paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
260bffe2-9426-4702-8dd5-7d872aa16123 | predicting-survival-outcomes-in-the-presence | 2210.13891 | null | https://arxiv.org/abs/2210.13891v1 | https://arxiv.org/pdf/2210.13891v1.pdf | Predicting Survival Outcomes in the Presence of Unlabeled Data | Many clinical studies require the follow-up of patients over time. This is challenging: apart from frequently observed drop-out, there are often also organizational and financial challenges, which can lead to reduced data collection and, in turn, can complicate subsequent analyses. In contrast, there is often plenty of... | ['Celine Vens', 'Fateme Nateghi Haredasht'] | 2022-10-25 | null | null | null | null | ['survival-analysis'] | ['miscellaneous'] | [ 2.73374826e-01 -1.29499659e-01 -5.98876953e-01 -7.62433529e-01
-1.06812453e+00 -3.64291281e-01 2.78143048e-01 8.61286581e-01
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-3.68930787e-01 -5.66528141e-01 -5.04514456e-01 -8.87752354e-01
-1.35812119e-01 7.26338089e-01 -1.33530617e-01 5.25897481... | [7.720090866088867, 5.495548248291016] |
64ccf831-7b19-4d3b-86a6-6a9e1ce63f56 | deep-clustering-with-measure-propagation | 2104.08967 | null | https://arxiv.org/abs/2104.08967v3 | https://arxiv.org/pdf/2104.08967v3.pdf | Deep Clustering with Measure Propagation | Deep models have improved state-of-the-art for both supervised and unsupervised learning. For example, deep embedded clustering (DEC) has greatly improved the unsupervised clustering performance, by using stacked autoencoders for representation learning. However, one weakness of deep modeling is that the local neighbor... | ['Patrick Haffner', 'Michael Johnston', 'Qiming Huang', 'Padmasundari Gopalakrishnan', 'Badrinath Jayakumar', 'Minhua Chen'] | 2021-04-18 | null | null | null | null | ['text-clustering', 'short-text-clustering'] | ['natural-language-processing', 'natural-language-processing'] | [-2.74035960e-01 -2.85147373e-02 -3.02485526e-02 -3.49235028e-01
-5.07999480e-01 -5.58899701e-01 5.28896868e-01 4.44616348e-01
-5.83038151e-01 9.57190916e-02 4.60515231e-01 -1.82168111e-02
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-8.07927102e-02 6.09144449e-01 -4.67415974e-02 9.53144208... | [10.406146049499512, 6.587937355041504] |
f0ef62c3-cc61-4699-946a-302a548f119c | mdp-a-generalized-framework-for-text-guided | 2303.16765 | null | https://arxiv.org/abs/2303.16765v2 | https://arxiv.org/pdf/2303.16765v2.pdf | MDP: A Generalized Framework for Text-Guided Image Editing by Manipulating the Diffusion Path | Image generation using diffusion can be controlled in multiple ways. In this paper, we systematically analyze the equations of modern generative diffusion networks to propose a framework, called MDP, that explains the design space of suitable manipulations. We identify 5 different manipulations, including intermediate ... | ['Peter Wonka', 'Michael Birsak', 'Biao Zhang', 'Qian Wang'] | 2023-03-29 | null | null | null | null | ['text-guided-image-editing'] | ['computer-vision'] | [ 1.88132450e-01 1.60324693e-01 -1.61436066e-01 -8.64578784e-02
-8.34033117e-02 -6.80119395e-01 1.06327200e+00 -3.07377636e-01
-1.62786201e-01 5.99430442e-01 4.38143760e-01 -8.48516524e-02
-3.63274753e-01 -9.17003810e-01 -4.01921004e-01 -7.73580134e-01
7.20034018e-02 2.61068791e-01 3.20603371e-01 -4.91165668... | [11.354276657104492, -0.26944106817245483] |
bd786c7c-b7fd-41cb-99af-7ee29be8214f | efficient-majority-voting-in-digital-hardware | 2108.03979 | null | https://arxiv.org/abs/2108.03979v1 | https://arxiv.org/pdf/2108.03979v1.pdf | Efficient Majority Voting in Digital Hardware | In recent years, machine learning methods became increasingly important for a manifold number of applications. However, they often suffer from high computational requirements impairing their efficient use in real-time systems, even when employing dedicated hardware accelerators. Ensemble learning methods are especially... | ['Michael Lunglmayr', 'Mario Huemer', 'Stefan Baumgartner'] | 2021-08-09 | null | null | null | null | ['handwritten-digit-recognition'] | ['computer-vision'] | [ 4.53742683e-01 -1.27473712e-01 7.98924267e-02 -4.20071632e-01
-3.39203715e-01 -4.00820345e-01 8.22592258e-01 6.67287290e-01
-7.27084756e-01 6.12289488e-01 -7.12948859e-01 -8.09884906e-01
-5.80289736e-02 -1.00789654e+00 -3.87963951e-01 -8.76955092e-01
1.05274647e-01 4.33216274e-01 9.12865400e-02 9.07047093... | [8.227312088012695, 3.988574743270874] |
ed97c43e-63d5-4f64-81b6-44c2c4a88173 | an-explainable-classification-model-for | 2105.10368 | null | https://arxiv.org/abs/2105.10368v3 | https://arxiv.org/pdf/2105.10368v3.pdf | Development and evaluation of an Explainable Prediction Model for Chronic Kidney Disease Patients based on Ensemble Trees | Chronic Kidney Disease (CKD), where delayed recognition implies premature mortality, is currently experiencing a globally increasing incidence and high cost to health systems. Data mining allows discovering subtle patterns in CKD indicators to contribute to an early diagnosis. This work presents the development and eva... | ['Pedro A. Moreno-Sanchez'] | 2021-05-21 | null | null | null | null | ['kidney-function'] | ['medical'] | [-1.67627871e-01 2.22609550e-01 -1.51060164e-01 -6.23956382e-01
