induced Mask Transformers for Effective Opportunistic Gastric Cancer Screening on Non-contrast CT Scans | Gastric cancer is the third leading cause of cancer-related mortality worldwide, but no guideline-recommended screening test exists. Existing methods can be invasive, expensive, and lack sensitivity to identify early-stage gastric cancer. In this study, we explore the feasibility of using a deep learning approach on no... | ['Ling Zhang', 'Zaiyi Liu', 'Li Zhang', 'Le Lu', 'Bin Dong', 'Jingren Zhou', 'Hexin Dong', 'Mingyan Qiu', 'Junli Wang', 'Jiawen Yao', 'Xin Chen', 'Yingda Xia', 'Mingze Yuan'] | 2023-07-10 | null | null | null | null | ['specificity'] | ['natural-language-processing'] | [-6.01117834e-02 2.49303635e-02 -3.14965427e-01 -6.18555993e-02
-1.24216366e+00 -3.41282636e-01 1.73857734e-01 5.01273692e-01
-5.41472495e-01 2.13764414e-01 -1.94013447e-01 -4.91590619e-01
3.39548290e-01 -9.90774691e-01 -4.85574275e-01 -9.42004502e-01
-3.49010736e-01 5.36362648e-01 4.54949796e-01 1.65702567... | [15.06147575378418, -2.670104503631592] |
04924c78-2d1c-4551-b095-66f28ad3f6c9 | federated-online-clustering-of-bandits | 2208.14865 | null | https://arxiv.org/abs/2208.14865v1 | https://arxiv.org/pdf/2208.14865v1.pdf | Federated Online Clustering of Bandits | Contextual multi-armed bandit (MAB) is an important sequential decision-making problem in recommendation systems. A line of works, called the clustering of bandits (CLUB), utilize the collaborative effect over users and dramatically improve the recommendation quality. Owing to the increasing application scale and publi... | ['John C. S. Lui', 'Shuai Li', 'Tong Yu', 'Haoru Zhao', 'Xutong Liu'] | 2022-08-31 | null | null | null | null | ['online-clustering'] | ['computer-vision'] | [-2.70561159e-01 -1.96116194e-01 -6.84895694e-01 -3.99647564e-01
-1.18474126e+00 -9.71315563e-01 9.83538851e-02 7.76498169e-02
-2.75551468e-01 8.91897738e-01 2.57850617e-01 -5.77053249e-01
-5.90979159e-01 -6.27083182e-01 -9.22577560e-01 -1.26391351e+00
1.11723915e-01 4.25495446e-01 -1.68275565e-01 2.97216177... | [4.600452899932861, 3.4438791275024414] |
dd2ce228-3d5b-4120-adb5-d32ec5bc1829 | clothes-invariant-feature-learning-by-causal | 2305.06145 | null | https://arxiv.org/abs/2305.06145v1 | https://arxiv.org/pdf/2305.06145v1.pdf | Clothes-Invariant Feature Learning by Causal Intervention for Clothes-Changing Person Re-identification | Clothes-invariant feature extraction is critical to the clothes-changing person re-identification (CC-ReID). It can provide discriminative identity features and eliminate the negative effects caused by the confounder--clothing changes. But we argue that there exists a strong spurious correlation between clothes and hum... | ['Nenghai Yu', 'Wanli Ouyang', 'Qi Chu', 'Yating Liu', 'Yuenan Hou', 'Bin Liu', 'Yan Lu', 'Xulin Li'] | 2023-05-10 | null | null | null | null | ['person-re-identification'] | ['computer-vision'] | [-3.74988653e-02 -3.86413991e-01 -1.17674388e-01 -5.19392908e-01
-2.93866664e-01 -2.84960955e-01 7.70073593e-01 -3.37807715e-01
-2.73497373e-01 6.71562135e-01 5.91455102e-01 3.87689501e-01
-2.24142179e-01 -6.19454145e-01 -8.05824161e-01 -9.05477107e-01
1.31991394e-02 -2.59574026e-01 -3.56569469e-01 -1.06506832... | [14.716497421264648, 0.9601019620895386] |
