paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
8f1efdd5-ce49-434a-9ea6-0c334ada9958 | query-based-keyphrase-extraction-from-long | 2205.05391 | null | https://arxiv.org/abs/2205.05391v1 | https://arxiv.org/pdf/2205.05391v1.pdf | Query-Based Keyphrase Extraction from Long Documents | Transformer-based architectures in natural language processing force input size limits that can be problematic when long documents need to be processed. This paper overcomes this issue for keyphrase extraction by chunking the long documents while keeping a global context as a query defining the topic for which relevant... | ['Pavel Smrz', 'Martin Docekal'] | 2022-05-11 | null | null | null | null | ['keyphrase-extraction'] | ['natural-language-processing'] | [ 1.72689572e-01 7.89359678e-03 -2.86095262e-01 -2.23785132e-01
-7.98911989e-01 -7.05531657e-01 1.12824488e+00 9.51282084e-01
-1.22462440e+00 9.18480754e-01 6.35082603e-01 -3.72544885e-01
-2.47137681e-01 -9.42851722e-01 -6.41305268e-01 -3.62165004e-01
-5.65873226e-04 5.85840285e-01 6.16485775e-01 -1.76444620... | [12.25340461730957, 8.866875648498535] |
45eb6c21-7b38-434a-9e28-11e149a122a7 | clipface-text-guided-editing-of-textured-3d | 2212.01406 | null | https://arxiv.org/abs/2212.01406v2 | https://arxiv.org/pdf/2212.01406v2.pdf | ClipFace: Text-guided Editing of Textured 3D Morphable Models | We propose ClipFace, a novel self-supervised approach for text-guided editing of textured 3D morphable model of faces. Specifically, we employ user-friendly language prompts to enable control of the expressions as well as appearance of 3D faces. We leverage the geometric expressiveness of 3D morphable models, which inh... | ['Matthias Nießner', 'Angela Dai', 'Justus Thies', 'Shivangi Aneja'] | 2022-12-02 | null | null | null | null | ['texture-synthesis'] | ['computer-vision'] | [ 4.72884864e-01 5.16699970e-01 2.71808326e-01 -5.68453789e-01
-5.96645474e-01 -8.05934429e-01 6.53554380e-01 -8.23806107e-01
3.10723156e-01 3.25041592e-01 3.97933573e-02 1.32799178e-01
3.30683947e-01 -9.34033930e-01 -1.08041990e+00 -5.36191821e-01
-2.05902588e-02 4.80025053e-01 -5.01897037e-01 -3.89899731... | [12.686161041259766, -0.3821890354156494] |
7963b40f-4112-49fe-bd21-12552bbed7b0 | trusted-multi-view-classification-with | 2204.11423 | null | https://arxiv.org/abs/2204.11423v3 | https://arxiv.org/pdf/2204.11423v3.pdf | Trusted Multi-View Classification with Dynamic Evidential Fusion | Existing multi-view classification algorithms focus on promoting accuracy by exploiting different views, typically integrating them into common representations for follow-up tasks. Although effective, it is also crucial to ensure the reliability of both the multi-view integration and the final decision, especially for ... | ['Joey Tianyi Zhou', 'Huazhu Fu', 'Changqing Zhang', 'Zongbo Han'] | 2022-04-25 | null | null | null | null | ['multi-view-learning'] | ['computer-vision'] | [-3.45772505e-01 3.18143293e-02 -2.33696461e-01 -5.53686321e-01
-1.28367424e+00 -6.82625234e-01 6.53253555e-01 3.75122607e-01
5.38242273e-02 8.30699027e-01 4.37919572e-02 2.90054381e-01
-5.35634160e-01 -7.68685222e-01 -5.70099413e-01 -1.15389705e+00
2.05217466e-01 4.65837926e-01 -7.30764046e-02 2.33178467... | [8.511534690856934, 4.498816967010498] |
1b7cd0d8-56ff-4533-882b-4899c237c8e0 | end-to-end-optimized-arrhythmia-detection | 2111.11789 | null | https://arxiv.org/abs/2111.11789v1 | https://arxiv.org/pdf/2111.11789v1.pdf | End-to-End Optimized Arrhythmia Detection Pipeline using Machine Learning for Ultra-Edge Devices | Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia worldwide, with 2% of the population affected. It is associated with an increased risk of strokes, heart failure and other heart-related complications. Monitoring at-risk individuals and detecting asymptomatic AF could result in considerable public healt... | ['Vineeth Vijayaraghavan', 'Shanthakumar S', 'Vishal Nagarajan', 'Sachin Krishan T', 'Sideshwar J B'] | 2021-11-23 | null | null | null | null | ['arrhythmia-detection', 'atrial-fibrillation-detection'] | ['medical', 'medical'] | [ 2.35152066e-01 -9.22986045e-02 -1.66498810e-01 -1.21558547e-01
-6.50689423e-01 -4.52730566e-01 -1.85796440e-01 6.12684667e-01
-3.03956330e-01 8.40241373e-01 -2.49985754e-01 -8.28634441e-01
-1.85894087e-01 -9.75666285e-01 -5.50359450e-02 -3.50069195e-01
-4.29027230e-01 1.85542211e-01 -1.67059585e-01 4.09793377... | [14.14015007019043, 3.2403175830841064] |
f21f8619-b0ef-4d74-9b70-7669dcae7116 | indoor-smartphone-slam-with-learned-echoic | 2210.08493 | null | https://arxiv.org/abs/2210.08493v1 | https://arxiv.org/pdf/2210.08493v1.pdf | Indoor Smartphone SLAM with Learned Echoic Location Features | Indoor self-localization is a highly demanded system function for smartphones. The current solutions based on inertial, radio frequency, and geomagnetic sensing may have degraded performance when their limiting factors take effect. In this paper, we present a new indoor simultaneous localization and mapping (SLAM) syst... | ['Guosheng Lin', 'Rui Tan', 'Zhenyu Yan', 'Qun Song', 'Wenjie Luo'] | 2022-10-16 | null | null | null | null | ['simultaneous-localization-and-mapping'] | ['computer-vision'] | [-3.14682163e-02 -4.37632143e-01 1.04845785e-01 -7.74956718e-02
-1.13062251e+00 -2.63654619e-01 4.26027961e-02 2.58236498e-01
-4.75248754e-01 8.76880229e-01 4.28749248e-02 -4.15843219e-01
-1.90364406e-01 -8.45232427e-01 -8.88381660e-01 -6.27469897e-01
-5.77664196e-01 -1.72583926e-02 2.11039767e-01 -9.64263082... | [6.3529887199401855, 0.9583912491798401] |
c9ca2bd9-c0ea-459c-ba20-65aeebede5a5 | unraveling-cold-start-enigmas-in-predictive | 2305.08120 | null | https://arxiv.org/abs/2305.08120v1 | https://arxiv.org/pdf/2305.08120v1.pdf | Unraveling Cold Start Enigmas in Predictive Analytics for OTT Media: Synergistic Meta-Insights and Multimodal Ensemble Mastery | The cold start problem is a common challenge in various domains, including media use cases such as predicting viewership for newly launched shows on Over-The-Top (OTT) platforms. In this study, we propose a generic approach to tackle cold start problems by leveraging metadata and employing multi-model ensemble techniqu... | ['A. Patra', 'K. Ganguly'] | 2023-05-14 | null | null | null | null | ['feature-engineering'] | ['methodology'] | [-4.32223260e-01 -5.23518562e-01 -2.39259794e-01 -4.83781576e-01
-8.22334766e-01 -7.01536775e-01 6.81210756e-01 2.57287204e-01
-2.25620151e-01 5.30898988e-01 2.04123661e-01 -9.65768099e-02
-8.88553634e-02 -6.54369891e-01 -4.29100506e-02 -5.37946522e-01
9.09128189e-02 2.29833618e-01 4.75864977e-01 -5.52742481... | [10.098807334899902, 5.762589931488037] |
ac516375-654b-4aa3-a741-1f07c1907e82 | unsupervised-paraphrasability-prediction-for | null | null | https://aclanthology.org/2022.naacl-main.237 | https://aclanthology.org/2022.naacl-main.237.pdf | Unsupervised Paraphrasability Prediction for Compound Nominalizations | Commonly found in academic and formal texts, a nominalization uses a deverbal noun to describe an event associated with its corresponding verb. Nominalizations can be difficult to interpret because of ambiguous semantic relations between the deverbal noun and its arguments. Automatic generation of clausal paraphrases f... | ['Carol Carol Webster', 'Ho Hung Lim', 'John Sie Yuen Lee'] | null | null | null | null | naacl-2022-7 | ['paraphrase-generation', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing'] | [ 4.78331745e-01 2.79061049e-01 -4.54320431e-01 -6.75525069e-01
-7.48677552e-01 -1.09566379e+00 8.38036001e-01 8.83889794e-01
-3.95423770e-01 1.05481768e+00 8.31215799e-01 -7.05419779e-01
-2.01366797e-01 -8.38163733e-01 -7.69081652e-01 -3.63350749e-01
6.05708063e-01 7.69635379e-01 1.96260419e-02 -3.69525880... | [10.664488792419434, 9.162837982177734] |
0ffa05e1-2c11-4de8-ba81-2e9c09db5420 | edge-adaptive-l2-regularization-image | 1811.08487 | null | http://arxiv.org/abs/1811.08487v1 | http://arxiv.org/pdf/1811.08487v1.pdf | Edge-adaptive l2 regularization image reconstruction from non-uniform Fourier data | Total variation regularization based on the l1 norm is ubiquitous in image
reconstruction. However, the resulting reconstructions are not always as sparse
in the edge domain as desired. Iteratively reweighted methods provide some
improvement in accuracy, but at the cost of extended runtime. In this paper we
examine the... | [] | 2018-11-20 | null | null | null | null | ['l2-regularization'] | ['methodology'] | [ 3.85241807e-01 -1.11881875e-01 8.42126533e-02 -1.93951920e-01
-9.18105423e-01 -1.16800003e-01 1.40779719e-01 -2.08970621e-01
-4.86164600e-01 6.54025137e-01 2.06951901e-01 -8.26216936e-02
-3.73725414e-01 -4.61008191e-01 -5.70239127e-01 -7.07127631e-01
-1.34649903e-01 6.51678741e-02 1.84217691e-01 1.91281457... | [11.704784393310547, -2.432959794998169] |
6bf1d825-b6f1-46ca-b2c5-8f9233a469d0 | detect-distill-and-update-learned-db-systems | 2210.05508 | null | https://arxiv.org/abs/2210.05508v2 | https://arxiv.org/pdf/2210.05508v2.pdf | Detect, Distill and Update: Learned DB Systems Facing Out of Distribution Data | Machine Learning (ML) is changing DBs as many DB components are being replaced by ML models. One open problem in this setting is how to update such ML models in the presence of data updates. We start this investigation focusing on data insertions (dominating updates in analytical DBs). We study how to update neural net... | ['Peter Triantafillou', 'Meghdad Kurmanji'] | 2022-10-11 | null | null | null | null | ['synthetic-data-generation', 'synthetic-data-generation'] | ['medical', 'miscellaneous'] | [-6.96788877e-02 1.59071520e-01 -2.11553842e-01 -3.35997641e-01
-7.67546654e-01 -4.53589827e-01 3.30573231e-01 3.59479338e-01
-4.62131798e-01 1.15984845e+00 -2.81702757e-01 -3.38962078e-01
-2.38168478e-01 -1.16755366e+00 -1.53633666e+00 -4.70715463e-01
-1.71103835e-01 1.22685742e+00 5.16447425e-01 5.29220887... | [9.42155933380127, 3.441514492034912] |
4118d9b0-90d6-4897-bda3-226be2498071 | hico-det-sg-and-v-coco-sg-new-data-splits-to | 2305.09948 | null | https://arxiv.org/abs/2305.09948v4 | https://arxiv.org/pdf/2305.09948v4.pdf | HICO-DET-SG and V-COCO-SG: New Data Splits for Evaluating the Systematic Generalization Performance of Human-Object Interaction Detection Models | Human-Object Interaction (HOI) detection is a task to localize humans and objects in an image and predict the interactions in human-object pairs. In real-world scenarios, HOI detection models are required systematic generalization, i.e., generalization to novel combinations of objects and interactions, because the trai... | ['Hisanao Akima', 'Tomotake Sasaki', 'Moyuru Yamada', 'Kentaro Takemoto'] | 2023-05-17 | null | null | null | null | ['human-object-interaction-detection', 'systematic-generalization'] | ['computer-vision', 'reasoning'] | [-1.53832883e-02 -1.01800948e-01 -4.77649644e-02 -3.24488401e-01
-1.16343997e-01 -4.09057885e-01 4.65854853e-01 -1.21529557e-01
-9.73267481e-02 3.54803115e-01 -1.14272386e-01 7.31904656e-02
-1.23221569e-01 -4.39019412e-01 -5.65863371e-01 -5.00815749e-01
-2.94441015e-01 6.06782377e-01 6.50815070e-01 3.96596175... | [9.595017433166504, 1.3763809204101562] |
6ba797ec-7654-424d-bb01-e5d9ee4b5335 | transformer-with-peak-suppression-and | 2107.06538 | null | https://arxiv.org/abs/2107.06538v2 | https://arxiv.org/pdf/2107.06538v2.pdf | Transformer with Peak Suppression and Knowledge Guidance for Fine-grained Image Recognition | Fine-grained image recognition is challenging because discriminative clues are usually fragmented, whether from a single image or multiple images. Despite their significant improvements, most existing methods still focus on the most discriminative parts from a single image, ignoring informative details in other regions... | ['Xiaoguang Han', 'Lili Wang', 'Xinda Liu'] | 2021-07-14 | null | null | null | null | ['fine-grained-image-recognition'] | ['computer-vision'] | [ 3.39065850e-01 -2.44547635e-01 -2.08199352e-01 -2.60463208e-01
-6.77494586e-01 -4.05836672e-01 5.05444288e-01 3.27022746e-02
-4.80938792e-01 5.67304254e-01 2.98275828e-01 2.28964269e-01
-5.16153276e-01 -7.71353781e-01 -4.97758359e-01 -1.17588925e+00
4.31104124e-01 7.77705461e-02 4.12921369e-01 2.10393056... | [9.749601364135742, 1.9437679052352905] |
081b26f1-6233-494a-81bf-d24f72af03a8 | benchmarking-transformers-based-models-on | 2207.09152 | null | https://arxiv.org/abs/2207.09152v1 | https://arxiv.org/pdf/2207.09152v1.pdf | Benchmarking Transformers-based models on French Spoken Language Understanding tasks | In the last five years, the rise of the self-attentional Transformer-based architectures led to state-of-the-art performances over many natural language tasks. Although these approaches are increasingly popular, they require large amounts of data and computational resources. There is still a substantial need for benchm... | ['Sophie Rosset', 'Christophe Servan', 'Sahar Ghannay', 'Oralie Cattan'] | 2022-07-19 | null | null | null | null | ['spoken-language-understanding', 'spoken-language-understanding'] | ['natural-language-processing', 'speech'] | [ 2.41999760e-01 -1.03981115e-01 -5.96879199e-02 -3.87806773e-01
-9.70519125e-01 -5.35089731e-01 9.02080238e-01 3.63790482e-01
-9.39036429e-01 7.96031952e-01 8.78960341e-02 -2.30526701e-01
-1.74944133e-01 -4.53509629e-01 -6.98165715e-01 -4.60120618e-01
1.42009556e-01 9.93163764e-01 2.26483226e-01 -4.41167653... | [13.836960792541504, 6.869466304779053] |
7f435983-4da4-4784-aa14-65d5669ee8ae | scaling-instruction-finetuned-language-models | 2210.11416 | null | https://arxiv.org/abs/2210.11416v5 | https://arxiv.org/pdf/2210.11416v5.pdf | Scaling Instruction-Finetuned Language Models | Finetuning language models on a collection of datasets phrased as instructions has been shown to improve model performance and generalization to unseen tasks. In this paper we explore instruction finetuning with a particular focus on (1) scaling the number of tasks, (2) scaling the model size, and (3) finetuning on cha... | ['Adams Yu', 'Dasha Valter', 'Kevin Robinson', 'Marie Pellat', 'Alex Castro-Ros', 'Yunxuan Li', 'Jason Wei', 'Quoc V. Le', 'Denny Zhou', 'Adam Roberts', 'Jacob Devlin', 'Jeff Dean', 'Ed H. Chi', 'Slav Petrov', 'Hongkun Yu', 'Andrew Dai', 'Yanping Huang', 'Vincent Zhao', 'Gaurav Mishra', 'Sharan Narang', 'Aakanksha Chow... | 2022-10-20 | null | null | null | null | ['multi-task-language-understanding', 'cross-lingual-question-answering', 'paraphrase-identification'] | ['methodology', 'natural-language-processing', 'natural-language-processing'] | [-1.50050357e-01 -5.18448293e-01 -5.20569444e-01 -3.18545759e-01
-1.25858450e+00 -6.87224805e-01 5.41180670e-01 -1.09870378e-02
-6.82425559e-01 5.98262787e-01 2.30638832e-01 -9.10902321e-01
1.07210360e-01 -4.13477033e-01 -8.54307532e-01 -3.83950651e-01
1.16810717e-01 5.41366041e-01 3.64795923e-01 -6.32377803... | [10.641461372375488, 8.341826438903809] |
1b8143c6-df17-47de-ab10-4c507a038ca9 | learning-deep-sketch-abstraction | 1804.04804 | null | http://arxiv.org/abs/1804.04804v1 | http://arxiv.org/pdf/1804.04804v1.pdf | Learning Deep Sketch Abstraction | Human free-hand sketches have been studied in various contexts including
sketch recognition, synthesis and fine-grained sketch-based image retrieval
(FG-SBIR). A fundamental challenge for sketch analysis is to deal with
drastically different human drawing styles, particularly in terms of