1.12646574e-03 9.69442725e-02 3.02310765e-01 7.90396631e-01
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-9.01785076e-01 -8.17516565e-01 7.54332468e-02 -4.77322191e-01
-5.28050780e-01 9.39520121e-01 -2.91310340e-01 1.64165217... | [8.45484733581543, 4.891333103179932] |
8b65d637-1637-402d-91de-07790af14a4e | detecting-backdoors-in-deep-text-classifiers | 2210.11264 | null | https://arxiv.org/abs/2210.11264v1 | https://arxiv.org/pdf/2210.11264v1.pdf | Detecting Backdoors in Deep Text Classifiers | Deep neural networks are vulnerable to adversarial attacks, such as backdoor attacks in which a malicious adversary compromises a model during training such that specific behaviour can be triggered at test time by attaching a specific word or phrase to an input. This paper considers the problem of diagnosing whether a ... | ['Trevor Cohn', 'Jun Wang', 'You Guo'] | 2022-10-11 | null | null | null | null | ['data-poisoning'] | ['adversarial'] | [ 5.94789982e-01 -6.35744706e-02 -1.59369946e-01 -1.23626009e-01
-5.04841745e-01 -1.50387335e+00 8.42375815e-01 4.61462647e-01
-5.42499423e-01 4.57979918e-01 -4.70061988e-01 -1.16070449e+00
1.71110556e-01 -9.96670127e-01 -1.06308115e+00 -6.54684424e-01
-2.06564859e-01 2.22848535e-01 4.24401879e-01 -2.12529540... | [5.843885898590088, 7.760409355163574] |
977388ce-1e7d-4998-af4f-317d25f34a7e | exploiting-segment-level-semantics-for-online | 2111.11044 | null | https://arxiv.org/abs/2111.11044v3 | https://arxiv.org/pdf/2111.11044v3.pdf | Exploring Segment-level Semantics for Online Phase Recognition from Surgical Videos | Automatic surgical phase recognition plays a vital role in robot-assisted surgeries. Existing methods ignored a pivotal problem that surgical phases should be classified by learning segment-level semantics instead of solely relying on frame-wise information. This paper presents a segment-attentive hierarchical consiste... | ['Xiaomeng Li', 'Xinpeng Ding'] | 2021-11-22 | null | null | null | null | ['surgical-phase-recognition'] | ['computer-vision'] | [ 4.44091499e-01 5.18948674e-01 -8.37192178e-01 -3.61339539e-01
-8.07294369e-01 -1.04317583e-01 9.25350934e-02 7.07984418e-02
-3.44453901e-01 4.85726535e-01 5.53801775e-01 -2.17613265e-01
-1.29591927e-01 -2.98997372e-01 -7.54399598e-01 -6.58892989e-01
-1.91678792e-01 9.57642645e-02 4.63783205e-01 -8.36524516... | [14.165145874023438, -3.26428484916687] |
416f3192-1c02-4c1f-bd78-04410f5d845a | evaluating-the-text-to-sql-capabilities-of | null | null | https://openreview.net/forum?id=lYli-bAuK54 | https://openreview.net/pdf?id=lYli-bAuK54 | Evaluating the Text-to-SQL Capabilities of Large Language Models | We perform an empirical evaluation of Text-to-SQL capabilities of the Codex language model. We find that, without any finetuning, Codex is a strong baseline on the Spider benchmark; we also analyze the failure modes of Codex in this setting. Furthermore, we demonstrate on the GeoQuery and Scholar benchmarks that a smal... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['text-to-sql'] | ['computer-code'] | [-6.21415079e-01 -1.01846218e-01 -9.04360235e-01 -4.68051553e-01
-1.11700523e+00 -7.89831996e-01 8.36117089e-01 1.25847280e-01
-1.01824112e-01 3.50733161e-01 4.69213307e-01 -8.20769966e-01
-2.43724406e-01 -6.92411780e-01 -9.27182853e-01 2.68401176e-01
-1.76481470e-01 5.42299688e-01 5.52412570e-01 -5.61274171... | [9.689699172973633, 7.858989238739014] |
ed0a16da-9336-4850-8cee-7976d2fe848d | emotional-talking-faces-making-videos-more | null | null | https://dl.acm.org/doi/10.1145/3551626.3564976 | https://dl.acm.org/doi/10.1145/3551626.3564976 | Emotional Talking Faces: Making Videos More Expressive and Realistic | Lip synchronization and talking face generation have gained a specific interest from the research community with the advent and need of digital communication in different fields. Prior works propose several elegant solutions to this problem. However, they often fail to create realistic-looking videos that account for p... | ['Rajiv Ratn Shah', 'Yifang Yin', 'Yi Yu', 'Sakshat Mali', 'Dhroov Goel', 'Sarthak Bhagat', 'Shagun Uppal', 'Sahil Goyal'] | 2022-12-13 | null | null | null | acm-multimedia-asia-2022-12 | ['talking-face-generation', 'face-generation'] | ['computer-vision', 'computer-vision'] | [-1.75299987e-01 1.23290330e-01 -1.36536509e-01 -7.08787382e-01
-3.36451717e-02 -5.51076651e-01 8.82922947e-01 -4.49552268e-01
1.43423080e-01 6.02238238e-01 6.88260436e-01 3.76600534e-01
2.83989638e-01 -5.56358278e-01 -2.44765297e-01 -3.03180188e-01
2.25087866e-01 -2.79592186e-01 -2.49023780e-01 -5.38260937... | [13.226481437683105, -0.3836461305618286] |