180ca778-fff4-4651-ad22-2589db6fd6cd | forget-me-not-learning-to-forget-in-text-to | 2303.17591 | null | https://arxiv.org/abs/2303.17591v1 | https://arxiv.org/pdf/2303.17591v1.pdf | Forget-Me-Not: Learning to Forget in Text-to-Image Diffusion Models | The unlearning problem of deep learning models, once primarily an academic concern, has become a prevalent issue in the industry. The significant advances in text-to-image generation techniques have prompted global discussions on privacy, copyright, and safety, as numerous unauthorized personal IDs, content, artistic c... | ['Humphrey Shi', 'Zhangyang Wang', 'Xingqian Xu', 'Kai Wang', 'Eric Zhang'] | 2023-03-30 | null | null | null | null | ['memorization'] | ['natural-language-processing'] | [ 1.58159301e-01 -1.00933336e-01 1.69686422e-01 -2.16051668e-01
-6.73644066e-01 -8.55892599e-01 5.99959850e-01 1.68587074e-01
-3.42829913e-01 6.81339204e-01 -9.22109038e-02 -4.29269582e-01
4.50843051e-02 -8.91487420e-01 -7.99156785e-01 -3.66929650e-01
2.32768565e-01 1.36621073e-01 6.42799139e-02 -3.52107324... | [11.493905067443848, -0.258380264043808] |
039fde06-f4fb-4745-8d24-47a79b2d2ff4 | look-across-elapse-disentangled | 1809.00338 | null | http://arxiv.org/abs/1809.00338v2 | http://arxiv.org/pdf/1809.00338v2.pdf | Look Across Elapse: Disentangled Representation Learning and Photorealistic Cross-Age Face Synthesis for Age-Invariant Face Recognition | Despite the remarkable progress in face recognition related technologies,
reliably recognizing faces across ages still remains a big challenge. The
appearance of a human face changes substantially over time, resulting in
significant intra-class variations. As opposed to current techniques for
age-invariant face recogni... | ['ShengMei Shen', 'Yan Xu', 'Lin Xiong', 'Junliang Xing', 'Jian Zhao', 'Yu Cheng', 'Sugiri Pranata', 'Jianshu Li', 'Hengzhu Liu', 'Fang Zhao', 'Yi Cheng', 'Yang Yang', 'Jiashi Feng', 'Shuicheng Yan', 'Haochong Lan'] | 2018-09-02 | null | null | null | null | ['age-invariant-face-recognition'] | ['computer-vision'] | [ 2.47569740e-01 -2.85344303e-01 1.80488229e-01 -8.64651442e-01
-5.73400080e-01 -5.25027633e-01 6.93420470e-01 -7.00900137e-01
-1.65576816e-01 5.54960787e-01 3.63258012e-02 -2.74480209e-02
1.15554419e-03 -6.76084697e-01 -6.87520087e-01 -9.16130722e-01
-2.50639394e-02 2.52895325e-01 -6.63042724e-01 -1.36294305... | [13.32457447052002, 0.6653309464454651] |
42054a3c-615a-41f6-a82d-3b269ac7551d | hybrid-relation-guided-set-matching-for-few | 2204.13423 | null | https://arxiv.org/abs/2204.13423v1 | https://arxiv.org/pdf/2204.13423v1.pdf | Hybrid Relation Guided Set Matching for Few-shot Action Recognition | Current few-shot action recognition methods reach impressive performance by learning discriminative features for each video via episodic training and designing various temporal alignment strategies. Nevertheless, they are limited in that (a) learning individual features without considering the entire task may lose the ... | ['Nong Sang', 'Rong Jin', 'Changxin Gao', 'Zhengrong Zuo', 'Mingqian Tang', 'Zhiwu Qing', 'Shiwei Zhang', 'Xiang Wang'] | 2022-04-28 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Wang_Hybrid_Relation_Guided_Set_Matching_for_Few-Shot_Action_Recognition_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Wang_Hybrid_Relation_Guided_Set_Matching_for_Few-Shot_Action_Recognition_CVPR_2022_paper.pdf | cvpr-2022-1 | ['few-shot-action-recognition', 'set-matching'] | ['computer-vision', 'computer-vision'] | [ 2.04398304e-01 -4.09317404e-01 -4.66502577e-01 -3.36057872e-01