abstraction level. In this work... | ['Timothy M. Hospedales', 'Yi-Zhe Song', 'Yongxin Yang', 'Umar Riaz Muhammad', 'Tao Xiang'] | 2018-04-13 | learning-deep-sketch-abstraction-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Muhammad_Learning_Deep_Sketch_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Muhammad_Learning_Deep_Sketch_CVPR_2018_paper.pdf | cvpr-2018-6 | ['sketch-based-image-retrieval', 'sketch-recognition'] | ['computer-vision', 'computer-vision'] | [ 4.39989448e-01 1.73497032e-02 -1.57535553e-01 -1.41168073e-01
-2.86688745e-01 -7.51291037e-01 9.57875073e-01 -8.01025778e-02
1.61165565e-01 2.58785814e-01 2.79993534e-01 -2.30094492e-01
1.18532656e-02 -8.77451837e-01 -6.82647169e-01 -3.81667823e-01
3.80316556e-01 5.30690491e-01 1.93853885e-01 -1.04509324... | [11.71695613861084, 0.40357282757759094] |
549171ec-bb1a-497a-bb94-dba5ad5d9686 | degpr-deep-guided-posterior-regularization | 2304.00741 | null | https://arxiv.org/abs/2304.00741v1 | https://arxiv.org/pdf/2304.00741v1.pdf | DeGPR: Deep Guided Posterior Regularization for Multi-Class Cell Detection and Counting | Multi-class cell detection and counting is an essential task for many pathological diagnoses. Manual counting is tedious and often leads to inter-observer variations among pathologists. While there exist multiple, general-purpose, deep learning-based object detection and counting methods, they may not readily transfer ... | ['Mausam', 'Prathosh AP', 'Lalita Mehra', 'Govind Makharia', 'Prasenjit Das', 'Chirag Mohapatra', 'Aayush Kumar Tyagi'] | 2023-04-03 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Tyagi_DeGPR_Deep_Guided_Posterior_Regularization_for_Multi-Class_Cell_Detection_and_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Tyagi_DeGPR_Deep_Guided_Posterior_Regularization_for_Multi-Class_Cell_Detection_and_CVPR_2023_paper.pdf | cvpr-2023-1 | ['medical-object-detection', 'cell-detection'] | ['computer-vision', 'computer-vision'] | [ 2.40880758e-01 -3.63465190e-01 -1.97151765e-01 -7.28739798e-02
-9.71348941e-01 -5.77331543e-01 4.12878841e-01 6.95337534e-01
-7.70068288e-01 6.74566031e-01 -2.17892770e-02 -1.43518403e-01
3.55839789e-01 -4.51080054e-01 -4.30116564e-01 -9.21014369e-01
-1.54599935e-01 8.31355035e-01 3.46162856e-01 2.58898914... | [14.991652488708496, -3.0605175495147705] |
f49c76d7-160e-428b-9542-6d4372600314 | weakly-supervised-3d-medical-image | 2302.02125 | null | https://arxiv.org/abs/2302.02125v1 | https://arxiv.org/pdf/2302.02125v1.pdf | Weakly-Supervised 3D Medical Image Segmentation using Geometric Prior and Contrastive Similarity | Medical image segmentation is almost the most important pre-processing procedure in computer-aided diagnosis but is also a very challenging task due to the complex shapes of segments and various artifacts caused by medical imaging, (i.e., low-contrast tissues, and non-homogenous textures). In this paper, we propose a s... | ['Jing Liao', 'Yan Xu', 'Qihua Dong', 'Hao Du'] | 2023-02-04 | null | null | null | null | ['weakly-supervised-segmentation'] | ['computer-vision'] | [ 2.44709641e-01 1.57019690e-01 -1.58706367e-01 -3.20707262e-01
-6.18813694e-01 -9.42393169e-02 2.69663811e-01 2.30913237e-01
-4.20590043e-01 4.42391574e-01 6.07964136e-02 -1.39688000e-01
4.91734548e-03 -6.54612124e-01 -4.19964582e-01 -1.16311836e+00
8.60395581e-02 2.71962970e-01 5.76006174e-01 -1.47591352... | [14.626012802124023, -2.235063076019287] |
1084b85b-df19-4f93-ab59-1afaef19e505 | anabranch-network-for-camouflaged-object-1 | 2105.09451 | null | https://arxiv.org/abs/2105.09451v1 | https://arxiv.org/pdf/2105.09451v1.pdf | Anabranch Network for Camouflaged Object Segmentation | Camouflaged objects attempt to conceal their texture into the background and discriminating them from the background is hard even for human beings. The main objective of this paper is to explore the camouflaged object segmentation problem, namely, segmenting the camouflaged object(s) for a given image. This problem has... | ['Akihiro Sugimoto', 'Minh-Triet Tran', 'Zhongliang Nie', 'Tam V. Nguyen', 'Trung-Nghia Le'] | 2021-05-20 | anabranch-network-for-camouflaged-object | https://www.researchgate.net/publication/332806868_Anabranch_network_for_camouflaged_object_segmentation | https://www.researchgate.net/publication/332806868_Anabranch_network_for_camouflaged_object_segmentation | computer-vision-and-image-understanding-2019 | ['camouflaged-object-segmentation'] | ['computer-vision'] | [ 6.47309899e-01 9.96991470e-02 -2.10650757e-01 -8.76538232e-02
-3.29948127e-01 -6.99525356e-01 4.71577704e-01 -7.69068450e-02
-4.21550274e-01 7.36587822e-01 -2.28088319e-01 -4.23596025e-01
2.75217086e-01 -8.18501890e-01 -6.96442068e-01 -7.72064209e-01
1.12715632e-01 1.65747344e-01 6.00423634e-01 -2.19663400... | [9.625077247619629, -0.12727481126785278] |
d32ac7f1-8932-4359-8c20-3cdcd5db0c23 | predicting-retrosynthetic-reaction-using-self | 1907.01356 | null | https://arxiv.org/abs/1907.01356v2 | https://arxiv.org/pdf/1907.01356v2.pdf | Predicting Retrosynthetic Reaction using Self-Corrected Transformer Neural Networks | Synthesis planning is the process of recursively decomposing target molecules into available precursors. Computer-aided retrosynthesis can potentially assist chemists in designing synthetic routes, but at present it is cumbersome and provides results of dissatisfactory quality. In this study, we develop a template-free... | ['Yuedong Yang', 'Jiahua Rao', 'Jun Xu', 'Zhongyue Zhang', 'Shuangjia Zheng'] | 2019-07-02 | null | null | null | null | ['retrosynthesis'] | ['medical'] | [ 7.82962918e-01 2.00672850e-01 -3.80972773e-01 -5.76580837e-02
-7.37662852e-01 -9.95078504e-01 8.14231157e-01 3.22057694e-01
-3.07239443e-01 1.09087837e+00 7.57941231e-02 -7.92111039e-01
5.26845336e-01 -7.88606882e-01 -9.30985272e-01 -7.08045244e-01
2.71498501e-01 4.24959779e-01 -7.10119978e-02 -3.49860907... | [4.501057147979736, 6.100210666656494] |
fc378ae8-148d-4cdd-95db-846e69545a3c | chunk-aware-alignment-and-lexical-constraint | 2207.11401 | null | https://arxiv.org/abs/2207.11401v2 | https://arxiv.org/pdf/2207.11401v2.pdf | Chunk-aware Alignment and Lexical Constraint for Visual Entailment with Natural Language Explanations | Visual Entailment with natural language explanations aims to infer the relationship between a text-image pair and generate a sentence to explain the decision-making process. Previous methods rely mainly on a pre-trained vision-language model to perform the relation inference and a language model to generate the corresp... | ['Min Zhang', 'Yuxing Ding', 'Lin Ma', 'Baotian Hu', 'Yunxin Li', 'Qian Yang'] | 2022-07-23 | null | null | null | null | ['visual-entailment'] | ['reasoning'] | [ 1.40073538e-01 5.55850208e-01 -1.88166529e-01 -5.42129993e-01
-2.97564209e-01 -4.05127853e-02 7.72545159e-01 -8.50579739e-02
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3.27560008e-01 -8.06475341e-01 -9.28487659e-01 -3.33387375e-01
7.24182665e-01 3.01600099e-01 2.71072686e-01 -2.57696770... | [10.819284439086914, 1.703214406967163] |
2cdd6204-4a93-468b-977c-78cdc4d0f5e2 | generative-view-correlation-adaptation-for | null | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2130_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123590307.pdf | Generative View-Correlation Adaptation for Semi-Supervised Multi-View Learning | Multi-view learning (MVL) explores the data extracted from multiple resources. It assumes that the complementary information between different views could be revealed to further improve the learning performance. There are two challenges. First, it is difficult to effectively combine the different view data together whi... | ['Yunyu Liu', 'Lichen Wang', 'Yun Fu', 'Yue Bai', 'Can Qin', 'Zhengming Ding'] | null | null | null | null | eccv-2020-8 | ['multi-view-learning'] | ['computer-vision'] | [ 2.92737484e-01 -2.53481511e-02 -2.22725064e-01 -3.59507143e-01
-9.39238846e-01 -6.09231949e-01 5.51052451e-01 -3.41577381e-01
2.87137590e-02 5.54427028e-01 3.52210104e-01 1.50927082e-01
1.99544683e-01 -5.16632438e-01 -5.10478199e-01 -8.87227178e-01
5.21224678e-01 1.86629500e-02 2.34836675e-02 -1.14599757... | [8.493098258972168, 4.541285991668701] |
a5f9868b-6708-4a22-951d-d7163d069975 | learning-graph-structure-with-a-finite-state | 2007.04929 | null | https://arxiv.org/abs/2007.04929v2 | https://arxiv.org/pdf/2007.04929v2.pdf | Learning Graph Structure With A Finite-State Automaton Layer | Graph-based neural network models are producing strong results in a number of domains, in part because graphs provide flexibility to encode domain knowledge in the form of relational structure (edges) between nodes in the graph. In practice, edges are used both to represent intrinsic structure (e.g., abstract syntax tr... | ['Hugo Larochelle', 'Daniel Tarlow', 'Daniel D. Johnson'] | 2020-07-09 | null | http://proceedings.neurips.cc/paper/2020/hash/1fdc0ee9d95c71d73df82ac8f0721459-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/1fdc0ee9d95c71d73df82ac8f0721459-Paper.pdf | neurips-2020-12 | ['variable-misuse'] | ['computer-code'] | [ 3.35905522e-01 8.43018234e-01 -5.39603889e-01 -3.75631124e-01
-3.79996359e-01 -8.13112020e-01 5.59966803e-01 5.68054259e-01
3.99771146e-03 3.60556930e-01 2.03363180e-01 -1.07437766e+00
2.17728410e-03 -1.43428338e+00 -1.29573834e+00 -8.06752220e-02
-5.06884754e-01 3.59291613e-01 4.40269262e-01 -4.17584151... | [8.81637191772461, 7.350799560546875] |
d8559872-3eb7-4ed1-9172-9342bddf0187 | learning-to-scale-temperature-in-masked-self | 2302.06130 | null | https://arxiv.org/abs/2302.06130v1 | https://arxiv.org/pdf/2302.06130v1.pdf | Learning to Scale Temperature in Masked Self-Attention for Image Inpainting | Recent advances in deep generative adversarial networks (GAN) and self-attention mechanism have led to significant improvements in the challenging task of inpainting large missing regions in an image. These methods integrate self-attention mechanism in neural networks to utilize surrounding neural elements based on the... | ['Yi Gong', 'Yuan Zeng', 'Xiang Zhou'] | 2023-02-13 | null | null | null | null | ['image-inpainting', 'patch-matching'] | ['computer-vision', 'computer-vision'] | [ 1.69156268e-01 -2.17779100e-01 6.28851578e-02 -4.08897787e-01
-6.77854478e-01 -2.06140056e-01 4.01324570e-01 -5.98864615e-01
-1.40778139e-01 7.87576854e-01 2.95814186e-01 1.40606001e-01
2.89913714e-01 -1.13393438e+00 -1.30029023e+00 -7.85744429e-01
2.39911705e-01 4.84505929e-02 1.59331143e-01 -4.57116753... | [11.44791316986084, -1.021410584449768] |
f0331bbf-8f3f-403f-bd89-ef7ad0e175cf | a-self-supervised-joint-training-framework | null | null | https://aclanthology.org/2022.findings-naacl.79 | https://aclanthology.org/2022.findings-naacl.79.pdf | A Self-supervised Joint Training Framework for Document Reranking | Pretrained language models such as BERT have been successfully applied to a wide range of natural language processing tasks and also achieved impressive performance in document reranking tasks. Recent works indicate that further pretraining the language models on the task-specific datasets before fine-tuning helps impr... | ['Hai Liu', 'Fu Lee Wang', 'Sijie Cheng', 'Tianyong Hao', 'Xiaozhi Zhu'] | null | null | null | null | findings-naacl-2022-7 | ['passage-ranking'] | ['natural-language-processing'] | [ 5.61070383e-01 -3.58049273e-02 -4.92508411e-01 -8.28934133e-01
-1.24063432e+00 -4.49788570e-01 8.40785921e-01 4.33250874e-01
-8.66773963e-01 5.05463302e-01 7.03747571e-01 -2.61163086e-01
-1.07991926e-01 -4.67814118e-01 -7.05327392e-01 -1.13999575e-01
9.97959748e-02 8.00637722e-01 6.42326057e-01 -5.68492949... | [11.455636024475098, 7.677432537078857] |
3ccf1969-3169-40ed-89d2-8c90c479a10a | recurrent-instance-segmentation-using | 1911.02103 | null | https://arxiv.org/abs/1911.02103v1 | https://arxiv.org/pdf/1911.02103v1.pdf | Recurrent Instance Segmentation using Sequences of Referring Expressions | The goal of this work is to segment the objects in an image that are referred to by a sequence of linguistic descriptions (referring expressions). We propose a deep neural network with recurrent layers that output a sequence of binary masks, one for each referring expression provided by the user. The recurrent layers i... | ['Xavier Giro-i-Nieto', 'Ionut-Teodor Sorodoc', 'Alba Herrera-Palacio', 'Carina Silberer', 'Gemma Boleda', 'Carles Ventura'] | 2019-11-05 | null | null | null | null | ['referring-expression-segmentation'] | ['computer-vision'] | [ 4.89504695e-01 4.98543531e-01 -2.05374137e-01 -7.19930708e-01
-6.37976885e-01 -5.99295318e-01 5.46920657e-01 -3.06525618e-01
-3.35922599e-01 1.72672153e-01 6.88504130e-02 -1.24585584e-01
3.87335062e-01 -6.02675080e-01 -1.11973548e+00 -6.09905720e-01
3.81853431e-01 3.82274956e-01 -1.07361861e-01 -2.16497004... | [10.35303783416748, 1.271130919456482] |
2256d6f2-e352-49bd-9fa8-f6089d2869e7 | ai-techniques-for-cone-beam-computed | 2306.03025 | null | https://arxiv.org/abs/2306.03025v2 | https://arxiv.org/pdf/2306.03025v2.pdf | AI Techniques for Cone Beam Computed Tomography in Dentistry: Trends and Practices | Cone-beam computed tomography (CBCT) is a popular imaging modality in dentistry for diagnosing and planning treatment for a variety of oral diseases with the ability to produce detailed, three-dimensional images of the teeth, jawbones, and surrounding structures. CBCT imaging has emerged as an essential diagnostic tool... | ['Suraiya Jabin', 'Saba Sarwar'] | 2023-06-05 | null | null | null | null | ['super-resolution'] | ['computer-vision'] | [ 1.79960296e-01 5.23673117e-01 -4.68932092e-01 -3.69022310e-01
-6.65994883e-01 1.96251124e-01 2.11982094e-02 4.01286095e-01
-3.68004173e-01 1.59035504e-01 3.10015917e-01 -2.02069864e-01
-9.02508423e-02 -8.46993923e-01 1.27508849e-01 -1.00100243e+00
8.52294415e-02 1.16938174e+00 2.84982294e-01 2.89389919... | [13.786505699157715, -2.2265753746032715] |
4001ac46-fd76-4c0c-acc6-8fc1b713326f | convergence-of-communications-control-and | 2307.02663 | null | https://arxiv.org/abs/2307.02663v1 | https://arxiv.org/pdf/2307.02663v1.pdf | Convergence of Communications, Control, and Machine Learning for Secure and Autonomous Vehicle Navigation | Connected and autonomous vehicles (CAVs) can reduce human errors in traffic accidents, increase road efficiency, and execute various tasks ranging from delivery to smart city surveillance. Reaping these benefits requires CAVs to autonomously navigate to target destinations. To this end, each CAV's navigation controller... | ['Choong Seon Hong', 'Walid Saad', 'Omid Semiari', 'Aidin Ferdowsi', 'Tengchan Zeng'] | 2023-07-05 | null | null | null | null | ['autonomous-vehicles', 'autonomous-navigation', 'intrusion-detection', 'navigate', 'decision-making'] | ['computer-vision', 'computer-vision', 'miscellaneous', 'reasoning', 'reasoning'] | [-7.81086460e-02 1.52840674e-01 -3.92714769e-01 1.57525286e-01
-3.24443758e-01 -5.96137881e-01 4.57092673e-01 -1.06292777e-01
-3.79463285e-01 6.01210177e-01 -4.15073305e-01 -9.68475819e-01
-4.92932409e-01 -9.14209843e-01 -3.61088544e-01 -7.41310358e-01
-2.73492128e-01 -6.56065568e-02 4.87256497e-01 -6.72427833... | [5.586440563201904, 1.6057730913162231] |