a8e75f3c-11e6-4b7f-9910-868e71be30fd | unsupervised-domain-adaptation-with-6 | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Hu_Unsupervised_Domain_Adaptation_With_Hierarchical_Gradient_Synchronization_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Hu_Unsupervised_Domain_Adaptation_With_Hierarchical_Gradient_Synchronization_CVPR_2020_paper.pdf | Unsupervised Domain Adaptation With Hierarchical Gradient Synchronization | Domain adaptation attempts to boost the performance on a target domain by borrowing knowledge from a well established source domain. To handle the distribution gap between two domains, the prominent approaches endeavor to extract domain-invariant features. It is known that after a perfect domain alignment the domain-in... | [' Xilin Chen', ' Shiguang Shan', ' Meina Kan', 'Lanqing Hu'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['partial-domain-adaptation'] | ['methodology'] | [ 9.69666392e-02 -4.09825087e-01 -4.50360864e-01 -6.48959756e-01
-5.84591031e-01 -7.65429199e-01 7.91601062e-01 4.10443217e-01
-2.38287821e-01 7.87986219e-01 2.53946751e-01 2.91175663e-01
-2.50159979e-01 -6.81959271e-01 -4.96737838e-01 -8.64508450e-01
2.52550662e-01 5.21604717e-01 4.00985181e-01 -4.10912573... | [10.41486930847168, 3.1458747386932373] |
641b0d30-5152-4a23-b070-e3786f66bb57 | real-time-selfie-video-stabilization | 2009.02007 | null | https://arxiv.org/abs/2009.02007v2 | https://arxiv.org/pdf/2009.02007v2.pdf | Real-Time Selfie Video Stabilization | We propose a novel real-time selfie video stabilization method. Our method is completely automatic and runs at 26 fps. We use a 1D linear convolutional network to directly infer the rigid moving least squares warping which implicitly balances between the global rigidity and local flexibility. Our network structure is s... | ['Jiyang Yu', 'Ravi Ramamoorthi', 'Ning Bi', 'Michel Sarkis', 'Keli Cheng'] | 2020-09-04 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Yu_Real-Time_Selfie_Video_Stabilization_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Yu_Real-Time_Selfie_Video_Stabilization_CVPR_2021_paper.pdf | cvpr-2021-1 | ['video-stabilization'] | ['computer-vision'] | [ 2.50330064e-02 -1.61718652e-02 -1.58722922e-01 -3.69134755e-03
-6.49103642e-01 -6.03355885e-01 3.51783425e-01 -4.33869869e-01
-3.19667161e-01 4.63715076e-01 3.46749485e-01 -5.49720675e-02
4.07030880e-01 -3.89321506e-01 -1.13571250e+00 -8.31498325e-01
-4.12094779e-02 1.22388206e-01 6.41909122e-01 -2.83390135... | [10.647167205810547, -1.3698042631149292] |
74ca1df6-1b40-4933-9f97-d82c9836c7df | text-to-image-diffusion-model-in-generative | 2303.07909 | null | https://arxiv.org/abs/2303.07909v2 | https://arxiv.org/pdf/2303.07909v2.pdf | Text-to-image Diffusion Models in Generative AI: A Survey | This survey reviews text-to-image diffusion models in the context that diffusion models have emerged to be popular for a wide range of generative tasks. As a self-contained work, this survey starts with a brief introduction of how a basic diffusion model works for image synthesis, followed by how condition or guidance ... | ['In So Kweon', 'Mengchun Zhang', 'Chaoning Zhang', 'Chenshuang Zhang'] | 2023-03-14 | null | null | null | null | ['text-guided-image-editing'] | ['computer-vision'] | [ 7.41123736e-01 2.84174919e-01 -1.68033212e-01 -1.71923339e-01
-4.72068787e-01 -4.22997177e-01 1.03128874e+00 -5.10749698e-01
-6.39166683e-02 5.72609782e-01 4.83616084e-01 -6.78531900e-02
2.69274833e-03 -7.51525760e-01 -5.19634128e-01 -8.29990625e-01
3.37633401e-01 3.85346830e-01 -1.58811435e-01 -2.38691494... | [11.33263874053955, -0.12963278591632843] |
5d2dacfd-f691-403d-a370-bb2d7a32b445 | modeling-multi-turn-conversation-with-deep | 1806.09102 | null | http://arxiv.org/abs/1806.09102v2 | http://arxiv.org/pdf/1806.09102v2.pdf | Modeling Multi-turn Conversation with Deep Utterance Aggregation | Multi-turn conversation understanding is a major challenge for building
intelligent dialogue systems. This work focuses on retrieval-based response
matching for multi-turn conversation whose related work simply concatenates the
conversation utterances, ignoring the interactions among previous utterances
for context mod... | ['Hai Zhao', 'Pengfei Zhu', 'Jiangtong Li', 'Zhuosheng Zhang', 'Gongshen Liu'] | 2018-06-24 | modeling-multi-turn-conversation-with-deep-2 | https://aclanthology.org/C18-1317 | https://aclanthology.org/C18-1317.pdf | coling-2018-8 | ['conversational-response-selection'] | ['natural-language-processing'] | [ 2.80405849e-01 3.22235048e-01 8.23438689e-02 -8.41379642e-01
-1.15930748e+00 -3.56247932e-01 8.62295389e-01 2.85051405e-01
-3.37828785e-01 7.65822947e-01 1.06697297e+00 -1.32066503e-01
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4.41933513e-01 7.41556406e-01 1.85803011e-01 -1.06680191... | [12.548691749572754, 7.823105812072754] |