-8.24565113e-01 -1.98944837e-01 6.11257195e-01 -1.71712965e-01
-3.09445620e-01 4.00114447e-01 4.10415053e-01 3.94125015e-01
-4.42534983e-01 -4.93271202e-01 -5.19077599e-01 -7.73391962e-01
-1.54304951e-01 1.65909499e-01 5.84601581e-01 -1.89003170... | [8.523598670959473, 0.803330659866333] |
202490b8-ebf7-4159-ade2-51d1bc6ce0bd | unsupervised-continual-learning-and-self-1 | null | null | https://openreview.net/forum?id=SJxakiC4u4 | https://openreview.net/pdf?id=SJxakiC4u4 | Unsupervised Continual Learning and Self-Taught Associative Memory Hierarchies | We first pose the Unsupervised Continual Learning (UCL) problem: learning salient representations from a non-stationary stream of unlabeled data in which the number of object classes varies with time. Given limited labeled data just before inference, those representations can also be associated with specific object typ... | ['Constantine Dovrolis', 'Zsolt Kira', 'Seth Baer', 'James Smith'] | 2019-03-24 | null | null | null | iclr-workshop-lld-2019 | ['online-clustering'] | ['computer-vision'] | [ 2.93097407e-01 2.19161451e-01 3.37765515e-02 -4.42594141e-01
-9.77645516e-02 -2.59403259e-01 4.22858417e-01 5.53496003e-01
-3.97383153e-01 7.59134531e-01 -8.90163034e-02 -2.16637403e-02
-2.29783222e-01 -6.20173097e-01 -1.12341511e+00 -4.94889051e-01
-4.40907359e-01 7.41232514e-01 6.46610200e-01 -5.71150668... | [9.824429512023926, 3.3538100719451904] |
dbdc2e61-4689-4526-acf4-8ac0d0954260 | on-the-n-gram-approximation-of-pre-trained | 2306.06892 | null | https://arxiv.org/abs/2306.06892v1 | https://arxiv.org/pdf/2306.06892v1.pdf | On the N-gram Approximation of Pre-trained Language Models | Large pre-trained language models (PLMs) have shown remarkable performance across various natural language understanding (NLU) tasks, particularly in low-resource settings. Nevertheless, their potential in Automatic Speech Recognition (ASR) remains largely unexplored. This study investigates the potential usage of PLMs... | ['Dietrich Klakow', 'Jesujoba Alabi', 'Aravind Krishnan'] | 2023-06-12 | null | null | null | null | ['automatic-speech-recognition'] | ['speech'] | [ 4.00615275e-01 3.55471730e-01 -3.50298166e-01 -4.64870542e-01
-1.48237145e+00 -3.26745689e-01 8.27001750e-01 1.20841272e-01
-6.12011194e-01 7.42416382e-01 6.22328877e-01 -9.32478845e-01
3.40641797e-01 -2.84356385e-01 -6.98633134e-01 -1.03091955e-01
4.24953014e-01 8.66439342e-01 1.70412660e-01 -4.89727519... | [14.37478256225586, 6.877290725708008] |
c6e4bc2a-4e1b-4080-80dd-be7175dd4791 | the-naughtyformer-a-transformer-understands | 2211.14369 | null | https://arxiv.org/abs/2211.14369v1 | https://arxiv.org/pdf/2211.14369v1.pdf | The Naughtyformer: A Transformer Understands Offensive Humor | Jokes are intentionally written to be funny, but not all jokes are created the same. Some jokes may be fit for a classroom of kindergarteners, but others are best reserved for a more mature audience. While recent work has shown impressive results on humor detection in text, here we instead investigate the more nuanced ... | ['Jason Wang', 'Steve Li', 'Alexander Cai', 'Leonard Tang'] | 2022-11-25 | null | null | null | null | ['humor-detection'] | ['natural-language-processing'] | [-3.73732150e-01 2.71779180e-01 -3.00652713e-01 1.15981713e-01