be72dd3e-9b77-4ead-9722-2962240574ed | ecological-sampling-of-gaze-shifts | null | null | https://ieeexplore.ieee.org/abstract/document/6502674?casa_token=1byinAjZ5pcAAAAA:rTMkAo5EL0GZ0i_cn00bG3r4fKPrmSkMII18iE6DPq9UZ9xN9HMWOid8cMjfrh8p1O_bB14 | https://ieeexplore.ieee.org/abstract/document/6502674?casa_token=1byinAjZ5pcAAAAA:rTMkAo5EL0GZ0i_cn00bG3r4fKPrmSkMII18iE6DPq9UZ9xN9HMWOid8cMjfrh8p1O_bB14 | Ecological Sampling of Gaze Shifts | Visual attention guides our gaze to relevant parts of the viewed scene, yet the moment-to-moment relocation of gaze can be different among observers even though the same locations are taken into account. Surprisingly, the variability of eye movements has been so far overlooked by the great majority of computational mod... | ['Mario Ferraro', 'Giuseppe Boccignone'] | 2013-04-16 | null | null | null | ieee-transactions-on-cybernetics-2013-4 | ['gaze-estimation', 'eye-tracking'] | ['computer-vision', 'computer-vision'] | [ 2.25421950e-01 -2.94834301e-02 3.59192759e-01 1.17029458e-01
4.06417251e-01 -6.19924009e-01 4.94751304e-01 -6.90112785e-02
-6.62293494e-01 5.68560064e-01 -5.13865352e-02 -1.71461061e-01
-4.79958504e-01 -2.95454681e-01 -6.71969056e-01 -1.12425053e+00
-9.42109972e-02 -1.36539638e-01 3.50520164e-01 -2.57629484... | [10.062028884887695, 1.6374067068099976] |
d09dc1db-bc30-4894-9fc6-6a60a008fbfa | sci-a-spectrum-concentrated-implicit-neural | 2209.15180 | null | https://arxiv.org/abs/2209.15180v5 | https://arxiv.org/pdf/2209.15180v5.pdf | SCI: A Spectrum Concentrated Implicit Neural Compression for Biomedical Data | Massive collection and explosive growth of biomedical data, demands effective compression for efficient storage, transmission and sharing. Readily available visual data compression techniques have been studied extensively but tailored for natural images/videos, and thus show limited performance on biomedical data which... | ['Qionghai Dai', 'Jinli Suo', 'Jinyuan Qu', 'Qianni Cao', 'Yuxiao Cheng', 'Tingxiong Xiao', 'Runzhao Yang'] | 2022-09-30 | null | null | null | null | ['data-compression'] | ['time-series'] | [ 4.20270115e-01 -1.85538799e-01 -4.64622885e-01 -1.68250248e-01
-4.44539666e-01 5.32955751e-02 -2.90738009e-02 1.88065007e-01
-2.67386049e-01 7.45734155e-01 3.09548110e-01 -1.79222509e-01
-4.90486145e-01 -5.27846634e-01 -5.34260929e-01 -8.84194016e-01
-3.97936970e-01 3.71448189e-01 -1.99592769e-01 -3.24138738... | [11.316238403320312, -1.598046898841858] |
540b3758-7be2-4f30-b65c-ef183965699c | hindiwsd-a-package-for-word-sense | null | null | https://aclanthology.org/2022.wildre-1.4 | https://aclanthology.org/2022.wildre-1.4.pdf | HindiWSD: A package for word sense disambiguation in Hinglish & Hindi | A lot of commendable work has been done, especially in high resource languages such as English, Spanish, French, etc. However, work done for Indic languages such as Hindi, Tamil, Telugu, etc is relatively less due to difficulty in finding relevant datasets, and the complexity of these languages. With the advent of Indo... | ['Chethan Sharma', 'Praatibh Surana', 'Mirza Yusuf'] | null | null | null | null | wildre-lrec-2022-6 | ['word-sense-disambiguation', 'word-similarity', 'transliteration', 'cross-lingual-information-retrieval'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-1.72957197e-01 -1.95300817e-01 3.09297830e-01 -2.28804931e-01
-8.15973163e-01 -9.78842497e-01 4.33658123e-01 5.38825512e-01
-8.10465634e-01 1.11403561e+00 2.77894109e-01 -6.42845869e-01
-2.49193415e-01 -6.29240513e-01 -1.63932562e-01 -3.77423823e-01
2.61082053e-01 5.59901178e-01 5.49965620e-01 -8.06851566... | [10.449542045593262, 9.630961418151855] |
2842920e-89c6-4f05-befa-f95e7b5d619c | generating-video-description-using-sequence | null | null | https://aclanthology.org/C16-1005 | https://aclanthology.org/C16-1005.pdf | Generating Video Description using Sequence-to-sequence Model with Temporal Attention | Automatic video description generation has recently been getting attention after rapid advancement in image caption generation. Automatically generating description for a video is more challenging than for an image due to its temporal dynamics of frames. Most of the work relied on Recurrent Neural Network (RNN) and rec... | ['Sang Phan', 'Raphael Shu', 'Yusuke Miyao', 'Yo Ehara', 'Noriki Nishida', 'Naoaki Okazaki', 'Natsuda Laokulrat', 'Hideki Nakayama'] | 2016-12-01 | generating-video-description-using-sequence-1 | https://aclanthology.org/C16-1005 | https://aclanthology.org/C16-1005.pdf | coling-2016-12 | ['video-description'] | ['computer-vision'] | [ 5.02198696e-01 -8.80686566e-02 -2.89604384e-02 -2.50681132e-01
-6.74230993e-01 -2.86982715e-01 8.25547636e-01 -3.12087744e-01
-2.69210517e-01 9.22131181e-01 6.98439658e-01 1.02904342e-01
3.77027422e-01 -2.33760729e-01 -6.28655434e-01 -5.02736807e-01
8.54991972e-02 1.61559150e-01 1.89381883e-01 -1.37683272... | [10.580971717834473, 0.611694872379303] |
63adfb87-cacd-4826-a9cf-7fec7fd904a4 | bayesian-optimisation-assisted-neural-network | 2203.04032 | null | https://arxiv.org/abs/2203.04032v1 | https://arxiv.org/pdf/2203.04032v1.pdf | Bayesian Optimisation-Assisted Neural Network Training Technique for Radio Localisation | Radio signal-based (indoor) localisation technique is important for IoT applications such as smart factory and warehouse. Through machine learning, especially neural networks methods, more accurate mapping from signal features to target positions can be achieved. However, different radio protocols, such as WiFi, Blueto... | ['Ziming Zhu', 'Peizheng Li', 'Xingchi Liu'] | 2022-03-08 | null | null | null | null | ['bayesian-optimisation'] | ['methodology'] | [ 4.99368966e-01 -3.51528823e-01 3.15991528e-02 -5.41329443e-01
-3.36897135e-01 -2.79811621e-01 5.65233052e-01 9.58261117e-02
-5.45124590e-01 8.06357861e-01 -2.58032307e-02 -3.49669278e-01
-1.05806208e+00 -7.90369570e-01 -3.44490439e-01 -1.09486914e+00
-1.90186575e-01 5.60831964e-01 -9.79161169e-03 3.16658393... | [6.425114631652832, 1.0043600797653198] |
973cd699-3474-4bed-9fb0-f3ae36cd4d2a | unsupervised-multi-hop-question-answering-by | 2010.12623 | null | https://arxiv.org/abs/2010.12623v2 | https://arxiv.org/pdf/2010.12623v2.pdf | Unsupervised Multi-hop Question Answering by Question Generation | Obtaining training data for multi-hop question answering (QA) is time-consuming and resource-intensive. We explore the possibility to train a well-performed multi-hop QA model without referencing any human-labeled multi-hop question-answer pairs, i.e., unsupervised multi-hop QA. We propose MQA-QG, an unsupervised frame... | ['William Yang Wang', 'Min-Yen Kan', 'Wenhan Xiong', 'Wenhu Chen', 'Liangming Pan'] | 2020-10-23 | null | https://aclanthology.org/2021.naacl-main.469 | https://aclanthology.org/2021.naacl-main.469.pdf | naacl-2021-4 | ['multi-hop-question-answering'] | ['knowledge-base'] | [-4.62984703e-02 6.54221475e-01 3.96364421e-01 -6.69084072e-01
-2.21789312e+00 -9.96420026e-01 1.19811974e-01 2.71084696e-01
-4.32795852e-01 8.46077442e-01 5.44495694e-02 -5.11560619e-01
-1.14420475e-02 -9.53804970e-01 -7.17239916e-01 -3.19802642e-01
6.61435902e-01 1.22978699e+00 6.38371527e-01 -6.09966159... | [11.296396255493164, 8.14024829864502] |
31c693e9-6833-4825-9a95-0bae091ae7a6 | dating-ancient-texts-an-approach-for-noisy | null | null | https://aclanthology.org/2020.lt4hala-1.3 | https://aclanthology.org/2020.lt4hala-1.3.pdf | Dating Ancient texts: an Approach for Noisy French Documents | Automatic dating of ancient documents is a very important area of research for digital humanities applications. Many documents available via digital libraries do not have any dating or dating that is uncertain. Document dating is not only useful by itself but it also helps to choose the appropriate NLP tools (lemmatize... | ['Ga{\\"e}l Lejeune', 'Ana{\\"e}lle Baledent', 'Nicolas Hiebel'] | 2020-05-01 | null | null | null | lrec-2020-5 | ['document-dating'] | ['natural-language-processing'] | [-1.35733277e-01 -4.62102383e-01 -2.12855220e-01 -4.01857406e-01
-9.51284170e-01 -9.88342285e-01 1.15203011e+00 4.61429060e-01
-1.00942659e+00 1.07064402e+00 2.97634304e-01 -5.31568229e-01
-1.05019957e-01 -8.94202530e-01 -4.20655638e-01 -3.68138254e-01
-2.12832093e-02 9.70703900e-01 3.57092321e-01 -2.82887667... | [10.216482162475586, 10.263041496276855] |
e16d1fd4-4517-4065-995c-d436c5bf4f34 | large-scale-cloze-test-dataset-created-by | 1711.03225 | null | http://arxiv.org/abs/1711.03225v3 | http://arxiv.org/pdf/1711.03225v3.pdf | Large-scale Cloze Test Dataset Created by Teachers | Cloze tests are widely adopted in language exams to evaluate students'
language proficiency. In this paper, we propose the first large-scale
human-created cloze test dataset CLOTH, containing questions used in
middle-school and high-school language exams. With missing blanks carefully
created by teachers and candidate ... | ['Qizhe Xie', 'Zihang Dai', 'Guokun Lai', 'Eduard Hovy'] | 2017-11-09 | large-scale-cloze-test-dataset-created-by-1 | https://aclanthology.org/D18-1257 | https://aclanthology.org/D18-1257.pdf | emnlp-2018-10 | ['cloze-test'] | ['natural-language-processing'] | [-3.09868246e-01 6.49667680e-02 -1.57194152e-01 -7.16916099e-02
-1.14493060e+00 -9.60518539e-01 3.44424874e-01 5.62495947e-01
-4.30632412e-01 5.80604911e-01 4.32000041e-01 -1.23173237e+00
-3.27745497e-01 -7.75833070e-01 -6.82735980e-01 2.00489581e-01
4.12977517e-01 7.57203773e-02 3.22160631e-01 -5.23152292... | [10.202585220336914, 7.638521671295166] |
b0eb6589-dce2-4482-ba6e-90b3e9595395 | cem-commonsense-aware-empathetic-response | 2109.05739 | null | https://arxiv.org/abs/2109.05739v2 | https://arxiv.org/pdf/2109.05739v2.pdf | CEM: Commonsense-aware Empathetic Response Generation | A key trait of daily conversations between individuals is the ability to express empathy towards others, and exploring ways to implement empathy is a crucial step towards human-like dialogue systems. Previous approaches on this topic mainly focus on detecting and utilizing the user's emotion for generating empathetic r... | ['Minlie Huang', 'Chujie Zheng', 'Sahand Sabour'] | 2021-09-13 | null | null | null | null | ['empathetic-response-generation'] | ['natural-language-processing'] | [-2.25544170e-01 4.72806811e-01 -3.35432822e-03 -5.79488814e-01
-2.42313921e-01 -4.13700134e-01 6.92990661e-01 1.37024149e-01
-3.59186202e-01 8.63132477e-01 8.66513431e-01 3.13565046e-01
3.58548015e-01 -6.96095109e-01 5.73698401e-01 -2.77899891e-01
6.87552750e-01 4.61359084e-01 -4.87855315e-01 -8.72469246... | [13.153701782226562, 7.626633644104004] |
8e1ebf8e-ee6b-4daf-95da-8cf480f0e368 | msanii-high-fidelity-music-synthesis-on-a | 2301.06468 | null | https://arxiv.org/abs/2301.06468v1 | https://arxiv.org/pdf/2301.06468v1.pdf | Msanii: High Fidelity Music Synthesis on a Shoestring Budget | In this paper, we present Msanii, a novel diffusion-based model for synthesizing long-context, high-fidelity music efficiently. Our model combines the expressiveness of mel spectrograms, the generative capabilities of diffusion models, and the vocoding capabilities of neural vocoders. We demonstrate the effectiveness o... | ['Kinyugo Maina'] | 2023-01-16 | null | null | null | null | ['audio-inpainting', 'music-generation', 'music-generation'] | ['audio', 'audio', 'music'] | [-1.42150834e-01 -3.06065649e-01 3.88652682e-02 4.10898894e-01
-8.16823125e-01 -6.80744708e-01 4.64497000e-01 -3.67872119e-01
8.36634915e-03 5.90541482e-01 6.40740454e-01 -2.78225243e-01
-1.47368833e-01 -6.35953724e-01 -5.33584118e-01 -2.59991944e-01
-3.21787477e-01 4.96739000e-02 4.66027856e-02 -2.90510118... | [15.572113990783691, 5.7970356941223145] |
44e33e07-deb9-43bd-a345-35cf73869346 | characterizing-the-influence-of-features-on | 1808.09718 | null | http://arxiv.org/abs/1808.09718v1 | http://arxiv.org/pdf/1808.09718v1.pdf | Characterizing the Influence of Features on Reading Difficulty Estimation for Non-native Readers | In recent years, the number of people studying English as a second language
(ESL) has surpassed the number of native speakers. Recent work have
demonstrated the success of providing personalized content based on reading
difficulty, such as information retrieval and summarization. However, almost
all prior studies of re... | ['Yeali S. Sun', 'Yi-Ting Huang', 'Meng Chang Chen'] | 2018-08-29 | null | null | null | null | ['novel-concepts'] | ['reasoning'] | [-3.84678207e-02 2.21872821e-01 -2.84846663e-01 -2.10282534e-01
-8.57265413e-01 -5.05572855e-01 4.84301984e-01 9.85808611e-01
-9.04901385e-01 3.21165413e-01 7.99350202e-01 -6.30813956e-01
-2.00582117e-01 -5.62258601e-01 -2.13303864e-01 2.22765580e-02
4.28795367e-01 9.71281305e-02 5.13683379e-01 -4.01113123... | [10.816519737243652, 10.30258560180664] |
0e6aab73-92c3-455e-8a45-a7af7ff366b3 | a-refined-deep-learning-architecture-for | 2007.07922 | null | https://arxiv.org/abs/2007.07922v1 | https://arxiv.org/pdf/2007.07922v1.pdf | A Refined Deep Learning Architecture for Diabetic Foot Ulcers Detection | Diabetic Foot Ulcers (DFU) that affect the lower extremities are a major complication of diabetes. Each year, more than 1 million diabetic patients undergo amputation due to failure to recognize DFU and get the proper treatment from clinicians. There is an urgent need to use a CAD system for the detection of DFU. In th... | ['Saeed Hassanpour', 'Manu Goyal'] | 2020-07-15 | null | null | null | null | ['diabetic-foot-ulcer-detection'] | ['medical'] | [-6.18636198e-02 -1.95330307e-01 -3.77215594e-01 -2.22997352e-01
-8.89155090e-01 -1.77596346e-01 1.20873503e-01 -1.14035290e-02
-3.62712353e-01 1.17463982e+00 1.74818367e-01 -4.66146469e-01
1.44903079e-01 -1.09844613e+00 -6.93769038e-01 -3.06264848e-01
-1.50244489e-01 3.49400878e-01 2.16655090e-01 -8.87832493... | [15.754172325134277, -3.8429386615753174] |
02df2b28-d714-4317-9828-dc04462d3245 | cluster-and-aggregate-face-recognition-with | 2210.10864 | null | https://arxiv.org/abs/2210.10864v3 | https://arxiv.org/pdf/2210.10864v3.pdf | Cluster and Aggregate: Face Recognition with Large Probe Set | Feature fusion plays a crucial role in unconstrained face recognition where inputs (probes) comprise of a set of $N$ low quality images whose individual qualities vary. Advances in attention and recurrent modules have led to feature fusion that can model the relationship among the images in the input set. However, atte... | ['Xiaoming Liu', 'Anil Jain', 'Feng Liu', 'Minchul Kim'] | 2022-10-19 | null | null | null | null | ['video-recognition'] | ['computer-vision'] | [ 1.53415009e-01 -6.10942304e-01 -8.09735805e-02 -7.46479511e-01
-6.48010790e-01 -2.16431454e-01 4.34129626e-01 -2.05059022e-01
-3.50703508e-01 3.79592121e-01 -7.98052773e-02 4.93032672e-02
-4.80849952e-01 -6.02206707e-01 -6.71136200e-01 -8.64630580e-01
-3.53097916e-01 -9.93112624e-02 -4.19113338e-02 -6.77058697... | [13.171126365661621, 0.6967378258705139] |
00a2a77e-944f-46b9-9288-9a638f2f1aa7 | textoir-an-integrated-and-visualized-platform-1 | 2110.15063 | null | https://arxiv.org/abs/2110.15063v1 | https://arxiv.org/pdf/2110.15063v1.pdf | TEXTOIR: An Integrated and Visualized Platform for Text Open Intent Recognition | TEXTOIR is the first integrated and visualized platform for text open intent recognition. It is composed of two main modules: open intent detection and open intent discovery. Each module integrates most of the state-of-the-art algorithms and benchmark intent datasets. It also contains an overall framework connecting th... | ['Kai Gao', 'Kang Zhao', 'Panpan Zhang', 'Hua Xu', 'Xiaoteng Li', 'Hanlei Zhang'] | 2021-09-13 | textoir-an-integrated-and-visualized-platform | https://aclanthology.org/2021.acl-demo.20 | https://aclanthology.org/2021.acl-demo.20.pdf | acl-2021-5 | ['intent-recognition', 'open-intent-detection', 'open-intent-discovery', 'intent-discovery'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-5.26747942e-01 -4.48806643e-01 -2.66651183e-01 -2.34779224e-01