5a88de7a-3fe2-4e27-bcb6-42003c108b25 | phrase-based-affordance-detection-via-cyclic | 2202.12076 | null | https://arxiv.org/abs/2202.12076v2 | https://arxiv.org/pdf/2202.12076v2.pdf | Phrase-Based Affordance Detection via Cyclic Bilateral Interaction | Affordance detection, which refers to perceiving objects with potential action possibilities in images, is a challenging task since the possible affordance depends on the person's purpose in real-world application scenarios. The existing works mainly extract the inherent human-object dependencies from image/video to ac... | ['Yang Cao', 'Yu Kang', 'Hongchen Luo', 'Wei Zhai', 'Liangsheng Lu'] | 2022-02-24 | null | null | null | null | ['affordance-detection'] | ['computer-vision'] | [ 1.14364386e-01 -3.99783671e-01 8.66082162e-02 -3.83368999e-01
-3.21071185e-02 -4.67229456e-01 6.01283550e-01 -2.25777954e-01
-4.81543988e-01 2.20503554e-01 4.83573258e-01 -2.38918699e-02
-1.10834472e-01 -3.66626799e-01 -6.33171380e-01 -5.96306264e-01
1.65593103e-01 -2.27183044e-01 3.46528590e-01 -3.76081496... | [5.1610894203186035, -0.09357694536447525] |
4c81f072-0653-4d55-a671-128558ef9247 | dasee-a-synthetic-database-of-domestic | 2104.13423 | null | https://arxiv.org/abs/2104.13423v2 | https://arxiv.org/pdf/2104.13423v2.pdf | DASEE A Synthetic Database of Domestic Acoustic Scenes and Events in Dementia Patients Environment | Access to informative databases is a crucial part of notable research developments. In the field of domestic audio classification, there have been significant advances in recent years. Although several audio databases exist, these can be limited in terms of the amount of information they provide, such as the exact loca... | ['Nidhal Abdulaziz', 'Stefano Fasciani', 'Christian Ritz', 'Abigail Copiaco'] | 2021-04-27 | null | null | null | null | ['room-impulse-response'] | ['audio'] | [ 2.37553939e-01 -3.02015275e-01 6.08141184e-01 -3.21011186e-01
-1.11393690e+00 -2.59342670e-01 3.34586501e-02 3.64970356e-01
-3.51007432e-01 8.44859660e-01 8.13418865e-01 1.95958346e-01
-2.76062071e-01 -6.33519411e-01 -4.10987198e-01 -6.22110009e-01
-3.60410929e-01 -5.56772575e-02 -2.35712379e-01 -3.42261195... | [15.398595809936523, 5.649293899536133] |
a98c0a1e-989c-44de-8a60-9a128bbdca86 | facial-uv-map-completion-for-pose-invariant | 2011.00912 | null | https://arxiv.org/abs/2011.00912v1 | https://arxiv.org/pdf/2011.00912v1.pdf | Facial UV Map Completion for Pose-invariant Face Recognition: A Novel Adversarial Approach based on Coupled Attention Residual UNets | Pose-invariant face recognition refers to the problem of identifying or verifying a person by analyzing face images captured from different poses. This problem is challenging due to the large variation of pose, illumination and facial expression. A promising approach to deal with pose variation is to fulfill incomplete... | ['Sang Dinh', 'Dung Nguyen', 'Chung Tran', 'In Seop Na'] | 2020-11-02 | null | null | null | null | ['robust-face-recognition'] | ['computer-vision'] | [ 2.91763037e-01 -1.59200445e-01 3.54004860e-01 -8.37059438e-01
-8.53895187e-01 -4.96242911e-01 4.27101970e-01 -7.77532756e-01
5.35022058e-02 7.20591068e-01 2.57864948e-02 2.92186022e-01
2.24634573e-01 -7.82758355e-01 -9.67841566e-01 -8.24023724e-01
4.02615398e-01 3.78108621e-01 -3.54700953e-01 -3.35422866... | [13.015554428100586, 0.20658694207668304] |
f5297c8c-43c6-4b18-ac46-78210ef2e040 | efficient-3d-semantic-segmentation-with-1 | 2306.08045 | null | https://arxiv.org/abs/2306.08045v1 | https://arxiv.org/pdf/2306.08045v1.pdf | Efficient 3D Semantic Segmentation with Superpoint Transformer | We introduce a novel superpoint-based transformer architecture for efficient semantic segmentation of large-scale 3D scenes. Our method incorporates a fast algorithm to partition point clouds into a hierarchical superpoint structure, which makes our preprocessing 7 times times faster than existing superpoint-based appr... | ['Loic Landrieu', 'Hugo Raguet', 'Damien Robert'] | 2023-06-13 | efficient-3d-semantic-segmentation-with | http://arxiv.org/abs/2306.08045 | https://arxiv.org/pdf/2306.08045 | null | ['3d-semantic-segmentation'] | ['computer-vision'] | [-1.72555462e-01 -6.36636987e-02 -1.30149230e-01 -2.45542347e-01
-1.13096225e+00 -6.53241634e-01 4.54237163e-01 1.48753792e-01
-2.61175424e-01 1.71241343e-01 -2.89199829e-01 -5.42037427e-01
2.88426369e-01 -8.29758644e-01 -9.85612333e-01 -1.91031605e-01
-1.12930819e-01 8.86859119e-01 9.03465331e-01 -9.87436771... | [7.964073657989502, -3.416027307510376] |
b63bd43b-061b-48ad-8a05-49c3c3fd006a | hope-speech-detection-on-social-media | 2212.07424 | null | https://arxiv.org/abs/2212.07424v1 | https://arxiv.org/pdf/2212.07424v1.pdf | Hope Speech Detection on Social Media Platforms | Since personal computers became widely available in the consumer market, the amount of harmful content on the internet has significantly expanded. In simple terms, harmful content is anything online which causes a person distress or harm. It may include hate speech, violent content, threats, non-hope speech, etc. The o... | ['Shankar Biradar', 'Sunil Saumya', 'Shubham Sharma', 'Jagrut Nemade', 'Pasupuleti Chandana', 'Pranjal Aggarwal'] | 2022-11-14 | null | null | null | null | ['hate-speech-detection', 'hope-speech-detection'] | ['natural-language-processing', 'natural-language-processing'] | [-2.85784274e-01 8.53950009e-02 -3.13089550e-01 -2.08135352e-01