-1.61863565e-01 -5.28444827e-01 5.32511771e-01 1.13822654e-01
5.47272861e-02 5.22520244e-01 6.56649649e-01 -9.77792516e-02
6.08785544e-03 -5.74097633e-01 -2.62472630e-01 -4.74596471e-01
7.66304314e-01 1.22591473e-01 2.88694769e-01 -6.32286251... | [8.886015892028809, 11.054946899414062] |
3236121b-3fad-4f64-9fe5-2b30008a8897 | exploring-optimal-voting-in-native-language | null | null | https://aclanthology.org/W17-5041 | https://aclanthology.org/W17-5041.pdf | Exploring Optimal Voting in Native Language Identification | We describe the submissions entered by the National Research Council Canada in the NLI-2017 evaluation. We mainly explored the use of voting, and various ways to optimize the choice and number of voting systems. We also explored the use of features that rely on no linguistic preprocessing. Long ngrams of characters obt... | ["Serge L{\\'e}ger", 'Cyril Goutte'] | 2017-09-01 | null | null | null | ws-2017-9 | ['native-language-identification'] | ['natural-language-processing'] | [ 1.30249947e-01 1.62669569e-01 -3.34652513e-02 -5.46288848e-01
-1.30725169e+00 -9.22190070e-01 1.15012586e+00 2.31370673e-01
-8.50099504e-01 7.45917618e-01 4.35158074e-01 -5.16189158e-01
-4.18541431e-02 -4.32003915e-01 -1.20434023e-01 -5.43511033e-01
5.47250152e-01 5.95803618e-01 -3.42618264e-02 -3.04746240... | [10.467229843139648, 10.41737174987793] |
c6f2c95d-dea8-4cac-aaef-42df256a61bc | sinusoidal-flow-a-fast-invertible | 2110.13344 | null | https://arxiv.org/abs/2110.13344v1 | https://arxiv.org/pdf/2110.13344v1.pdf | Sinusoidal Flow: A Fast Invertible Autoregressive Flow | Normalising flows offer a flexible way of modelling continuous probability distributions. We consider expressiveness, fast inversion and exact Jacobian determinant as three desirable properties a normalising flow should possess. However, few flow models have been able to strike a good balance among all these properties... | ['Yumou Wei'] | 2021-10-26 | null | null | null | null | ['normalising-flows'] | ['methodology'] | [-9.22271460e-02 2.93798655e-01 7.57296979e-02 2.81485505e-02
-3.96341026e-01 -8.15932214e-01 1.01991630e+00 -6.52718008e-01
-3.67688164e-02 1.03182101e+00 3.27053100e-01 -5.42350173e-01
-4.58986282e-01 -6.94676340e-01 -6.09175742e-01 -6.48577452e-01
-3.41177464e-01 4.21752781e-01 -1.40873000e-01 -1.31389216... | [7.171891689300537, 3.8037006855010986] |
bc93e4e9-490e-426e-9b60-89a0e2727f97 | an-efficient-framework-for-few-shot-skeleton | 2207.09925 | null | https://arxiv.org/abs/2207.09925v1 | https://arxiv.org/pdf/2207.09925v1.pdf | An Efficient Framework for Few-shot Skeleton-based Temporal Action Segmentation | Temporal action segmentation (TAS) aims to classify and locate actions in the long untrimmed action sequence. With the success of deep learning, many deep models for action segmentation have emerged. However, few-shot TAS is still a challenging problem. This study proposes an efficient framework for the few-shot skelet... | ['Lin Yuan', 'Xiaotian Lin', 'Qiang Wang', 'Leiyang Xu'] | 2022-07-20 | null | null | null | null | ['action-segmentation'] | ['computer-vision'] | [ 6.33564413e-01 -1.90472230e-01 -4.95508254e-01 -3.34408998e-01