-4.31543946e-01 -6.72577083e-01 5.22633672e-01 -1.04095429e-01
-9.31049064e-02 1.47332042e-01 6.67360544e-01 -2.41147906e-01
-4.95041125e-02 -5.77195227e-01 3.15423310e-02 -3.72183442e-01
-2.32155189e-01 4.63345706e-01 -1.25398412e-01 -3.31355184... | [12.425951957702637, 7.572813034057617] |
d96a8ff5-21e9-4246-affb-6d69e6668c80 | should-we-hard-code-the-recurrence-concept-or | 2005.09297 | null | https://arxiv.org/abs/2005.09297v1 | https://arxiv.org/pdf/2005.09297v1.pdf | Should we hard-code the recurrence concept or learn it instead ? Exploring the Transformer architecture for Audio-Visual Speech Recognition | The audio-visual speech fusion strategy AV Align has shown significant performance improvements in audio-visual speech recognition (AVSR) on the challenging LRS2 dataset. Performance improvements range between 7% and 30% depending on the noise level when leveraging the visual modality of speech in addition to the audit... | ['George Sterpu', 'Naomi Harte', 'Christian Saam'] | 2020-05-19 | null | null | null | null | ['audio-visual-speech-recognition'] | ['speech'] | [ 3.86411756e-01 1.37651160e-01 -3.47235128e-02 -1.21061541e-01
-1.33321202e+00 -4.40069288e-01 9.75052297e-01 -7.02981502e-02
-3.84182245e-01 2.99402267e-01 4.76171672e-01 -5.08925855e-01
2.25008294e-01 -1.34255097e-04 -6.16543293e-01 -9.91635740e-01
2.14714050e-01 2.80537993e-01 3.43266651e-02 -4.80522998... | [14.362855911254883, 5.174940586090088] |
3bb907a4-6034-4790-971b-43dc38c04f1a | self-inspection-method-of-unmanned-aerial | 2303.09013 | null | https://arxiv.org/abs/2303.09013v1 | https://arxiv.org/pdf/2303.09013v1.pdf | Self-Inspection Method of Unmanned Aerial Vehicles in Power Plants Using Deep Q-Network Reinforcement Learning | For the purpose of inspecting power plants, autonomous robots can be built using reinforcement learning techniques. The method replicates the environment and employs a simple reinforcement learning (RL) algorithm. This strategy might be applied in several sectors, including the electricity generation sector. A pre-trai... | ['Haoran Guan'] | 2023-03-16 | null | null | null | null | ['q-learning'] | ['methodology'] | [-1.87472165e-01 3.50293726e-01 -1.32624224e-01 1.72180772e-01
2.18014747e-01 -7.79392898e-01 3.79382074e-01 9.79162902e-02
-2.73010761e-01 9.68536377e-01 -6.04439497e-01 -6.84579492e-01
-5.13030589e-01 -1.13942170e+00 -5.70538461e-01 -6.90562010e-01
-3.07497114e-01 3.11914563e-01 4.25426550e-02 -6.70132220... | [4.628355026245117, 1.8902912139892578] |
1f6832ed-50c6-4ed5-92ce-c9a33e044dd2 | ternary-twitter-sentiment-classification-with | null | null | https://aclanthology.org/w18-6215 | https://aclanthology.org/w18-6215.pdf | Ternary Twitter Sentiment Classification with Distant Supervision and Sentiment-Specific Word Embeddings | null | ['Björn Gambäck', 'Frederik Gørvell de Lichtenberg', 'Mats Byrkjeland'] | 2018-10-01 | null | https://aclanthology.org/W18-6215 | https://aclanthology.org/W18-6215.pdf | emnlp-2018-10 | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [-2.44508207e-01 3.89024585e-01 -2.65282035e-01 -2.15905145e-01
-8.60921741e-02 -7.76765764e-01 4.48510379e-01 -7.23253429e-01
-5.48377395e-01 1.31954515e+00 3.66348401e-02 -9.49533224e-01
-2.40340635e-01 -1.05564880e+00 -8.44053447e-01 -8.75781775e-01
-7.42435038e-01 6.86515033e-01 1.44298598e-01 -6.52004302... | [-1.5392014980316162, 15.869206428527832] |
63e37d62-53ef-46f1-9211-0c6a660a4233 | representation-justification-and-explanation | 1812.05362 | null | https://arxiv.org/abs/1812.05362v2 | https://arxiv.org/pdf/1812.05362v2.pdf | Representation, Justification and Explanation in a Value Driven Agent: An Argumentation-Based Approach | Ethical and explainable artificial intelligence is an interdisciplinary research area involving computer science, philosophy, logic, the social sciences, etc. For an ethical autonomous system, the ability to justify and explain its decision making is a crucial aspect of transparency and trustworthiness. This paper take... | ['Michael Anderson', 'Beishui Liao', 'Susan Leigh Anderson'] | 2018-12-13 | null | null | null | null | ['epistemic-reasoning'] | ['miscellaneous'] | [ 1.38074696e-01 1.35558951e+00 -2.19021648e-01 -4.61246014e-01
3.30197453e-01 -5.04687607e-01 1.02785647e+00 5.52817285e-01
-2.66607642e-01 9.31813657e-01 9.40781385e-02 -6.66960180e-01
-5.97649217e-01 -1.01271093e+00 -5.18972695e-01 -5.24879754e-01
6.00436389e-01 4.84498769e-01 8.65264460e-02 -3.71244818... | [8.849723815917969, 6.631740093231201] |
d723d2da-dd57-45a1-92df-4f057460330c | unsupervised-homography-estimation-with | 2205.03821 | null | https://arxiv.org/abs/2205.03821v1 | https://arxiv.org/pdf/2205.03821v1.pdf | Unsupervised Homography Estimation with Coplanarity-Aware GAN | Estimating homography from an image pair is a fundamental problem in image alignment. Unsupervised learning methods have received increasing attention in this field due to their promising performance and label-free training. However, existing methods do not explicitly consider the problem of plane-induced parallax, whi... | ['Shuaicheng Liu', 'Qijun Zhao', 'Chunyu Lin', 'Nianjin Ye', 'Yuhang Lu', 'Mingbo Hong'] | 2022-05-08 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Hong_Unsupervised_Homography_Estimation_With_Coplanarity-Aware_GAN_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Hong_Unsupervised_Homography_Estimation_With_Coplanarity-Aware_GAN_CVPR_2022_paper.pdf | cvpr-2022-1 | ['homography-estimation'] | ['computer-vision'] | [ 5.75996041e-01 1.34713784e-01 -4.71808985e-02 -2.97511846e-01
-6.91880643e-01 -4.18506056e-01 5.00155449e-01 -5.66342294e-01
1.94673032e-01 4.16540474e-01 2.14671835e-01 5.16159683e-02
2.02032402e-01 -1.01409471e+00 -8.85074675e-01 -9.81839120e-01
5.16900301e-01 3.09269905e-01 1.94030300e-01 -3.06721032... | [8.847541809082031, -2.307605504989624] |
d79c6fb7-5592-4654-b496-5581a89d8ecb | audioslots-a-slot-centric-generative-model | 2305.05591 | null | https://arxiv.org/abs/2305.05591v1 | https://arxiv.org/pdf/2305.05591v1.pdf | AudioSlots: A slot-centric generative model for audio separation | In a range of recent works, object-centric architectures have been shown to be suitable for unsupervised scene decomposition in the vision domain. Inspired by these methods we present AudioSlots, a slot-centric generative model for blind source separation in the audio domain. AudioSlots is built using permutation-equiv... | ['Thomas Kipf', 'John R. Hershey', 'Klaus Greff', 'Scott Wisdom', 'Pradyumna Reddy'] | 2023-05-09 | null | null | null | null | ['speech-separation'] | ['speech'] | [ 4.74078298e-01 1.39083520e-01 1.36100635e-01 -4.28755343e-01
-1.42484832e+00 -6.92031324e-01 8.62441659e-01 -4.45494413e-01
-1.91446021e-01 3.20335507e-01 9.60125983e-01 -2.81061623e-02
-2.40439773e-01 -2.07231611e-01 -7.09986508e-01 -9.53197300e-01
-1.72917977e-01 6.08448207e-01 -5.92641858e-03 1.58107936... | [15.23212718963623, 5.285333633422852] |
238268cf-cbfd-4752-87d0-e946aeb31633 | visual-relationship-detection-with-deep | null | null | https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Visual+relationship+detection+with+deep+structural+ranking&btnG= | https://pdfs.semanticscholar.org/0709/f6328229a79c44be715db90df028786a3129.pdf | Visual relationship detection with deep structural ranking | Visual relationship detection aims to describe the interactions between pairs of objects. Different from individual object learning tasks, the number of possible relationships is much larger, which makes it hard to explore only based on the visual appearance of objects. In addition, due to the limited human effort, the... | ['Xilin Chen', 'Hong Chang', 'Yuhong Guo', 'Kongming Liang'] | 2018-04-27 | null | null | null | null | ['visual-relationship-detection'] | ['computer-vision'] | [ 1.76266924e-01 -1.45674154e-01 -2.63852924e-01 -3.54188949e-01
-3.85958068e-02 -2.49697626e-01 4.87395614e-01 4.74984527e-01
-1.95809066e-01 4.70010012e-01 9.93919596e-02 7.72265643e-02
-4.14602548e-01 -7.31046855e-01 -5.36055446e-01 -4.56210315e-01
1.31898122e-02 1.88691318e-01 5.75766504e-01 -2.25469526... | [10.197175979614258, 1.6504255533218384] |
ab1b714b-bbc9-4ae4-8e3f-e97cec48cfe4 | a-benchmark-for-edge-preserving-image | 1904.01579 | null | http://arxiv.org/abs/1904.01579v1 | http://arxiv.org/pdf/1904.01579v1.pdf | A Benchmark for Edge-Preserving Image Smoothing | Edge-preserving image smoothing is an important step for many low-level
vision problems. Though many algorithms have been proposed, there are several
difficulties hindering its further development. First, most existing algorithms
cannot perform well on a wide range of image contents using a single parameter
setting. Se... | ['Yizhou Yu', 'Xixi Jia', 'Feida Zhu', 'Zhetong Liang', 'Lei Zhang'] | 2019-04-02 | null | null | null | null | ['image-smoothing'] | ['computer-vision'] | [ 1.73719138e-01 -1.04223236e-01 -4.29990217e-02 -3.67414087e-01
-7.99036205e-01 -1.37864262e-01 7.00170696e-01 -1.17436342e-01
-5.96959710e-01 5.81846058e-01 3.16121101e-01 -7.29074609e-03
1.70891911e-01 -6.55441463e-01 -6.51616633e-01 -7.63882518e-01
-7.50113279e-02 -2.89888054e-01 7.40634620e-01 -2.23121196... | [10.868101119995117, -1.4345905780792236] |
5a5da9d1-6bed-49d4-bace-29e0fbe79be5 | data-domain-adaptation-aided-deep-table | 2211.06648 | null | https://arxiv.org/abs/2211.06648v1 | https://arxiv.org/pdf/2211.06648v1.pdf | DATa: Domain Adaptation-Aided Deep Table Detection Using Visual-Lexical Representations | Considerable research attention has been paid to table detection by developing not only rule-based approaches reliant on hand-crafted heuristics but also deep learning approaches. Although recent studies successfully perform table detection with enhanced results, they often experience performance degradation when they ... | ['Won-Yong Shin', 'Dongwoo Lee', 'Joungbin An', 'Hyebin Kwon'] | 2022-11-12 | null | null | null | null | ['table-detection'] | ['miscellaneous'] | [ 3.37182909e-01 6.98473901e-02 -2.46622205e-01 -2.50667840e-01
-7.12347984e-01 -5.84692836e-01 5.67173183e-01 5.99574924e-01
-3.97435874e-01 6.82056963e-01 -1.77959755e-01 -3.27699900e-01
1.68151811e-01 -1.03029764e+00 -1.11686957e+00 -2.17959344e-01
1.50181606e-01 8.06395948e-01 4.32939321e-01 -1.35136351... | [11.632781982421875, 2.974062442779541] |
a0579bf3-5de5-4791-ba27-a1d58cd4b425 | text-generation-with-speech-synthesis-for-asr | 2305.16333 | null | https://arxiv.org/abs/2305.16333v1 | https://arxiv.org/pdf/2305.16333v1.pdf | Text Generation with Speech Synthesis for ASR Data Augmentation | Aiming at reducing the reliance on expensive human annotations, data synthesis for Automatic Speech Recognition (ASR) has remained an active area of research. While prior work mainly focuses on synthetic speech generation for ASR data augmentation, its combination with text generation methods is considerably less explo... | ['Xi Chen', 'Irina-Elena Veliche', 'Jessie Salas', 'Ethan Campbell-Taylor', "Antony D'Avirro", 'David Zhang', 'Duc Le', 'Farnaz Abtahi', 'Nelson Cheng', 'David Goss-Grubbs', 'Shashank Jain', 'Ziran Jiang', 'Gil Keren', 'Zhuangqun Huang'] | 2023-05-22 | null | null | null | null | ['text-augmentation', 'automatic-speech-recognition', 'speech-synthesis'] | ['natural-language-processing', 'speech', 'speech'] | [ 1.03895032e+00 7.15342581e-01 -1.72863826e-01 -3.08754653e-01
-1.16189337e+00 -4.14611816e-01 9.58395541e-01 -4.05388400e-02
-4.01858538e-01 7.48985887e-01 7.06324577e-01 -8.22068274e-01
8.19518387e-01 -3.34857732e-01 -6.23261988e-01 -2.91958004e-01
6.40877366e-01 6.30831659e-01 -2.44319573e-01 -5.42638302... | [14.422961235046387, 6.906684875488281] |
4bc9e854-bebf-4ac0-a3f4-c8a580db0be0 | can-machine-generate-traditional-chinese | 1606.05829 | null | http://arxiv.org/abs/1606.05829v1 | http://arxiv.org/pdf/1606.05829v1.pdf | Can Machine Generate Traditional Chinese Poetry? A Feigenbaum Test | Recent progress in neural learning demonstrated that machines can do well in
regularized tasks, e.g., the game of Go. However, artistic activities such as
poem generation are still widely regarded as human's special capability. In
this paper, we demonstrate that a simple neural model can imitate human in some
tasks of ... | ['Qixin Wang', 'Dong Wang', 'Tianyi Luo'] | 2016-06-19 | null | null | null | null | ['game-of-go'] | ['playing-games'] | [ 4.49137807e-01 3.56726855e-01 1.47848457e-01 3.73515338e-02
-6.24245346e-01 -3.15463424e-01 8.69710147e-01 -4.87349123e-01
-2.68154144e-01 8.86440933e-01 2.58583546e-01 -3.98759097e-01
2.73922861e-01 -1.27482557e+00 -5.48287928e-01 -4.66651678e-01
4.90027726e-01 7.25520492e-01 5.21152187e-03 -8.38064134... | [11.585979461669922, 9.369278907775879] |
7e32c481-ee10-445a-9c3f-703c473778e5 | only-a-matter-of-style-age-transformation | 2102.02754 | null | https://arxiv.org/abs/2102.02754v2 | https://arxiv.org/pdf/2102.02754v2.pdf | Only a Matter of Style: Age Transformation Using a Style-Based Regression Model | The task of age transformation illustrates the change of an individual's appearance over time. Accurately modeling this complex transformation over an input facial image is extremely challenging as it requires making convincing, possibly large changes to facial features and head shape, while still preserving the input ... | ['Daniel Cohen-Or', 'Or Patashnik', 'Yuval Alaluf'] | 2021-02-04 | null | null | null | null | ['face-age-editing'] | ['computer-vision'] | [ 5.37590861e-01 6.52647436e-01 3.03346794e-02 -6.67001069e-01
-3.21695387e-01 -4.32091802e-01 8.12241971e-01 -4.79638577e-01
-1.94898844e-01 5.63066304e-01 3.12539369e-01 2.86542803e-01
4.42792684e-01 -8.00726771e-01 -8.92359495e-01 -7.33771980e-01
3.44667077e-01 3.36398661e-01 -5.81830919e-01 -2.21066456... | [12.507688522338867, -0.24584843218326569] |
3c08d016-016e-42ed-ae7b-38a98b622d05 | face-morphing-attack-detection-with-denoising-1 | 2306.15733 | null | https://arxiv.org/abs/2306.15733v1 | https://arxiv.org/pdf/2306.15733v1.pdf | Face Morphing Attack Detection with Denoising Diffusion Probabilistic Models | Morphed face images have recently become a growing concern for existing face verification systems, as they are relatively easy to generate and can be used to impersonate someone's identity for various malicious purposes. Efficient Morphing Attack Detection (MAD) that generalizes well across different morphing technique... | ['Vitomir Štruc', 'Marija Ivanovska'] | 2023-06-27 | face-morphing-attack-detection-with-denoising | https://ieeexplore.ieee.org/document/10156877 | https://lmi.fe.uni-lj.si/wp-content/uploads/2023/06/IWBF2023___Face_Morphing_Attack_Detection_with_Denoising_Diffusion_Probabilistic_Models.pdf | international-workshop-on-biometrics-and | ['face-verification'] | ['computer-vision'] | [ 5.14258407e-02 -4.62910712e-01 -8.83542672e-02 -4.87767130e-01
-7.68653572e-01 -6.99058235e-01 8.97536278e-01 6.40860498e-02
-2.33707547e-01 6.14747047e-01 -4.83021408e-01 -2.22828329e-01
6.11697547e-02 -7.06210554e-01 -5.35961747e-01 -7.42667079e-01
-3.33430767e-01 5.86937070e-01 1.97614357e-01 -2.27749512... | [13.014703750610352, 1.127640962600708] |