-6.15596831e-01 -5.03544331e-01 5.64362109e-01 3.90070230e-01
-2.75026232e-01 6.23262584e-01 8.00561845e-01 -3.46522570e-01
3.21953833e-01 -3.53500634e-01 9.47040245e-02 -4.37741250e-01
4.42685097e-01 -1.31636038e-01 6.60101697e-02 -4.45055872... | [8.902311325073242, 10.625885963439941] |
7235e0ca-4e34-473f-98c7-cf2a29eca85c | hdrfeat-a-feature-rich-network-for-high | 2211.04238 | null | https://arxiv.org/abs/2211.04238v1 | https://arxiv.org/pdf/2211.04238v1.pdf | HDRfeat: A Feature-Rich Network for High Dynamic Range Image Reconstruction | A major challenge for high dynamic range (HDR) image reconstruction from multi-exposed low dynamic range (LDR) images, especially with dynamic scenes, is the extraction and merging of relevant contextual features in order to suppress any ghosting and blurring artifacts from moving objects. To tackle this, in this work ... | ['Orcun Göksel', 'Bozhi Liu', 'Fei Zhou', 'Lingkai Zhu'] | 2022-11-08 | null | null | null | null | ['hdr-reconstruction'] | ['computer-vision'] | [ 4.97951031e-01 -1.33161455e-01 1.84309587e-01 -3.64739239e-01
-8.43688309e-01 -1.03841625e-01 6.94801927e-01 -4.64056432e-01
-3.86481196e-01 6.52421057e-01 7.40975201e-01 1.36745319e-01
-2.44748414e-01 -5.14510274e-01 -6.64038599e-01 -8.22257638e-01
-2.26778060e-01 -9.25222635e-02 9.59548354e-02 -4.02745456... | [10.85548210144043, -2.229591131210327] |
1359cb7b-b309-4063-a663-c7595966c742 | deep-reinforcement-learning-applied-to-an | 2304.06567 | null | https://arxiv.org/abs/2304.06567v1 | https://arxiv.org/pdf/2304.06567v1.pdf | Deep reinforcement learning applied to an assembly sequence planning problem with user preferences | Deep reinforcement learning (DRL) has demonstrated its potential in solving complex manufacturing decision-making problems, especially in a context where the system learns over time with actual operation in the absence of training data. One interesting and challenging application for such methods is the assembly sequen... | ['Pedro Neto', 'Miguel Neves'] | 2023-04-13 | null | null | null | null | ['q-learning'] | ['methodology'] | [-1.10181101e-01 3.58282208e-01 -1.33127809e-01 1.32358801e-02
-5.56483388e-01 -5.11044502e-01 2.61125416e-01 2.58649558e-01
-5.30617535e-01 1.10573494e+00 -1.85197338e-01 -3.23599726e-01
-7.40274727e-01 -7.65033782e-01 -8.11965704e-01 -8.40542793e-01
-3.60498667e-01 1.03568661e+00 -1.81678548e-01 -6.82719350... | [4.48565673828125, 2.082456111907959] |
1703a8da-6b76-4a40-a9bc-e23dca4d1a40 | disjoint-cnn-for-multivariate-time-series | null | null | https://ieeexplore.ieee.org/abstract/document/9679860 | https://ieeexplore.ieee.org/abstract/document/9679860 | Disjoint-CNN for Multivariate Time Series Classification | Time series classification algorithms have been mainly dominated by non-deep learning models.
Deep learning for Multivariate Time Series Classification (MTSC) has gained huge interest in recent years.
Most state-of-the-art deep learning methods are convolutional-based where 1-dimensional (1D) convolutions are used t... | ['Mahsa Salehi', 'Chang Wei Tan', 'Navid Mohammadi Foumani'] | 2022-01-20 | null | null | null | 2021-international-conference-on-data-mining | ['time-series-classification'] | ['time-series'] | [-3.13742012e-01 -8.44289839e-01 -9.52762216e-02 -2.14818686e-01
-4.38750833e-01 -4.58952546e-01 5.53208709e-01 9.56406519e-02
-7.55148172e-01 3.83199662e-01 9.29254964e-02 -6.58141077e-01
-4.63127047e-01 -6.22249186e-01 -8.48346770e-01 -5.79349756e-01
-9.89772797e-01 -1.09379806e-01 5.93067780e-02 -3.85736078... | [7.027994155883789, 2.893315076828003] |
a4fce5a4-6311-40bb-a706-61088236e1e1 | learning-from-multi-perception-features-for | 2305.18547 | null | https://arxiv.org/abs/2305.18547v1 | https://arxiv.org/pdf/2305.18547v1.pdf | Learning from Multi-Perception Features for Real-Word Image Super-resolution | Currently, there are two popular approaches for addressing real-world image super-resolution problems: degradation-estimation-based and blind-based methods. However, degradation-estimation-based methods may be inaccurate in estimating the degradation, making them less applicable to real-world LR images. On the other ha... | ['Yanning Zhang', 'In So Kweon', 'Jinqiu Sun', 'Pei Wang', 'Trung X. Pham', 'Kang Zhang', 'Axi Niu'] | 2023-05-26 | null | null | null | null | ['image-super-resolution'] | ['computer-vision'] | [ 4.69159722e-01 -4.00310606e-01 -2.92126179e-01 -2.44907647e-01
-1.15802121e+00 -1.98876530e-01 2.32964367e-01 -2.68416375e-01
-4.90249544e-02 7.69923210e-01 4.01022226e-01 9.34882984e-02
5.53378314e-02 -5.41666210e-01 -4.89530474e-01 -7.40506828e-01
2.82660961e-01 -4.36236769e-01 3.50487858e-01 -3.05908591... | [11.207847595214844, -2.122040271759033] |