-9.82360840e-01 8.54295492e-02 3.06585729e-01 -4.22815084e-01
-4.26853836e-01 3.85969281e-01 4.90088195e-01 2.20290482e-01
2.13962212e-01 -5.92319787e-01 -4.95671839e-01 -7.26628125e-01
1.93173334e-01 7.46878088e-02 9.44529593e-01 -1.92954820... | [8.421177864074707, 0.5094887614250183] |
4bbe569e-1b12-4ec4-91ff-10e5d1466bd1 | thompson-sampling-for-improved-exploration-in | 2306.17693 | null | https://arxiv.org/abs/2306.17693v1 | https://arxiv.org/pdf/2306.17693v1.pdf | Thompson sampling for improved exploration in GFlowNets | Generative flow networks (GFlowNets) are amortized variational inference algorithms that treat sampling from a distribution over compositional objects as a sequential decision-making problem with a learnable action policy. Unlike other algorithms for hierarchical sampling that optimize a variational bound, GFlowNet alg... | ['Yoshua Bengio', 'Nikolay Malkin', 'Sarath Chandar', 'Cheng-Hao Liu', 'Maksym Korablyov', 'Moksh Jain', 'Kanika Madan', 'Jarrid Rector-Brooks'] | 2023-06-30 | null | null | null | null | ['thompson-sampling', 'active-learning', 'multi-armed-bandits', 'active-learning', 'decision-making'] | ['methodology', 'methodology', 'miscellaneous', 'natural-language-processing', 'reasoning'] | [ 1.27561586e-02 3.97556484e-01 -9.05931175e-01 -1.70534790e-01
-1.06047344e+00 -4.56485540e-01 9.30596292e-01 -3.12244982e-01
-5.52616835e-01 1.45945859e+00 1.71890706e-01 -4.96934444e-01
-5.51610291e-01 -9.78094280e-01 -8.78486335e-01 -1.00684679e+00
-4.66084927e-02 1.16408253e+00 2.74799943e-01 4.91047233... | [4.369206428527832, 2.527585506439209] |
a6bd91be-4fec-476b-9e44-a720c71f09c1 | john-ate-5-apples-john-ate-some-apples-self | 2206.08263 | null | https://arxiv.org/abs/2206.08263v1 | https://arxiv.org/pdf/2206.08263v1.pdf | 'John ate 5 apples' != 'John ate some apples': Self-Supervised Paraphrase Quality Detection for Algebraic Word Problems | This paper introduces the novel task of scoring paraphrases for Algebraic Word Problems (AWP) and presents a self-supervised method for doing so. In the current online pedagogical setting, paraphrasing these problems is helpful for academicians to generate multiple syntactically diverse questions for assessments. It al... | ['Vikram Goyal', 'Mukesh Mohania', 'Venktesh V', 'Rishabh Gupta'] | 2022-06-16 | null | null | null | null | ['paraphrase-generation', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing'] | [ 2.99446702e-01 1.44406080e-01 -3.40554357e-01 -3.57460797e-01
-1.31534457e+00 -9.74476576e-01 2.60903209e-01 5.90901136e-01
-1.38805076e-01 6.53576434e-01 2.10609838e-01 -7.69755840e-01
-7.47025758e-02 -8.80309820e-01 -8.63172293e-01 -2.42460549e-01
6.69809043e-01 4.13150668e-01 1.56832546e-01 -5.15999675... | [11.532612800598145, 9.201079368591309] |