c307c6ab-dce0-467b-9811-b85000869022 | precursor-of-anomaly-detection-for-irregular | 2306.15489 | null | https://arxiv.org/abs/2306.15489v2 | https://arxiv.org/pdf/2306.15489v2.pdf | Precursor-of-Anomaly Detection for Irregular Time Series | Anomaly detection is an important field that aims to identify unexpected patterns or data points, and it is closely related to many real-world problems, particularly to applications in finance, manufacturing, cyber security, and so on. While anomaly detection has been studied extensively in various fields, detecting fu... | ['Noseong Park', 'Jaehoon Lee', 'Sheo Yon Jhin'] | 2023-06-27 | null | null | null | null | ['anomaly-detection', 'multi-task-learning', 'irregular-time-series'] | ['methodology', 'methodology', 'time-series'] | [ 2.64726430e-01 -5.25928199e-01 2.47151643e-01 -2.12059692e-01
-3.58908266e-01 -3.13349396e-01 6.81036055e-01 7.32325256e-01
-1.37658969e-01 4.81321394e-01 -3.10616016e-01 -7.78754711e-01
-3.54816347e-01 -5.79980493e-01 -5.12806714e-01 -8.00447285e-01
-6.07897580e-01 3.43559235e-01 2.63558507e-01 -3.28486919... | [7.350043773651123, 2.666863441467285] |
617f91c9-e445-4f89-894b-ac5aaa2cf422 | specialist-diffusion-plug-and-play-sample | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Lu_Specialist_Diffusion_Plug-and-Play_Sample-Efficient_Fine-Tuning_of_Text-to-Image_Diffusion_Models_To_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Lu_Specialist_Diffusion_Plug-and-Play_Sample-Efficient_Fine-Tuning_of_Text-to-Image_Diffusion_Models_To_CVPR_2023_paper.pdf | Specialist Diffusion: Plug-and-Play Sample-Efficient Fine-Tuning of Text-to-Image Diffusion Models To Learn Any Unseen Style | Diffusion models have demonstrated impressive capability of text-conditioned image synthesis, and broader application horizons are emerging by personalizing those pretrained diffusion models toward generating some specialized target object or style. In this paper, we aim to learn an unseen style by simply fine-tuni... | ['Humphrey Shi', 'Zhangyang Wang', 'Shant Navasardyan', 'Kai Wang', 'Hazarapet Tunanyan', 'Haoming Lu'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['disentanglement'] | ['methodology'] | [ 2.89188921e-01 9.76695716e-02 -1.18613549e-01 -1.73031464e-01
-7.77515411e-01 -6.28894150e-01 8.79946053e-01 -5.21913886e-01
-2.53447205e-01 7.52598703e-01 4.09690946e-01 -5.04837669e-02
-6.60547928e-04 -8.01026285e-01 -6.64039254e-01 -6.97703540e-01
3.24660957e-01 8.34388733e-01 9.89046693e-02 -4.74140674... | [11.347957611083984, -0.3368754982948303] |
832ce4ae-c256-40e1-b886-bb3f5d32a4bf | enforcing-interpretability-and-its | 2010.13764 | null | https://arxiv.org/abs/2010.13764v2 | https://arxiv.org/pdf/2010.13764v2.pdf | Enforcing Interpretability and its Statistical Impacts: Trade-offs between Accuracy and Interpretability | To date, there has been no formal study of the statistical cost of interpretability in machine learning. As such, the discourse around potential trade-offs is often informal and misconceptions abound. In this work, we aim to initiate a formal study of these trade-offs. A seemingly insurmountable roadblock is the lack o... | ['Daniel M. Roy', 'Shai Ben-David', 'Gintare Karolina Dziugaite'] | 2020-10-26 | null | null | null | null | ['misconceptions'] | ['miscellaneous'] | [ 5.06744504e-01 8.78202975e-01 -1.47372156e-01 -6.96901739e-01
-6.40633345e-01 -7.09064901e-01 6.10222042e-01 4.18697596e-01
-5.31014204e-01 6.08716249e-01 2.66453505e-01 -7.60292947e-01
-5.32931626e-01 -4.43766385e-01 -6.08083546e-01 -6.18769825e-01
1.49230585e-01 4.88520682e-01 -3.39434236e-01 1.79029956... | [8.741117477416992, 5.708701133728027] |
793ec5be-105f-4fde-a8ef-e867425304d4 | salsi-a-new-seismic-attribute-for-salt-dome | 1901.02937 | null | http://arxiv.org/abs/1901.02937v1 | http://arxiv.org/pdf/1901.02937v1.pdf | SalSi: A new seismic attribute for salt dome detection | In this paper, we propose a saliency-based attribute, SalSi, to detect salt
dome bodies within seismic volumes. SalSi is based on the saliency theory and
modeling of the human vision system (HVS). In this work, we aim to highlight
the parts of the seismic volume that receive highest attention from the human
interpreter... | ['Tariq Alshawi', 'Ghassan AlRegib', 'Zhiling Long', 'Muhammad Amir Shafiq'] | 2019-01-09 | null | null | null | null | ['seismic-interpretation'] | ['miscellaneous'] | [-1.03235140e-01 9.79048386e-02 6.40732527e-01 -4.43670340e-02
-5.06092310e-01 -2.46809945e-01 4.72841799e-01 3.60879004e-01
-5.84067106e-01 2.09500507e-01 4.58989799e-01 5.27012683e-02
-1.80722833e-01 -6.72883630e-01 -4.06668246e-01 -8.18763971e-01
-3.75310987e-01 1.68989614e-01 9.21442509e-01 -3.88425291... | [9.46243953704834, -0.626992404460907] |
94207fd0-be29-47c4-a5e2-e6d9b6fea489 | recovering-aes-keys-with-a-deep-cold-boot | 2106.04876 | null | https://arxiv.org/abs/2106.04876v1 | https://arxiv.org/pdf/2106.04876v1.pdf | Recovering AES Keys with a Deep Cold Boot Attack | Cold boot attacks inspect the corrupted random access memory soon after the power has been shut down. While most of the bits have been corrupted, many bits, at random locations, have not. Since the keys in many encryption schemes are being expanded in memory into longer keys with fixed redundancies, the keys can often ... | ['Lior Wolf', 'Eliya Nachmani', 'Itamar Zimerman'] | 2021-06-09 | null | null | null | null | ['cryptanalysis'] | ['miscellaneous'] | [ 1.97791785e-01 -2.51634289e-02 -1.82609558e-01 1.31929040e-01
-5.17051935e-01 -9.76273298e-01 5.29948957e-02 3.49038869e-01
-5.13863564e-01 5.69845259e-01 -2.89617926e-01 -1.17437351e+00
1.84474722e-01 -1.13898325e+00 -8.90952349e-01 -7.98285723e-01
-3.91275495e-01 8.90681967e-02 3.04750532e-01 -4.96307909... | [5.776139259338379, 7.337114334106445] |
d439cd38-a074-47a0-b550-100b1da4166a | on-the-complementarity-between-pre-training-1 | 2209.03316 | null | https://arxiv.org/abs/2209.03316v3 | https://arxiv.org/pdf/2209.03316v3.pdf | On the Complementarity between Pre-Training and Random-Initialization for Resource-Rich Machine Translation | Pre-Training (PT) of text representations has been successfully applied to low-resource Neural Machine Translation (NMT). However, it usually fails to achieve notable gains (sometimes, even worse) on resource-rich NMT on par with its Random-Initialization (RI) counterpart. We take the first step to investigate the comp... | ['DaCheng Tao', 'Weifeng Liu', 'Yu Cao', 'Li Shen', 'Liang Ding', 'Changtong Zan'] | 2022-09-07 | null | https://aclanthology.org/2022.coling-1.445 | https://aclanthology.org/2022.coling-1.445.pdf | coling-2022-10 | ['low-resource-neural-machine-translation'] | ['natural-language-processing'] | [ 3.80104125e-01 -2.22287308e-02 -5.55471838e-01 -8.21470395e-02
-1.14257979e+00 -5.60701489e-01 8.57142746e-01 -6.77906796e-02
-4.22143191e-01 1.02572572e+00 4.67003793e-01 -6.44201040e-01
2.11956445e-02 -5.33859313e-01 -8.49915802e-01 -6.70024872e-01
3.97927940e-01 8.97688329e-01 -3.44641030e-01 -5.35500050... | [11.639525413513184, 10.114669799804688] |
213c43a4-62e5-4a1e-b5db-bdd6a864b9e0 | character-aware-neural-morphological | null | null | https://aclanthology.org/P17-2105 | https://aclanthology.org/P17-2105.pdf | Character-Aware Neural Morphological Disambiguation | We develop a language-independent, deep learning-based approach to the task of morphological disambiguation. Guided by the intuition that the correct analysis should be {``}most similar{''} to the context, we propose dense representations for morphological analyses and surface context and a simple yet effective way of ... | ['Gulmira Tolegen', 'Alymzhan Toleu', 'Aibek Makazhanov'] | 2017-07-01 | null | null | null | acl-2017-7 | ['morphological-disambiguation'] | ['natural-language-processing'] | [-1.90918043e-01 -5.63513786e-02 1.80197552e-01 -4.11348373e-01
-6.97936714e-01 -1.03932273e+00 5.00910759e-01 7.30821729e-01
-7.28854358e-01 6.04970992e-01 3.06720674e-01 -8.93382430e-01
-7.77689219e-02 -8.81274104e-01 -2.77692258e-01 -4.90634561e-01
-1.31346270e-01 7.44524896e-01 2.66935825e-01 -7.17141449... | [10.407983779907227, 10.132219314575195] |
d23bd0d8-a424-45b9-8bfd-df8a9407e386 | analysis-over-vision-based-models-for | 2305.17451 | null | https://arxiv.org/abs/2305.17451v1 | https://arxiv.org/pdf/2305.17451v1.pdf | Analysis over vision-based models for pedestrian action anticipation | Anticipating human actions in front of autonomous vehicles is a challenging task. Several papers have recently proposed model architectures to address this problem by combining multiple input features to predict pedestrian crossing actions. This paper focuses specifically on using images of the pedestrian's context as ... | ['François Charpillet', 'François Aioun', 'Julien Moreau', 'Lina Achaji'] | 2023-05-27 | null | null | null | null | ['action-anticipation', 'autonomous-vehicles'] | ['computer-vision', 'computer-vision'] | [ 1.28502890e-01 4.40097898e-01 -1.37623772e-01 -7.05585241e-01
-4.48239475e-01 -1.61875024e-01 9.63209033e-01 7.43791535e-02
-2.44503915e-01 4.67494011e-01 3.91134232e-01 -5.47146320e-01
2.14915007e-01 -7.49539852e-01 -8.43792737e-01 -3.07516783e-01
-5.75127378e-02 3.10327321e-01 5.63873410e-01 -4.88926768... | [6.236481189727783, 0.6743611097335815] |
6e7ef943-2ca8-4592-a505-82e28764abca | timers-document-level-temporal-relation | null | null | https://aclanthology.org/2021.acl-short.67 | https://aclanthology.org/2021.acl-short.67.pdf | TIMERS: Document-level Temporal Relation Extraction | We present TIMERS - a TIME, Rhetorical and Syntactic-aware model for document-level temporal relation classification in the English language. Our proposed method leverages rhetorical discourse features and temporal arguments from semantic role labels, in addition to traditional local syntactic features, trained through... | ['Dinesh Manocha', 'Quan Hung Tran', 'Vlad Morariu', 'Franck Dernoncourt', 'Rajiv Jain', 'Puneet Mathur'] | 2021-08-01 | null | null | null | acl-2021-5 | ['temporal-relation-extraction', 'temporal-relation-classification'] | ['natural-language-processing', 'natural-language-processing'] | [-1.31963283e-01 5.13439417e-01 -1.31148648e+00 -6.54248476e-01
-9.00967360e-01 -7.53959298e-01 1.44056046e+00 5.39228439e-01
-3.24887604e-01 9.53392863e-01 1.07603812e+00 -5.79149365e-01
-7.25352466e-02 -5.72996259e-01 -3.60112548e-01 -2.45580971e-01
-5.50461709e-01 5.05865991e-01 4.87179399e-01 -7.30721176... | [9.197713851928711, 9.219600677490234] |
783e7608-8452-44eb-a041-a224fd890255 | multi-modal-graph-learning-over-umls | 2307.04461 | null | https://arxiv.org/abs/2307.04461v1 | https://arxiv.org/pdf/2307.04461v1.pdf | Multi-modal Graph Learning over UMLS Knowledge Graphs | Clinicians are increasingly looking towards machine learning to gain insights about patient evolutions. We propose a novel approach named Multi-Modal UMLS Graph Learning (MMUGL) for learning meaningful representations of medical concepts using graph neural networks over knowledge graphs based on the unified medical lan... | ['Rita Kuznetsova', 'Gunnar Rätsch', 'Manuel Burger'] | 2023-07-10 | null | null | null | null | ['graph-learning', 'knowledge-graphs'] | ['graphs', 'knowledge-base'] | [ 3.15496922e-01 8.43876839e-01 -5.68450809e-01 -4.43874091e-01
-8.65763485e-01 -2.47761086e-01 3.56873155e-01 1.20873165e+00
3.14467810e-02 5.77670932e-01 6.44440413e-01 -6.58759713e-01
-6.12813354e-01 -1.02271450e+00 -4.48976815e-01 -1.45378247e-01
-5.39145887e-01 8.59705210e-01 4.90100384e-02 -9.14817005... | [7.901112079620361, 6.934577941894531] |
d458ead6-3415-4288-9f73-2228267262f3 | automatic-fast-and-robust-characterization-of | 1805.12071 | null | http://arxiv.org/abs/1805.12071v2 | http://arxiv.org/pdf/1805.12071v2.pdf | Automatic, fast and robust characterization of noise distributions for diffusion MRI | Knowledge of the noise distribution in magnitude diffusion MRI images is the
centerpiece to quantify uncertainties arising from the acquisition process. The
use of parallel imaging methods, the number of receiver coils and imaging
filters applied by the scanner, amongst other factors, dictate the resulting
signal distr... | ['Samuel St-Jean', 'Max A. Viergever', 'Alexander Leemans', 'Alberto De Luca'] | 2018-05-30 | null | null | null | null | ['noise-estimation'] | ['medical'] | [ 3.30529213e-01 -4.59822893e-01 3.06436688e-01 -4.46302950e-01
-6.29778028e-01 -6.67750776e-01 4.10887599e-01 1.52026847e-01
-8.17531943e-01 1.11136663e+00 -9.01721269e-02 -2.55793601e-01
-3.33644748e-01 -2.15499848e-01 -5.76974988e-01 -1.16471291e+00
-5.01358688e-01 4.92922157e-01 5.60188234e-01 4.07649547... | [13.37751579284668, -2.4774341583251953] |
7b65548c-9fe1-48f0-b7aa-7a58c0be9f4e | systematic-review-on-reinforcement-learning | 2305.07466 | null | https://arxiv.org/abs/2305.07466v1 | https://arxiv.org/pdf/2305.07466v1.pdf | Systematic Review on Reinforcement Learning in the Field of Fintech | Applications of Reinforcement Learning in the Finance Technology (Fintech) have acquired a lot of admiration lately. Undoubtedly Reinforcement Learning, through its vast competence and proficiency, has aided remarkable results in the field of Fintech. The objective of this systematic survey is to perform an exploratory... | ['Rashid Mehmood', 'Iyad Katib', 'Nadeem Malibari'] | 2023-04-29 | null | null | null | null | ['portfolio-optimization'] | ['time-series'] | [-8.52881074e-01 1.10283501e-01 -4.34665740e-01 -1.67349372e-02
-2.40628764e-01 -3.92467290e-01 2.14407861e-01 2.14902192e-01
-2.47315913e-01 9.71883118e-01 1.18852049e-01 -5.15307963e-01
-8.82214010e-01 -1.05863881e+00 -2.58493513e-01 -5.34218550e-01
-1.17086448e-01 7.73174524e-01 -2.92228371e-01 -5.97830176... | [4.456421375274658, 3.9221487045288086] |
65dd750b-aca9-414a-8b4d-88a9f3093cd5 | see-more-know-more-unsupervised-video-object-1 | 2001.06810 | null | https://arxiv.org/abs/2001.06810v1 | https://arxiv.org/pdf/2001.06810v1.pdf | See More, Know More: Unsupervised Video Object Segmentation with Co-Attention Siamese Networks | We introduce a novel network, called CO-attention Siamese Network (COSNet), to address the unsupervised video object segmentation task from a holistic view. We emphasize the importance of inherent correlation among video frames and incorporate a global co-attention mechanism to improve further the state-of-the-art deep... | ['Ling Shao', 'Xiankai Lu', 'Jianbing Shen', 'Fatih Porikli', 'Chao Ma', 'Wenguan Wang'] | 2020-01-19 | see-more-know-more-unsupervised-video-object | http://openaccess.thecvf.com/content_CVPR_2019/html/Lu_See_More_Know_More_Unsupervised_Video_Object_Segmentation_With_Co-Attention_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Lu_See_More_Know_More_Unsupervised_Video_Object_Segmentation_With_Co-Attention_CVPR_2019_paper.pdf | cvpr-2019-6 | ['unsupervised-video-object-segmentation', 'video-polyp-segmentation'] | ['computer-vision', 'computer-vision'] | [ 2.15412155e-01 -3.17445427e-01 -4.08830523e-01 -3.90375882e-01
-8.03848922e-01 -3.44220102e-01 3.87104660e-01 -2.76847154e-01
-4.64731246e-01 3.91352892e-01 4.47074592e-01 2.69979566e-01
9.68972966e-02 -3.76125365e-01 -9.98898566e-01 -7.24808753e-01
-2.28094414e-01 5.48355058e-02 6.85865760e-01 2.36566305... | [9.246973037719727, -0.16227711737155914] |
edb3a825-67a6-4b2a-8052-715a1321405a | learning-hard-alignments-with-variational | 1705.05524 | null | http://arxiv.org/abs/1705.05524v2 | http://arxiv.org/pdf/1705.05524v2.pdf | Learning Hard Alignments with Variational Inference | There has recently been significant interest in hard attention models for
tasks such as object recognition, visual captioning and speech recognition.