dec3869a-f1ab-4039-8dc5-eaafb628c9c0 | hybrid-lemmatization-in-huspacy | 2306.07636 | null | https://arxiv.org/abs/2306.07636v1 | https://arxiv.org/pdf/2306.07636v1.pdf | Hybrid lemmatization in HuSpaCy | Lemmatization is still not a trivial task for morphologically rich languages. Previous studies showed that hybrid architectures usually work better for these languages and can yield great results. This paper presents a hybrid lemmatizer utilizing both a neural model, dictionaries and hand-crafted rules. We introduce a ... | ['Richárd Farkas', 'Gergő Szabó', 'Zsolt Szántó', 'György Orosz', 'Péter Berkecz'] | 2023-06-13 | null | null | null | null | ['lemmatization'] | ['natural-language-processing'] | [-4.60708529e-01 6.93929121e-02 -3.39272380e-01 -4.83662277e-01
-7.53137648e-01 -6.95786834e-01 5.60117006e-01 2.65140980e-02
-1.03007329e+00 7.56878257e-01 3.28404009e-01 -6.82600617e-01
3.21780980e-01 -9.41035509e-01 -5.98465025e-01 -3.78842920e-01
2.49478787e-01 8.48272443e-01 -1.34328738e-01 -5.97376585... | [10.458258628845215, 10.053814888000488] |
f20b1d89-f5e1-47c3-a736-a8059eff369e | reducing-label-noise-in-anchor-free-object | 2008.01167 | null | https://arxiv.org/abs/2008.01167v2 | https://arxiv.org/pdf/2008.01167v2.pdf | Reducing Label Noise in Anchor-Free Object Detection | Current anchor-free object detectors label all the features that spatially fall inside a predefined central region of a ground-truth box as positive. This approach causes label noise during training, since some of these positively labeled features may be on the background or an occluder object, or they are simply not d... | ['Samet Hicsonmez', 'Nermin Samet', 'Emre Akbas'] | 2020-08-03 | reducing-label-noise-in-anchor-free-object-1 | null | null | bmvc-2020-8 | ['small-object-detection'] | ['computer-vision'] | [ 1.10477418e-01 1.10658512e-01 -3.74661922e-01 -4.38464403e-01
-1.10620904e+00 -5.51841080e-01 4.08642709e-01 2.67420650e-01
-5.95280886e-01 5.64188540e-01 -1.48897514e-01 2.93164980e-02
3.42705697e-01 -6.65923059e-01 -7.97355115e-01 -6.84748888e-01
3.76540683e-02 3.22499245e-01 1.12684464e+00 3.07904929... | [9.157746315002441, 1.171528697013855] |
caf88221-b425-4396-8bc8-cec10eaf3a0d | iris-interpretable-rubric-informed | 2303.09097 | null | https://arxiv.org/abs/2303.09097v1 | https://arxiv.org/pdf/2303.09097v1.pdf | IRIS: Interpretable Rubric-Informed Segmentation for Action Quality Assessment | AI-driven Action Quality Assessment (AQA) of sports videos can mimic Olympic judges to help score performances as a second opinion or for training. However, these AI methods are uninterpretable and do not justify their scores, which is important for algorithmic accountability. Indeed, to account for their decisions, in... | ['Brian Y. Lim', 'Nobuo Kawaguchi', 'Hitoshi Matsuyama'] | 2023-03-16 | null | null | null | null | ['action-quality-assessment'] | ['computer-vision'] | [ 2.64389545e-01 2.58438975e-01 -3.44935596e-01 -7.04717636e-01
-9.44330931e-01 -9.11806345e-01 1.77476808e-01 -7.30294734e-02
-3.04978549e-01 3.66665035e-01 5.44496119e-01 -3.80601078e-01
-1.23865411e-01 -3.02081198e-01 -7.47583449e-01 -8.77799690e-02
4.76486653e-01 4.70194876e-01 2.36620873e-01 -1.73703387... | [7.9109954833984375, 0.5385114550590515] |
f9ba7c8c-c6bb-4b16-bbf9-d7fa4ab89e52 | designing-explainable-predictive-machine | 2306.11771 | null | https://arxiv.org/abs/2306.11771v1 | https://arxiv.org/pdf/2306.11771v1.pdf | Designing Explainable Predictive Machine Learning Artifacts: Methodology and Practical Demonstration | Prediction-oriented machine learning is becoming increasingly valuable to organizations, as it may drive applications in crucial business areas. However, decision-makers from companies across various industries are still largely reluctant to employ applications based on modern machine learning algorithms. We ascribe th... | ['Peter Kowalczyk', 'Giacomo Welsch'] | 2023-06-20 | null | null | null | null | ['explainable-artificial-intelligence'] | ['computer-vision'] | [ 1.01194613e-01 5.46507061e-01 -6.50555551e-01 -3.21512133e-01
-2.52104457e-02 -2.97822982e-01 5.92545033e-01 1.47450298e-01
1.02568269e-01 2.54214436e-01 7.67296255e-02 -1.17421973e+00
-5.80038965e-01 -7.06201434e-01 -5.85689962e-01 -1.55430034e-01
4.41560745e-01 2.06816956e-01 -4.33520228e-01 -2.38828674... | [8.793246269226074, 6.022544860839844] |
640c11a3-648f-4508-955c-34900205058f | visual-transformers-for-primates | 2212.10093 | null | https://arxiv.org/abs/2212.10093v1 | https://arxiv.org/pdf/2212.10093v1.pdf | Visual Transformers for Primates Classification and Covid Detection | We apply the vision transformer, a deep machine learning model build around the attention mechanism, on mel-spectrogram representations of raw audio recordings. When adding mel-based data augmentation techniques and sample-weighting, we achieve comparable performance on both (PRS and CCS challenge) tasks of ComParE21, ... | ['Claudia-Linnhoff Popien', 'Andreas Sedlmeier', 'Robert Müller', 'Steffen Illium'] | 2022-12-20 | null | null | null | null | ['audio-classification'] | ['audio'] | [ 3.76632124e-01 -1.74453080e-01 7.11436048e-02 -1.69222459e-01