c0e78fe1-1a5a-4d13-9979-132b42b1e50e | patchnet-short-range-template-matching-for | 2103.07371 | null | https://arxiv.org/abs/2103.07371v1 | https://arxiv.org/pdf/2103.07371v1.pdf | PatchNet -- Short-range Template Matching for Efficient Video Processing | Object recognition is a fundamental problem in many video processing tasks, accurately locating seen objects at low computation cost paves the way for on-device video recognition. We propose PatchNet, an efficient convolutional neural network to match objects in adjacent video frames. It learns the patchwise correlatio... | ['William J. Dally', 'Song Han', 'Sibo Zhu', 'Huizi Mao'] | 2021-03-10 | null | null | null | null | ['template-matching'] | ['computer-vision'] | [-2.58497715e-01 -5.02178609e-01 -2.18156680e-01 -1.63006693e-01
-6.04676545e-01 -5.70742548e-01 1.81159288e-01 -1.95033476e-01
-6.42062128e-01 5.19139707e-01 -3.50100398e-01 -4.32461590e-01
1.02348775e-01 -4.68142748e-01 -1.15761507e+00 -3.00591409e-01
-2.78751999e-01 -1.53439835e-01 5.64912200e-01 1.59650937... | [8.836750030517578, -0.15914317965507507] |
544110fb-68cb-4963-b16d-92b9953f08f4 | diverse-few-shot-text-classification-with | 1805.07513 | null | http://arxiv.org/abs/1805.07513v1 | http://arxiv.org/pdf/1805.07513v1.pdf | Diverse Few-Shot Text Classification with Multiple Metrics | We study few-shot learning in natural language domains. Compared to many
existing works that apply either metric-based or optimization-based
meta-learning to image domain with low inter-task variance, we consider a more
realistic setting, where tasks are diverse. However, it imposes tremendous
difficulties to existing ... | ['Jin-Feng Yi', 'Bo-Wen Zhou', 'Shiyu Chang', 'Xiaoxiao Guo', 'Yu Cheng', 'Saloni Potdar', 'Mo Yu', 'Haoyu Wang', 'Gerald Tesauro'] | 2018-05-19 | diverse-few-shot-text-classification-with-1 | https://aclanthology.org/N18-1109 | https://aclanthology.org/N18-1109.pdf | naacl-2018-6 | ['few-shot-text-classification'] | ['natural-language-processing'] | [ 3.51061702e-01 -5.33935010e-01 -4.17523414e-01 -6.06657863e-01
-1.00719440e+00 -7.18421936e-02 9.03679073e-01 6.85843155e-02
-7.96642661e-01 6.00802183e-01 3.35068017e-01 1.80363730e-01
-2.33009517e-01 -5.69228411e-01 -1.61138475e-01 -5.60699463e-01
2.18403444e-01 6.00842655e-01 5.46778917e-01 -4.81977820... | [10.02521800994873, 3.078815221786499] |
65c6e5bb-b23e-468c-bf84-b3c2ad64a435 | coloured-noise-time-series-as-appropriate | 2006.16204 | null | https://arxiv.org/abs/2006.16204v1 | https://arxiv.org/pdf/2006.16204v1.pdf | Coloured noise time series as appropriate models for environmental variation in artificial evolutionary systems | Ecological, environmental and geophysical time series consistently exhibit the characteristics of coloured (1/f^\b{eta}) noise. Here we briefly survey the literature on coloured noise, population persistence and related evolutionary dynamics, before introducing coloured noise as an appropriate model for environmental v... | ['James M. Borg', 'Matt Grove', 'Fiona Polack'] | 2020-06-29 | null | null | null | null | ['artificial-life'] | ['miscellaneous'] | [ 3.76617312e-01 -5.71204007e-01 7.37003982e-01 4.66522910e-02
3.44525158e-01 -5.60212851e-01 8.55413914e-01 -1.16622843e-01
-7.34771669e-01 7.79924631e-01 2.27488205e-01 -6.01773620e-01
-4.32468951e-01 -7.47322321e-01 -2.90905982e-01 -1.23005712e+00
-5.01822054e-01 -9.20279548e-02 2.65177935e-01 -7.56482482... | [5.592878341674805, 4.124729156494141] |
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