Hard attention can offer benefits over soft attention such as decreased
computational cost, but training hard attention models can be difficult because
of the discrete la... | ['Colin Raffel', 'George Tucker', 'Chung-Cheng Chiu', 'Navdeep Jaitly', 'Kevin Swersky', 'Dieterich Lawson'] | 2017-05-16 | null | null | null | null | ['hard-attention'] | ['methodology'] | [ 1.73498183e-01 2.38941148e-01 -2.44990945e-01 -2.72432119e-01
-1.22254443e+00 -2.30787724e-01 6.82416916e-01 -2.44837552e-01
-5.90518117e-01 9.70909774e-01 3.64290565e-01 -3.04434747e-01
-1.44540705e-02 -1.35125950e-01 -8.55096161e-01 -7.85546720e-01
2.00640917e-01 6.01505220e-01 -5.59296422e-02 2.09782496... | [10.09252643585205, 1.9616557359695435] |
b30829df-fe45-4ced-8e0a-c9ec9665d935 | faheem-at-nadi-shared-task-identifying-the | null | null | https://aclanthology.org/2020.wanlp-1.29 | https://aclanthology.org/2020.wanlp-1.29.pdf | Faheem at NADI shared task: Identifying the dialect of Arabic tweet | This paper describes Faheem (adj. of understand), our submission to NADI (Nuanced Arabic Dialect Identification) shared task. With so many Arabic dialects being under-studied due to the scarcity of the resources, the objective is to identify the Arabic dialect used in the tweet, country wise. We propose a machine learn... | ['Aqil Azmi', 'Nouf AlShenaifi'] | null | null | null | null | coling-wanlp-2020-12 | ['dialect-identification'] | ['natural-language-processing'] | [-5.62233925e-01 -4.41417068e-01 -2.98720568e-01 -4.71523970e-01
-5.87094426e-01 -9.10893500e-01 9.73619401e-01 3.11635107e-01
-7.36237764e-01 7.40970910e-01 4.39501464e-01 -6.42541051e-01
-9.88106877e-02 -8.98468435e-01 2.98543517e-02 -5.80752969e-01
-3.95726323e-01 9.37910259e-01 -2.55738884e-01 -1.13204634... | [10.184222221374512, 10.722079277038574] |
f5e9b226-ab39-4181-b0c9-323597ff3379 | ernie-sparse-learning-hierarchical-efficient-1 | 2203.12276 | null | https://arxiv.org/abs/2203.12276v1 | https://arxiv.org/pdf/2203.12276v1.pdf | ERNIE-SPARSE: Learning Hierarchical Efficient Transformer Through Regularized Self-Attention | Sparse Transformer has recently attracted a lot of attention since the ability for reducing the quadratic dependency on the sequence length. We argue that two factors, information bottleneck sensitivity and inconsistency between different attention topologies, could affect the performance of the Sparse Transformer. Thi... | ['Haifeng Wang', 'Hua Wu', 'Hao Tian', 'Yu Sun', 'Zhida Feng', 'Shikun Feng', 'Yuxiang Lu', 'Li Chen', 'Jiaxiang Liu', 'Yang Liu'] | 2022-03-23 | null | null | null | null | ['sparse-learning'] | ['methodology'] | [ 2.18072027e-01 -6.93119392e-02 -1.99109316e-02 -1.91416696e-01
-1.39469278e+00 -2.90639967e-01 4.27917689e-01 -2.50880271e-01
-2.69575864e-01 5.84148824e-01 6.44369781e-01 -2.59813219e-01
-6.63671494e-02 -3.38000506e-01 -7.24956572e-01 -7.58085907e-01
6.16806000e-03 5.04531741e-01 2.55098313e-01 -4.58010912... | [10.905529022216797, 6.6412153244018555] |
30f40fce-8853-4faa-99ff-d6e7cad78d25 | transform-contrast-and-tell-coherent-entity | 2302.02124 | null | https://arxiv.org/abs/2302.02124v1 | https://arxiv.org/pdf/2302.02124v1.pdf | Transform, Contrast and Tell: Coherent Entity-Aware Multi-Image Captioning | Coherent entity-aware multi-image captioning aims to generate coherent captions for multiple adjacent images in a news document. There are coherence relationships among adjacent images because they often describe same entities or events. These relationships are important for entity-aware multi-image captioning, but are... | ['Jingqiang Chen'] | 2023-02-04 | null | null | null | null | ['coherence-evaluation'] | ['natural-language-processing'] | [ 2.98643529e-01 2.78100312e-01 -2.02488959e-01 -3.75230908e-01
-1.21347618e+00 -3.86583000e-01 9.54119146e-01 8.10167752e-03
-2.50211984e-01 9.11188543e-01 8.03026736e-01 1.73104838e-01
3.39849502e-01 -4.14530456e-01 -1.14413857e+00 -5.52745104e-01
3.22854072e-01 6.91938400e-01 3.35608095e-01 -2.14790910... | [10.974367141723633, 1.0159038305282593] |
e61156f6-adaf-40cc-84e9-e17f316f340f | viewer-centred-surface-completion-for | 2209.06407 | null | https://arxiv.org/abs/2209.06407v1 | https://arxiv.org/pdf/2209.06407v1.pdf | Viewer-Centred Surface Completion for Unsupervised Domain Adaptation in 3D Object Detection | Every autonomous driving dataset has a different configuration of sensors, originating from distinct geographic regions and covering various scenarios. As a result, 3D detectors tend to overfit the datasets they are trained on. This causes a drastic decrease in accuracy when the detectors are trained on one dataset and... | ['Stewart Worrall', 'Eduardo Nebot', 'Mao Shan', 'Julie Stephany Berrio', 'Darren Tsai'] | 2022-09-14 | null | null | null | null | ['multi-target-domain-adaptation'] | ['computer-vision'] | [ 2.53897786e-01 7.43497834e-02 -6.13963231e-02 -6.82785809e-01
-8.52311373e-01 -7.94250786e-01 6.50148809e-01 -5.11713363e-02
-4.38414127e-01 2.93043196e-01 -8.10852125e-02 -1.52016118e-01
1.24674812e-02 -7.21084177e-01 -1.08248293e+00 -3.52488488e-01
3.31717938e-01 8.40857923e-01 5.97190917e-01 -1.27425835... | [8.146768569946289, -2.6696958541870117] |
5f746465-a0b5-4e29-a8e6-38e9a9265b82 | ffhq-uv-normalized-facial-uv-texture-dataset | 2211.13874 | null | https://arxiv.org/abs/2211.13874v2 | https://arxiv.org/pdf/2211.13874v2.pdf | FFHQ-UV: Normalized Facial UV-Texture Dataset for 3D Face Reconstruction | We present a large-scale facial UV-texture dataset that contains over 50,000 high-quality texture UV-maps with even illuminations, neutral expressions, and cleaned facial regions, which are desired characteristics for rendering realistic 3D face models under different lighting conditions. The dataset is derived from a ... | ['Linchao Bao', 'Jinshan Pan', 'Haoxian Zhang', 'Di Kang', 'Haoran Bai'] | 2022-11-25 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Bai_FFHQ-UV_Normalized_Facial_UV-Texture_Dataset_for_3D_Face_Reconstruction_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Bai_FFHQ-UV_Normalized_Facial_UV-Texture_Dataset_for_3D_Face_Reconstruction_CVPR_2023_paper.pdf | cvpr-2023-1 | ['3d-face-reconstruction', 'face-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 3.37370217e-01 1.35324374e-01 2.53742278e-01 -6.82793796e-01
-1.02060437e+00 -2.28850693e-01 6.34003341e-01 -8.92825186e-01
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3.22256207e-01 -9.02121127e-01 -9.18879747e-01 -7.51919508e-01
4.61937338e-01 5.93850195e-01 -1.88675880e-01 -4.11304533... | [12.811223030090332, -0.26517248153686523] |
58867e20-1ba4-47c2-ba75-7d9fa8e6d7cf | retrieval-re-ranking-and-multi-task-learning | null | null | https://aclanthology.org/2021.eacl-main.26 | https://aclanthology.org/2021.eacl-main.26.pdf | Retrieval, Re-ranking and Multi-task Learning for Knowledge-Base Question Answering | Question answering over knowledge bases (KBQA) usually involves three sub-tasks, namely topic entity detection, entity linking and relation detection. Due to the large number of entities and relations inside knowledge bases (KB), previous work usually utilized sophisticated rules to narrow down the search space and man... | ['Bing Xiang', 'Ramesh Nallapati', 'Patrick Ng', 'Zhiguo Wang'] | 2021-04-01 | null | null | null | eacl-2021-2 | ['knowledge-base-question-answering'] | ['natural-language-processing'] | [-3.61687869e-01 2.05121949e-01 -5.03860652e-01 -1.66618094e-01
-1.42451251e+00 -4.38114643e-01 4.04493570e-01 3.52353990e-01
-7.88083255e-01 9.22632873e-01 4.61330749e-02 -2.95807838e-01
-3.84980321e-01 -9.96092021e-01 -1.03674066e+00 -1.99715853e-01
-1.11195341e-01 9.27235186e-01 9.11934435e-01 -3.26659173... | [10.336955070495605, 8.060524940490723] |
482e7685-df89-4359-90b3-c22a34e5b9ed | a-hybrid-system-for-systematic-generalization | 2306.17249 | null | https://arxiv.org/abs/2306.17249v1 | https://arxiv.org/pdf/2306.17249v1.pdf | A Hybrid System for Systematic Generalization in Simple Arithmetic Problems | Solving symbolic reasoning problems that require compositionality and systematicity is considered one of the key ingredients of human intelligence. However, symbolic reasoning is still a great challenge for deep learning models, which often cannot generalize the reasoning pattern to out-of-distribution test cases. In t... | ['Alessandro Sperduti', 'Alberto Testolin', 'Flavio Petruzzellis'] | 2023-06-29 | null | null | null | null | ['systematic-generalization'] | ['reasoning'] | [ 3.83890122e-01 1.75992459e-01 2.01028228e-01 -4.05384958e-01
-3.93200696e-01 -8.56270373e-01 3.25322926e-01 1.32919833e-01
-3.14992189e-01 6.80934370e-01 -3.88898402e-01 -8.58081281e-01
-2.20926732e-01 -1.23767304e+00 -9.56842482e-01 -1.51810735e-01
3.78646217e-02 9.22724247e-01 3.95726204e-01 -5.84706545... | [9.415355682373047, 7.303221702575684] |
b35f60a3-4c39-44de-886f-d0be2c04a282 | post-hoc-analysis-of-arabic-transformer | 2210.09990 | null | https://arxiv.org/abs/2210.09990v1 | https://arxiv.org/pdf/2210.09990v1.pdf | Post-hoc analysis of Arabic transformer models | Arabic is a Semitic language which is widely spoken with many dialects. Given the success of pre-trained language models, many transformer models trained on Arabic and its dialects have surfaced. While there have been an extrinsic evaluation of these models with respect to downstream NLP tasks, no work has been carried... | ['Hassan Sajjad', 'Fahim Dalvi', 'Nadir Durrani', 'Ahmed Abdelali'] | 2022-10-18 | null | null | null | null | ['morphological-tagging'] | ['natural-language-processing'] | [-1.09645061e-01 3.01474314e-02 1.51465401e-01 -3.87546360e-01
-1.76556230e-01 -1.00128841e+00 7.64650643e-01 3.44567209e-01
-4.71130192e-01 1.64191797e-01 6.79004014e-01 -4.49327528e-01
1.05531521e-01 -8.54088306e-01 -4.32563484e-01 -7.35471606e-01
-1.40179753e-01 6.44374371e-01 8.79848450e-02 -6.94074571... | [10.631967544555664, 10.016727447509766] |
22045b0a-386e-41c5-beb6-8d56bd657b86 | slabert-talk-pretty-one-day-modeling-second | 2305.19589 | null | https://arxiv.org/abs/2305.19589v1 | https://arxiv.org/pdf/2305.19589v1.pdf | SLABERT Talk Pretty One Day: Modeling Second Language Acquisition with BERT | Second language acquisition (SLA) research has extensively studied cross-linguistic transfer, the influence of linguistic structure of a speaker's native language [L1] on the successful acquisition of a foreign language [L2]. Effects of such transfer can be positive (facilitating acquisition) or negative (impeding acqu... | ['Vera Tobin', 'Alekhya Yadavalli', 'Aditya Yadavalli'] | 2023-05-31 | null | null | null | null | ['language-acquisition', 'cross-lingual-transfer'] | ['natural-language-processing', 'natural-language-processing'] | [-1.02551192e-01 4.41845395e-02 -6.28344834e-01 -4.63811696e-01
-6.71148181e-01 -6.37589693e-01 7.26457179e-01 2.60418266e-01
-5.25502741e-01 5.92464149e-01 6.78313732e-01 -1.05567634e+00
-1.42051384e-01 -6.17119312e-01 -8.39936316e-01 -1.94187179e-01
9.17245671e-02 2.73452044e-01 5.37314676e-02 -5.40629745... | [10.872736930847168, 9.953985214233398] |
9ad7a769-9915-4d41-92d9-c2fc83450071 | toward-discourse-aware-models-for | null | null | https://aclanthology.org/2021.ranlp-srw.29 | https://aclanthology.org/2021.ranlp-srw.29.pdf | Toward Discourse-Aware Models for Multilingual Fake News Detection | Statements that are intentionally misstated (or manipulated) are of considerable interest to researchers, government, security, and financial systems. According to deception literature, there are reliable cues for detecting deception and the belief that liars give off cues that may indicate their deception is near-univ... | ['Thiago Pardo', 'Fabrício Benevenuto', 'Francielle Vargas'] | null | null | null | null | ranlp-2021-9 | ['deception-detection'] | ['miscellaneous'] | [-2.52789445e-03 4.44598585e-01 -3.22891563e-01 -2.69559056e-01
-6.85344815e-01 -8.45947683e-01 1.12085199e+00 4.50655878e-01
-1.38728365e-01 1.03237104e+00 8.24996233e-01 -7.53291607e-01
-6.01358153e-02 -2.94031024e-01 -3.99963796e-01 -2.33282983e-01
5.69248796e-01 1.71955153e-01 -1.16537854e-01 -6.64993942... | [8.224410057067871, 10.349774360656738] |
bec47391-6f2f-4c5c-be6c-c94358f1fabf | emotionic-emotional-inertia-and-contagion | 2303.11117 | null | https://arxiv.org/abs/2303.11117v2 | https://arxiv.org/pdf/2303.11117v2.pdf | EmotionIC: Emotional Inertia and Contagion-driven Dependency Modelling for Emotion Recognition in Conversation | Emotion Recognition in Conversation (ERC) has attracted growing attention in recent years as a result of the advancement and implementation of human-computer interface technologies. However, previous approaches to modeling global and local context dependencies lost the diversity of dependency information and do not tak... | ['Zhigang Zeng', 'XiaoPing Wang', 'Jiang Li', 'Yingjian Liu'] | 2023-03-20 | null | null | null | null | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [-2.69333005e-01 8.55047554e-02 1.95095748e-01 -8.29303205e-01
-2.17487752e-01 -1.97623104e-01 7.20838785e-01 -2.06776448e-02
-1.53920189e-01 5.04226625e-01 8.67213070e-01 1.69592217e-01
2.83155084e-01 -3.34398031e-01 1.94863379e-01 -7.26811826e-01