-1.41605186e+00 -6.23669088e-01 5.74405134e-01 2.83930123e-01
-3.49008441e-01 2.24352777e-01 9.38705385e-01 -2.56291926e-01
2.40167633e-01 -4.83007636e-03 -5.02075493e-01 -2.40814835e-01
-3.56073529e-01 1.91544831e-01 -1.46013632e-01 -1.64717168... | [15.306347846984863, 5.1941237449646] |
8b7f5159-deff-494b-9452-ac53c376d5c3 | a-structurally-regularized-cnn-architecture | 2306.16604 | null | https://arxiv.org/abs/2306.16604v1 | https://arxiv.org/pdf/2306.16604v1.pdf | A Structurally Regularized CNN Architecture via Adaptive Subband Decomposition | We propose a generalized convolutional neural network (CNN) architecture that first decomposes the input signal into subbands by an adaptive filter bank structure, and then uses convolutional layers to extract features from each subband independently. Fully connected layers finally combine the extracted features to per... | ['Zeljko Zilic', 'Ioannis Psaromiligkos', 'Pavel Sinha'] | 2023-06-29 | null | null | null | null | ['quantization'] | ['methodology'] | [ 1.27491996e-01 3.34161520e-02 -2.52732009e-01 -5.72892368e-01
-6.75079823e-01 -9.85168442e-02 1.27120361e-01 -2.35803291e-01
-7.99111784e-01 5.67069232e-01 -1.72283903e-01 -2.36581251e-01
-1.47282004e-01 -8.61737132e-01 -9.19124663e-01 -6.50400698e-01
-1.48087978e-01 -4.20459181e-01 7.23464713e-02 -5.97898886... | [9.091662406921387, 2.2507131099700928] |
f9597019-31c2-4b60-8297-47e5eb95490f | temporal-graph-benchmark-for-machine-learning | 2307.01026 | null | https://arxiv.org/abs/2307.01026v1 | https://arxiv.org/pdf/2307.01026v1.pdf | Temporal Graph Benchmark for Machine Learning on Temporal Graphs | We present the Temporal Graph Benchmark (TGB), a collection of challenging and diverse benchmark datasets for realistic, reproducible, and robust evaluation of machine learning models on temporal graphs. TGB datasets are of large scale, spanning years in duration, incorporate both node and edge-level prediction tasks a... | ['Reihaneh Rabbany', 'Guillaume Rabusseau', 'Michael Bronstein', 'Jure Leskovec', 'Emanuele Rossi', 'Weihua Hu', 'Matthias Fey', 'Jacob Danovitch', 'Farimah Poursafaei', 'Shenyang Huang'] | 2023-07-03 | null | null | null | null | ['property-prediction'] | ['medical'] | [-2.04321533e-01 -1.23594262e-01 -7.13878989e-01 -3.02607596e-01
-4.03478682e-01 -8.68468761e-01 7.51157522e-01 6.41229510e-01
4.16073613e-02 7.47253239e-01 2.39318199e-02 -5.94068646e-01
-5.12075007e-01 -9.95272934e-01 -5.93348682e-01 -3.39788020e-01
-9.63316560e-01 7.45432258e-01 6.10820591e-01 -2.50716776... | [7.039707660675049, 6.112297534942627] |
d0df6b48-ec7d-414b-a742-d7281a88872d | moving-tiger-beyond-sentence-level | null | null | https://aclanthology.org/L18-1348 | https://aclanthology.org/L18-1348.pdf | Moving TIGER beyond Sentence-Level | null | ['Jonas Kuhn', 'Agnieszka Falenska', 'Kerstin Eckart'] | 2018-05-01 | moving-tiger-beyond-sentence-level-1 | https://aclanthology.org/L18-1348 | https://aclanthology.org/L18-1348.pdf | lrec-2018-5 | ['morphological-tagging'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.38075590133667, 3.652378559112549] |
c58cbac1-1b8b-4a3f-8a04-d06e07232594 | an-unsupervised-neural-attention-model-for | null | null | https://aclanthology.org/P17-1036 | https://aclanthology.org/P17-1036.pdf | An Unsupervised Neural Attention Model for Aspect Extraction | Aspect extraction is an important and challenging task in aspect-based sentiment analysis. Existing works tend to apply variants of topic models on this task. While fairly successful, these methods usually do not produce highly coherent aspects. In this paper, we present a novel neural approach with the aim of discover... | ['Wee Sun Lee', 'Hwee Tou Ng', 'Daniel Dahlmeier', 'Ruidan He'] | 2017-07-01 | null | null | null | acl-2017-7 | ['aspect-extraction'] | ['natural-language-processing'] | [-4.02038544e-02 1.38820812e-01 -5.91607928e-01 -5.24408877e-01
-6.57591522e-01 -2.96911955e-01 9.52987671e-01 4.11702573e-01
-4.59611803e-01 5.30017674e-01 9.70591068e-01 6.92432281e-03
1.97415985e-02 -8.78252864e-01 -4.17539328e-01 -6.17348909e-01
8.11030492e-02 3.17777574e-01 -8.37711915e-02 -2.91471303... | [11.339153289794922, 6.706550598144531] |
eb3201bb-dbe2-4911-a790-e51cd5006fc4 | tensor-based-intrinsic-subspace | 2010.09193 | null | https://arxiv.org/abs/2010.09193v7 | https://arxiv.org/pdf/2010.09193v7.pdf | Tensor-based Intrinsic Subspace Representation Learning for Multi-view Clustering | As a hot research topic, many multi-view clustering approaches are proposed over the past few years. Nevertheless, most existing algorithms merely take the consensus information among different views into consideration for clustering. Actually, it may hinder the multi-view clustering performance in real-life applicatio... | ['Shuangxun Ma', 'Haoyu Tang', 'Jihua Zhu', 'Yu Zhang', 'Zhongyu Li', 'Qinghai Zheng'] | 2020-10-19 | null | null | null | null | ['multi-view-subspace-clustering'] | ['computer-vision'] | [-3.89356583e-01 -6.84162080e-01 -1.58732384e-01 -1.23474963e-01