-2.25802451e-01 1.63709551e-01 -4.90575671e-01 -5.80986857... | [13.015625, 6.080755710601807] |
935e5008-7bc5-43ed-a03c-a9439b1c5e5b | generating-complement-data-for-aspect-term | null | null | https://aclanthology.org/2022.deeplo-1.21 | https://aclanthology.org/2022.deeplo-1.21.pdf | Generating Complement Data for Aspect Term Extraction with GPT-2 | t | ['Bonan Min and Thien Huu Nguyen', 'Franck Dernoncourt', 'Amir Pouran Ben Veyseh'] | null | null | null | null | deeplo-2022-7 | ['term-extraction'] | ['natural-language-processing'] | [ 1.89939722e-01 2.36788392e-01 -5.91508627e-01 -3.67945790e-01
-5.24475813e-01 -8.09045732e-01 2.93398023e-01 -7.17031658e-01
-1.54879212e-01 7.98508823e-01 -1.99629426e-01 -6.90531909e-01
-3.04593027e-01 -7.58767843e-01 -8.23488533e-01 -8.08756053e-01
-9.00167286e-01 6.62851155e-01 2.46676758e-01 -2.83387691... | [-7.271073818206787, 3.8188908100128174] |
57da3ff4-36b4-428e-b330-188bded37248 | ltc-se-expanding-the-potential-of-liquid-time | 2304.08691 | null | https://arxiv.org/abs/2304.08691v1 | https://arxiv.org/pdf/2304.08691v1.pdf | LTC-SE: Expanding the Potential of Liquid Time-Constant Neural Networks for Scalable AI and Embedded Systems | We present LTC-SE, an improved version of the Liquid Time-Constant (LTC) neural network algorithm originally proposed by Hasani et al. in 2021. This algorithm unifies the Leaky-Integrate-and-Fire (LIF) spiking neural network model with Continuous-Time Recurrent Neural Networks (CTRNNs), Neural Ordinary Differential Equ... | ['Hamdan Abdellatef', 'Ferhat Atasoy', 'Michael Bidollahkhani'] | 2023-04-18 | null | null | null | null | ['time-series-prediction'] | ['time-series'] | [-2.96674222e-02 -2.59479195e-01 1.45718932e-01 -3.79531421e-02
1.17367491e-01 -5.99264562e-01 4.17231590e-01 -5.69416463e-01
-5.95196426e-01 7.43513167e-01 -2.00733811e-01 -5.25860846e-01
-1.83549240e-01 -5.03107786e-01 -5.53457379e-01 -9.72013772e-01
-4.41188514e-01 -1.78358018e-01 5.27425230e-01 -2.32558116... | [8.17261791229248, 2.5860421657562256] |
e8952d7d-799f-4673-ab8f-cee23fc2b489 | recogym-a-reinforcement-learning-environment | 1808.00720 | null | http://arxiv.org/abs/1808.00720v2 | http://arxiv.org/pdf/1808.00720v2.pdf | RecoGym: A Reinforcement Learning Environment for the problem of Product Recommendation in Online Advertising | Recommender Systems are becoming ubiquitous in many settings and take many
forms, from product recommendation in e-commerce stores, to query suggestions
in search engines, to friend recommendation in social networks. Current
research directions which are largely based upon supervised learning from
historical data appea... | ['Flavian vasile', 'David Rohde', 'Travis Dunlop', 'Stephen Bonner', 'Alexandros Karatzoglou'] | 2018-08-02 | null | null | null | null | ['product-recommendation'] | ['miscellaneous'] | [-6.05527908e-02 -2.95834262e-02 -5.72956920e-01 -3.66222769e-01
-1.13228306e-01 -6.41034484e-01 6.71418250e-01 1.88421771e-01
-5.37987530e-01 5.50461650e-01 3.24697733e-01 -6.23129904e-01
-7.17767894e-01 -9.62781429e-01 -5.72381735e-01 -5.92814505e-01
-2.76668161e-01 6.39953136e-01 2.98833281e-01 -7.39544749... | [9.935948371887207, 5.70648193359375] |
e74427ab-4cf9-4e29-96da-3f0ab49f46c3 | learning-dynamic-knowledge-graphs-to | 2002.09127 | null | https://arxiv.org/abs/2002.09127v4 | https://arxiv.org/pdf/2002.09127v4.pdf | Learning Dynamic Belief Graphs to Generalize on Text-Based Games | Playing text-based games requires skills in processing natural language and sequential decision making. Achieving human-level performance on text-based games remains an open challenge, and prior research has largely relied on hand-crafted structured representations and heuristics. In this work, we investigate how an ag... | ['Marc-Antoine Rondeau', 'Mikuláš Zelinka', 'Marc-Alexandre Côté', 'Ashutosh Adhikari', 'Xingdi Yuan', 'William L. Hamilton', 'Romain Laroche', 'Pascal Poupart', 'Jian Tang', 'Adam Trischler'] | 2020-02-21 | null | http://proceedings.neurips.cc/paper/2020/hash/1fc30b9d4319760b04fab735fbfed9a9-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/1fc30b9d4319760b04fab735fbfed9a9-Paper.pdf | neurips-2020-12 | ['text-based-games'] | ['playing-games'] | [ 1.49494573e-01 4.58333969e-01 -3.20276886e-01 -1.20984115e-01
-7.92299807e-01 -5.54881692e-01 8.74825239e-01 2.64676601e-01
-4.30249184e-01 8.07046413e-01 5.41182101e-01 -4.99315113e-01
-1.75788522e-01 -1.16067398e+00 -6.50878370e-01 -1.49343684e-01
-4.19548839e-01 1.22277641e+00 3.27379555e-01 -7.97267735... | [3.7882511615753174, 1.3968206644058228] |
a9e07ab6-3221-4349-890b-5d9839c3a698 | adaptive-plant-propagation-algorithm-for | 1708.07040 | null | http://arxiv.org/abs/1708.07040v1 | http://arxiv.org/pdf/1708.07040v1.pdf | Adaptive Plant Propagation Algorithm for Solving Economic Load Dispatch Problem | Optimization problems in design engineering are complex by nature, often
because of the involvement of critical objective functions accompanied by a
number of rigid constraints associated with the products involved. One such
problem is Economic Load Dispatch (ED) problem which focuses on the
optimization of the fuel co... | ['Sayan Nag'] | 2017-08-04 | null | null | null | null | ['metaheuristic-optimization'] | ['methodology'] | [ 1.54023275e-01 -1.20561048e-01 2.67696261e-01 1.91919938e-01
2.55968124e-01 -5.07943273e-01 2.39013672e-01 4.45365220e-01
-2.05299497e-01 9.45952296e-01 -3.97495627e-01 -1.36218756e-01
-1.15622354e+00 -7.73888588e-01 -8.48796517e-02 -8.72500300e-01
3.33349183e-02 7.40438521e-01 -1.57323480e-01 -5.35910964... | [5.728173732757568, 3.4316728115081787] |
ccd97aaa-675c-426d-bd09-682432b05c70 | recurrent-saliency-transformation-network | 1709.04518 | null | http://arxiv.org/abs/1709.04518v4 | http://arxiv.org/pdf/1709.04518v4.pdf | Recurrent Saliency Transformation Network: Incorporating Multi-Stage Visual Cues for Small Organ Segmentation | We aim at segmenting small organs (e.g., the pancreas) from abdominal CT
scans. As the target often occupies a relatively small region in the input
image, deep neural networks can be easily confused by the complex and variable
background. To alleviate this, researchers proposed a coarse-to-fine approach,
which used pre... | ['Elliot K. Fishman', 'Yuyin Zhou', 'Qihang Yu', 'Lingxi Xie', 'Alan L. Yuille', 'Yan Wang'] | 2017-09-13 | recurrent-saliency-transformation-network-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Yu_Recurrent_Saliency_Transformation_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Yu_Recurrent_Saliency_Transformation_CVPR_2018_paper.pdf | cvpr-2018-6 | ['pancreas-segmentation'] | ['medical'] | [ 1.89531460e-01 2.20828786e-01 -3.26348573e-01 -2.26414442e-01
-5.92221797e-01 -2.36962184e-01 1.67027116e-01 2.67111361e-01
-4.74082381e-01 6.62995398e-01 6.41851425e-02 -1.02263495e-01
1.56333461e-01 -6.25285923e-01 -7.10444927e-01 -8.61686110e-01
1.16284102e-01 2.87316889e-01 6.64859593e-01 1.29151568... | [14.563542366027832, -2.572664737701416] |
2e248361-037f-4146-a2ba-a3f06de37426 | learning-to-reuse-distractors-to-support | 2210.13964 | null | https://arxiv.org/abs/2210.13964v2 | https://arxiv.org/pdf/2210.13964v2.pdf | Learning to Reuse Distractors to support Multiple Choice Question Generation in Education | Multiple choice questions (MCQs) are widely used in digital learning systems, as they allow for automating the assessment process. However, due to the increased digital literacy of students and the advent of social media platforms, MCQ tests are widely shared online, and teachers are continuously challenged to create n... | ['Thomas Demeester', 'Chris Develder', 'Johannes Deleu', 'Lucas Sterckx', 'Amir Hadifar', 'Semere Kiros Bitew'] | 2022-10-25 | null | null | null | null | ['question-generation'] | ['natural-language-processing'] | [-3.71144116e-01 -1.11535117e-02 1.74521759e-01 -3.48891228e-01
-1.10573947e+00 -1.15143836e+00 5.04365087e-01 5.54890692e-01
-4.06641215e-01 6.54522598e-01 2.10170791e-01 -5.80041111e-01
-4.82498676e-01 -6.73522234e-01 -4.90890145e-01 -8.81153196e-02
4.74896073e-01 4.31141168e-01 9.48160648e-01 -6.25349522... | [11.257516860961914, 8.146797180175781] |
f4974305-ca63-4179-9822-85ff0c1c924f | learning-on-gradients-generalized-artifacts | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Tan_Learning_on_Gradients_Generalized_Artifacts_Representation_for_GAN-Generated_Images_Detection_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Tan_Learning_on_Gradients_Generalized_Artifacts_Representation_for_GAN-Generated_Images_Detection_CVPR_2023_paper.pdf | Learning on Gradients: Generalized Artifacts Representation for GAN-Generated Images Detection | Recently, there has been a significant advancement in image generation technology, known as GAN. It can easily generate realistic fake images, leading to an increased risk of abuse. However, most image detectors suffer from sharp performance drops in unseen domains. The key of fake image detection is to develop a g... | ['Yunchao Wei', 'Guanghua Gu', 'Shikui Wei', 'Yao Zhao', 'Chuangchuang Tan'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['fake-image-detection'] | ['computer-vision'] | [ 3.08068991e-01 -2.90637732e-01 1.07054478e-02 5.42951785e-02
-9.23577726e-01 -4.26308304e-01 5.83300292e-01 -2.83176512e-01
6.69731721e-02 5.45566082e-01 -4.89085279e-02 -1.92790076e-01
4.63663042e-01 -7.50375748e-01 -7.83482075e-01 -7.47673571e-01
4.19188261e-01 -3.70120674e-01 1.09625593e-01 -9.28537995... | [12.434298515319824, 1.0358116626739502] |
d42baeea-1c0e-41c5-a115-d7cc9dd70f83 | aspect-is-not-you-need-no-aspect-differential | null | null | https://aclanthology.org/2022.naacl-main.115 | https://aclanthology.org/2022.naacl-main.115.pdf | Aspect Is Not You Need: No-aspect Differential Sentiment Framework for Aspect-based Sentiment Analysis | Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment classification task. Most recent efforts adopt pre-trained model to classify the sentences with aspects. However, the aspect sentiment bias from pre-trained model brings some noise to the ABSA task. Besides, traditional methods using cross-entropy loss ... | ['Xu Bai', 'Lei Jiang', 'Huailiang Peng', 'Rui Liu', 'Jiahao Cao'] | null | null | null | null | naacl-2022-7 | ['aspect-based-sentiment-analysis'] | ['natural-language-processing'] | [ 1.46365939e-02 -1.63150147e-01 -3.88273560e-02 -8.59294534e-01
-4.24100488e-01 -6.84580147e-01 6.88655674e-01 2.79806674e-01
-4.75215435e-01 4.37771976e-01 2.18848661e-01 -2.06153825e-01
9.39516276e-02 -1.08927095e+00 -5.32087088e-01 -7.49253750e-01
6.26071274e-01 1.36875153e-01 1.00374028e-01 -7.99676597... | [11.424759864807129, 6.682975769042969] |
a702aeca-6a25-4845-bb1d-229fb9a8d3dd | lifting-transformer-for-3d-human-pose | 2103.14304 | null | https://arxiv.org/abs/2103.14304v8 | https://arxiv.org/pdf/2103.14304v8.pdf | Exploiting Temporal Contexts with Strided Transformer for 3D Human Pose Estimation | Despite the great progress in 3D human pose estimation from videos, it is still an open problem to take full advantage of a redundant 2D pose sequence to learn representative representations for generating one 3D pose. To this end, we propose an improved Transformer-based architecture, called Strided Transformer, which... | ['Wenming Yang', 'Pichao Wang', 'Mengyuan Liu', 'Runwei Ding', 'Hong Liu', 'Wenhao Li'] | 2021-03-26 | null | null | null | null | ['monocular-3d-human-pose-estimation'] | ['computer-vision'] | [ 1.01975702e-01 -7.74442554e-02 -1.76385026e-02 -4.60635096e-01
-7.14580834e-01 -1.49854943e-01 4.41497535e-01 -3.68136853e-01
-3.89835209e-01 4.24314201e-01 3.80468965e-01 1.57434329e-01
1.46660864e-01 -5.04257619e-01 -9.08209741e-01 -5.52230060e-01
-1.32966908e-02 2.37815127e-01 1.42235741e-01 -2.44660497... | [7.144070625305176, -0.6615970134735107] |
c4905274-6f5f-40b7-8839-ca1b876aab48 | concealed-object-detection | 2102.10274 | null | https://arxiv.org/abs/2102.10274v2 | https://arxiv.org/pdf/2102.10274v2.pdf | Concealed Object Detection | We present the first systematic study on concealed object detection (COD), which aims to identify objects that are "perfectly" embedded in their background. The high intrinsic similarities between the concealed objects and their background make COD far more challenging than traditional object detection/segmentation. To... | ['Ling Shao', 'Ming-Ming Cheng', 'Ge-Peng Ji', 'Deng-Ping Fan'] | 2021-02-20 | null | null | null | null | ['camouflaged-object-segmentation', 'dichotomous-image-segmentation'] | ['computer-vision', 'computer-vision'] | [ 2.26775244e-01 -1.42274633e-01 -1.46638915e-01 -3.41788918e-01
-5.96935153e-01 -8.74065399e-01 7.35279202e-01 -7.18704611e-02
-4.11828160e-01 2.15879962e-01 2.69923180e-01 -8.42269957e-02
1.85486436e-01 -4.05154079e-01 -8.07118356e-01 -7.96640694e-01
-5.10892451e-01 2.35087305e-01 6.94358230e-01 4.18279599... | [9.513334274291992, -0.04513311758637428] |
10ce663f-36bb-462e-9464-8563e5cd8db6 | visualgptscore-visio-linguistic-reasoning | 2306.01879 | null | https://arxiv.org/abs/2306.01879v1 | https://arxiv.org/pdf/2306.01879v1.pdf | VisualGPTScore: Visio-Linguistic Reasoning with Multimodal Generative Pre-Training Scores | Vision-language models (VLMs) discriminatively pre-trained with contrastive image-text matching losses such as $P(\text{match}|\text{text}, \text{image})$ have been criticized for lacking compositional understanding. This means they might output similar scores even if the original caption is rearranged into a different... | ['Deva Ramanan', 'Pengchuan Zhang', 'Deepak Pathak', 'Xinyue Chen', 'Zhiqiu Lin'] | 2023-06-02 | null | null | null | null | ['image-text-matching', 'text-matching'] | ['computer-vision', 'natural-language-processing'] | [ 5.61248958e-01 1.91323325e-01 -9.06133950e-02 -5.90750098e-01
-1.31132400e+00 -6.44323766e-01 1.04137218e+00 6.86192587e-02
-7.08041012e-01 3.54183584e-01 2.33693242e-01 -5.69828033e-01
-3.01546864e-02 -6.74460828e-01 -1.16671503e+00 -7.16055870e-01
2.39866242e-01 3.93874884e-01 -3.66984047e-02 6.56762868... | [10.837247848510742, 1.596927523612976] |
5f1a5f18-29e2-4c4f-b01d-5e8ba68d9e84 | adaptive-unfolding-total-variation-network | 2110.00984 | null | https://arxiv.org/abs/2110.00984v4 | https://arxiv.org/pdf/2110.00984v4.pdf | Adaptive Unfolding Total Variation Network for Low-Light Image Enhancement | Real-world low-light images suffer from two main degradations, namely, inevitable noise and poor visibility. Since the noise exhibits different levels, its estimation has been implemented in recent works when enhancing low-light images from raw Bayer space. When it comes to sRGB color space, the noise estimation become... | ['Wentian Shi', 'Daming Shi', 'Chuanjun Zheng'] | 2021-10-03 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Zheng_Adaptive_Unfolding_Total_Variation_Network_for_Low-Light_Image_Enhancement_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Zheng_Adaptive_Unfolding_Total_Variation_Network_for_Low-Light_Image_Enhancement_ICCV_2021_paper.pdf | iccv-2021-1 | ['noise-estimation'] | ['medical'] | [ 4.81330514e-01 -5.57831824e-01 5.34538031e-01 -3.05436403e-01
-5.22508025e-01 -5.15284091e-02 1.53128237e-01 -5.63334942e-01
-3.71697575e-01 6.11499608e-01 7.09352195e-02 -4.79123965e-02
-1.82209998e-01 -7.93245316e-01 -4.88546133e-01 -1.36534083e+00
6.43182337e-01 -2.97354490e-01 2.64388859e-01 -3.63787740... | [10.810745239257812, -2.520984411239624] |
91cf5663-83d4-4282-b248-3369c2db4d56 | optimising-the-input-window-alignment-in-cd | 1606.09163 | null | http://arxiv.org/abs/1606.09163v1 | http://arxiv.org/pdf/1606.09163v1.pdf | Optimising The Input Window Alignment in CD-DNN Based Phoneme Recognition for Low Latency Processing | We present a systematic analysis on the performance of a phonetic recogniser
when the window of input features is not symmetric with respect to the current
frame. The recogniser is based on Context Dependent Deep Neural Networks
(CD-DNNs) and Hidden Markov Models (HMMs). The objective is to reduce the
latency of the sy... | ['Akash Kumar Dhaka', 'Giampiero Salvi'] | 2016-06-29 | null | null | null | null | ['low-latency-processing'] | ['robots'] | [ 5.00728488e-01 9.65425372e-02 1.38350070e-01 -3.56833696e-01
-5.87784171e-01 -2.67740488e-01 7.03284979e-01 -8.71027634e-02
-8.95039141e-01 4.71295446e-01 3.37580174e-01 -4.15727854e-01
-3.44430259e-03 -2.34149814e-01 -2.63330817e-01 -8.36856544e-01
-1.98836669e-01 2.78562695e-01 5.20270824e-01 -1.08945608... | [14.684435844421387, 5.927675247192383] |
de54d70a-d5f2-4bd3-8525-34900e4221d8 | universal-deep-network-for-steganalysis-of | 2111.12231 | null | https://arxiv.org/abs/2111.12231v1 | https://arxiv.org/pdf/2111.12231v1.pdf | Universal Deep Network for Steganalysis of Color Image based on Channel Representation | Up to now, most existing steganalytic methods are designed for grayscale images, and they are not suitable for color images that are widely used in current social networks. In this paper, we design a universal color image steganalysis network (called UCNet) in spatial and JPEG domains. The proposed method includes prep... | ['Jiwu Huang', 'Shunquan Tan', 'Weiqi Luo', 'Kangkang Wei'] | 2021-11-24 | null | null | null | null | ['steganalysis'] | ['computer-vision'] | [ 5.97551167e-01 -2.90324688e-01 9.76449996e-02 7.49469176e-02
-5.67395822e-04 -1.93897456e-01 2.37111241e-01 -6.91826165e-01
-5.00987411e-01 4.20356423e-01 -3.50976169e-01 -6.49017453e-01
3.54765892e-01 -1.24108112e+00 -4.38465238e-01 -9.53709424e-01
-8.21161717e-02 -5.81248999e-01 5.80441594e-01 -5.03611386... | [4.295793056488037, 8.054793357849121] |
27a39bc8-7cb1-4ecc-bcc3-ec9453183c56 | energy-efficient-downlink-semantic-generative | 2306.05041 | null | https://arxiv.org/abs/2306.05041v1 | https://arxiv.org/pdf/2306.05041v1.pdf | Energy-Efficient Downlink Semantic Generative Communication with Text-to-Image Generators | In this paper, we introduce a novel semantic generative communication (SGC) framework, where generative users leverage text-to-image (T2I) generators to create images locally from downloaded text prompts, while non-generative users directly download images from a base station (BS). Although generative users help reduce... | ['Jinho Choi', 'Sooyoung Kim', 'Jihong Park', 'Hyein Lee'] | 2023-06-08 | null | null | null | null | ['total-energy'] | ['miscellaneous'] | [ 5.37022114e-01 5.37533581e-01 -9.09070745e-02 1.70687847e-02
-9.43060696e-01 -7.38777637e-01 5.45383275e-01 -4.42538559e-01
1.14333797e-02 7.36191809e-01 1.20388687e-01 -3.88387650e-01
4.75467086e-01 -1.06614399e+00 -7.24056780e-01 -1.05406499e+00
8.53660181e-02 2.42944807e-01 -1.04018450e-01 2.88392574... | [11.102506637573242, -0.4519113004207611] |
b8c7b0d9-2f83-44f9-930e-c84628a81f27 | are-quantitative-features-of-lung-nodules | 1908.05667 | null | https://arxiv.org/abs/1908.05667v1 | https://arxiv.org/pdf/1908.05667v1.pdf | Are Quantitative Features of Lung Nodules Reproducible at Different CT Acquisition and Reconstruction Parameters? | Consistency and duplicability in Computed Tomography (CT) output is essential to quantitative imaging for lung cancer detection and monitoring. This study of CT-detected lung nodules investigated the reproducibility of volume-, density-, and texture-based features (outcome variables) over routine ranges of radiation-do... | ['Matthew T. Bigelow', 'Vikash Gupta', "Thomas P. O'Donnell", 'Barbaros S. Erdal', 'Rainer Grimmer', 'Gehan F. M. Ibrahim', 'Andreas Wimmer', 'Richard D. White', 'Mutlu Demirer', 'Chiemezie C. Amadi', 'Luciano M. Prevedello', 'Kevin J. Little'] | 2019-08-14 | null | null | null | null | ['texture-classification'] | ['computer-vision'] | [ 2.18196958e-01 -2.60272443e-01 -3.15396070e-01 1.41718769e-02
-1.02896440e+00 -4.78545964e-01 3.08823556e-01 6.21518910e-01
-5.48842847e-01 2.37764373e-01 2.24266991e-01 -7.80072749e-01
-4.70938832e-01 -8.97975028e-01 -1.98051050e-01 -7.65067816e-01
-2.83678383e-01 6.27347469e-01 9.42202687e-01 3.77593011... | [15.069999694824219, -2.162994861602783] |
dbda3837-a1b2-4cf9-bef4-35cfd0b618c4 | spectral-cross-domain-neural-network-with | 2301.10171 | null | https://arxiv.org/abs/2301.10171v1 | https://arxiv.org/pdf/2301.10171v1.pdf | Spectral Cross-Domain Neural Network with Soft-adaptive Threshold Spectral Enhancement | Electrocardiography (ECG) signals can be considered as multi-variable time-series. The state-of-the-art ECG data classification approaches, based on either feature engineering or deep learning techniques, treat separately spectral and time domains in machine learning systems. No spectral-time domain communication mecha... | ['Rossella Arcucci', 'Weiping Ding', 'Sibo Cheng', 'Che Liu'] | 2023-01-10 | null | null | null | null | ['electrocardiography-ecg', 'feature-engineering'] | ['methodology', 'methodology'] | [ 3.59037042e-01 -2.48850897e-01 7.77375698e-02 -3.55544090e-01
-7.55272508e-01 -4.48599279e-01 4.61827479e-02 1.84270203e-01
-4.68135297e-01 8.53971243e-01 -4.22678709e-01 -2.30103105e-01
-7.25585699e-01 -5.26370943e-01 -3.24647427e-01 -8.47850323e-01
-5.50254643e-01 1.46073233e-02 -3.34885418e-02 -2.13002607... | [14.28073501586914, 3.284977436065674] |
c324cfc2-7518-4ee7-aa83-dd19d5b49048 | a-style-aware-content-loss-for-real-time-hd | 1807.10201 | null | http://arxiv.org/abs/1807.10201v2 | http://arxiv.org/pdf/1807.10201v2.pdf | A Style-Aware Content Loss for Real-time HD Style Transfer | Recently, style transfer has received a lot of attention. While much of this
research has aimed at speeding up processing, the approaches are still lacking
from a principled, art historical standpoint: a style is more than just a
single image or an artist, but previous work is limited to only a single
instance of a sty... | ['Björn Ommer', 'Dmytro Kotovenko', 'Sabine Lang', 'Artsiom Sanakoyeu'] | 2018-07-26 | a-style-aware-content-loss-for-real-time-hd-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Artsiom_Sanakoyeu_A_Style-aware_Content_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Artsiom_Sanakoyeu_A_Style-aware_Content_ECCV_2018_paper.pdf | eccv-2018-9 | ['video-style-transfer', 'image-stylization'] | ['computer-vision', 'computer-vision'] | [ 3.64283234e-01 -8.96458700e-02 1.18923739e-01 -3.52040648e-01
-5.15425265e-01 -7.03607202e-01 7.76983917e-01 -2.48306200e-01
-3.95297438e-01 6.64179146e-01 2.82990426e-01 7.79541284e-02
2.96151400e-01 -9.29919362e-01 -1.14962447e+00 -3.74256134e-01
4.34895873e-01 4.56393778e-01 2.33934909e-01 -2.08899170... | [11.445302963256836, -0.3635285496711731] |
1b3ad9d5-57c7-4551-9009-ed0ad1785c26 | deep-learning-human-mind-for-automated-visual | 1609.00344 | null | https://arxiv.org/abs/1609.00344v2 | https://arxiv.org/pdf/1609.00344v2.pdf | Deep Learning Human Mind for Automated Visual Classification | What if we could effectively read the mind and transfer human visual capabilities to computer vision methods? In this paper, we aim at addressing this question by developing the first visual object classifier driven by human brain signals. In particular, we employ EEG data evoked by visual object stimuli combined with ... | ['Concetto Spampinato', 'Simone Palazzo', 'Nasim Souly', 'Isaak Kavasidis', 'Mubarak Shah', 'Daniela Giordano'] | 2016-09-01 | deep-learning-human-mind-for-automated-visual-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Spampinato_Deep_Learning_Human_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Spampinato_Deep_Learning_Human_CVPR_2017_paper.pdf | cvpr-2017-7 | ['object-categorization'] | ['computer-vision'] | [ 3.86128217e-01 8.52444395e-02 3.19893301e-01 -3.09221059e-01
-1.67439833e-01 -3.72114182e-01 8.85167181e-01 -4.12481725e-02
-6.66355193e-01 6.61956549e-01 -1.22092694e-01 -1.20287836e-01
-9.73537490e-02 -5.23350418e-01 -7.27042139e-01 -7.08362579e-01
-5.73240668e-02 2.38558888e-01 -8.84379447e-02 3.93840969... | [9.98229694366455, 2.423039674758911] |
7d748ff9-1011-4fc4-b83b-2f8a163d4844 | two-level-attention-with-two-stage-multi-task | 1811.12139 | null | http://arxiv.org/abs/1811.12139v1 | http://arxiv.org/pdf/1811.12139v1.pdf | Two-level Attention with Two-stage Multi-task Learning for Facial Emotion Recognition | Compared with facial emotion recognition on categorical model, the
dimensional emotion recognition can describe numerous emotions of the real
world more accurately. Most prior works of dimensional emotion estimation only
considered laboratory data and used video, speech or other multi-modal
features. The effect of thes... | ['Fuji Ren', 'Xiaohua Wang', 'Min Hu', 'Muzi Peng', 'Lijuan Pan', 'Chunhua Jin'] | 2018-11-29 | null | null | null | null | ['facial-emotion-recognition'] | ['computer-vision'] | [-3.35301682e-02 -2.51844168e-01 -2.29261001e-03 -6.30547702e-01
-3.72700006e-01 -1.45959839e-01 3.66925001e-01 -3.83140504e-01
-4.41653788e-01 4.63906705e-01 2.27471411e-01 3.93105447e-01
-2.50099003e-02 -2.86783487e-01 -1.92430139e-01 -8.18702877e-01
-1.18859097e-01 -2.89589107e-01 -4.70166624e-01 -3.21676403... | [13.6426362991333, 1.8166791200637817] |
78d781d1-853d-4aa1-8497-503d1b4622f3 | learning-transferable-visual-models-from | 2103.00020 | null | https://arxiv.org/abs/2103.00020v1 | https://arxiv.org/pdf/2103.00020v1.pdf | Learning Transferable Visual Models From Natural Language Supervision | State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories. This restricted form of supervision limits their generality and usability since additional labeled data is needed to specify any other visual concept. Learning directly from raw text about images is a promisi... | ['Ilya Sutskever', 'Gretchen Krueger', 'Jack Clark', 'Pamela Mishkin', 'Amanda Askell', 'Girish Sastry', 'Sandhini Agarwal', 'Gabriel Goh', 'Aditya Ramesh', 'Chris Hallacy', 'Jong Wook Kim', 'Alec Radford'] | 2021-02-26 | null | null | null | null | ['zero-shot-transfer-image-classification', 'open-vocabulary-attribute-detection', 'object-categorization', 'zero-shot-cross-modal-retrieval', 'meme-classification'] | ['computer-vision', 'computer-vision', 'computer-vision', 'miscellaneous', 'natural-language-processing'] | [ 4.34002548e-01 -8.49220678e-02 -5.13251126e-01 -5.98350346e-01
-8.45022261e-01 -8.15109372e-01 1.04752266e+00 4.52413596e-02
-6.97924435e-01 4.93921667e-01 2.81964242e-01 -2.31860667e-01
4.40064639e-01 -4.57461536e-01 -1.23114848e+00 -4.18037325e-01
2.68743008e-01 5.27544677e-01 3.40705603e-01 -2.00195070... | [10.084856033325195, 1.826259970664978] |
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