-5.42704225e-01 -4.79869664e-01 2.50328809e-01 -3.80403221e-01
-7.76024982e-02 2.64998138e-01 5.55956542e-01 3.06431085e-01
-6.10574424e-01 -3.34631771e-01 -2.75454503e-02 -1.22317159e+00
3.38328302e-01 1.76962242e-01 2.41813064e-02 -1.01733789... | [8.261122703552246, 4.62471866607666] |
6eaae324-af63-4c3e-8a5f-620eace56f55 | powerbev-a-powerful-yet-lightweight-framework | 2306.10761 | null | https://arxiv.org/abs/2306.10761v1 | https://arxiv.org/pdf/2306.10761v1.pdf | PowerBEV: A Powerful Yet Lightweight Framework for Instance Prediction in Bird's-Eye View | Accurately perceiving instances and predicting their future motion are key tasks for autonomous vehicles, enabling them to navigate safely in complex urban traffic. While bird's-eye view (BEV) representations are commonplace in perception for autonomous driving, their potential in a motion prediction setting is less ex... | ['Juergen Gall', 'Marius Cordts', 'Niklas Hanselmann', 'Xieyuanli Chen', 'Shuxiao Ding', 'Peizheng Li'] | 2023-06-19 | null | null | null | null | ['motion-prediction', 'autonomous-vehicles', 'navigate'] | ['computer-vision', 'computer-vision', 'reasoning'] | [-3.39494571e-02 -2.32546777e-01 -3.90228122e-01 -5.22422433e-01
-4.37767893e-01 -5.78014374e-01 1.04875219e+00 -1.28681153e-01
-5.91107547e-01 4.34134305e-01 3.69515717e-01 -5.02744615e-01
-5.47603741e-02 -6.31286442e-01 -7.30642796e-01 -4.41300869e-01
8.05017203e-02 2.20926598e-01 5.11105418e-01 -4.26359087... | [6.394125461578369, 0.563029944896698] |
2929ac99-30e3-4f54-afaa-5c17cc876ec9 | slot-dependency-modeling-for-zero-shot-cross | null | null | https://aclanthology.org/2022.coling-1.42 | https://aclanthology.org/2022.coling-1.42.pdf | Slot Dependency Modeling for Zero-Shot Cross-Domain Dialogue State Tracking | Zero-shot learning for Dialogue State Tracking (DST) focuses on generalizing to an unseen domain without the expense of collecting in domain data. However, previous zero-shot DST methods ignore the slot dependencies in a multidomain dialogue, resulting in sub-optimal performances when adapting to unseen domains. In thi... | ['Li Guo', 'Zheng Lin', 'Yanhe Fu', 'Piji Li', 'Yanan Cao', 'Qingyue Wang'] | null | null | null | null | coling-2022-10 | ['dialogue-state-tracking'] | ['natural-language-processing'] | [ 6.15943968e-02 3.32920820e-01 -3.67161602e-01 -4.67024624e-01
-8.32825005e-01 -4.10557359e-01 8.45471799e-01 3.09619179e-04
-3.47519249e-01 1.24373066e+00 5.23525238e-01 4.42033820e-02
6.66283071e-02 -5.94303966e-01 4.52329312e-03 -3.30582649e-01
2.54365027e-01 9.84470606e-01 7.25159943e-01 -7.92733669... | [12.816496849060059, 7.839766502380371] |
d18b3901-02d1-46a4-811f-fa220736c5ac | attentive-state-space-modeling-of-disease | null | null | http://papers.nips.cc/paper/9311-attentive-state-space-modeling-of-disease-progression | http://papers.nips.cc/paper/9311-attentive-state-space-modeling-of-disease-progression.pdf | Attentive State-Space Modeling of Disease Progression | Models of disease progression are instrumental for predicting patient outcomes and understanding disease dynamics. Existing models provide the patient with pragmatic (supervised) predictions of risk, but do not provide the clinician with intelligible (unsupervised) representations of disease pathophysiology. In this pa... | ['Mihaela van der Schaar', 'Ahmed M. Alaa'] | 2019-12-01 | null | null | null | neurips-2019-12 | ['predicting-patient-outcomes'] | ['medical'] | [ 1.79317981e-01 4.28031653e-01 -4.49922174e-01 -6.15246892e-01
-6.64454520e-01 -4.84125912e-02 5.55551350e-01 3.78889799e-01
-1.46686256e-01 5.69636941e-01 1.06079435e+00 -6.01240039e-01
-6.87675774e-01 -5.33747554e-01 -2.98158824e-01 -5.94410717e-01
-6.40562892e-01 1.12268674e+00 -4.60292190e-01 1.60506085... | [7.859282493591309, 5.851579189300537] |
fa250790-7ade-4153-a831-5364cc807a68 | recognition-of-basic-hand-movements-using | 1810.10062 | null | http://arxiv.org/abs/1810.10062v1 | http://arxiv.org/pdf/1810.10062v1.pdf | Recognition of basic hand movements using Electromyography | The aim of this work was to identify six basic movements of the hand using
two systems. Being an interdisciplinary topic, there has been conducted
studying in the anatomy of forearm muscles, biosignals, the method of
electromyography (EMG) and methods of pattern recognition. Moreover, the signal
contained enough noise ... | [] | 2018-10-23 | null | null | null | null | ['electromyography-emg'] | ['medical'] | [ 1.97464570e-01 1.38661236e-01 1.46556526e-01 2.12569848e-01
-1.39395386e-01 -2.65294045e-01 2.76631713e-01 -6.56791449e-01
-6.43481731e-01 6.56284392e-01 8.08317494e-03 6.19470328e-02
-5.21855652e-01 -3.11597407e-01 -1.49596170e-01 -6.83225632e-01
-2.17499301e-01 3.60579401e-01 4.29736450e-02 -1.48455516... | [6.866512298583984, 0.20303912460803986] |
547e8c97-a538-47fc-9ace-111b84a417cb | detecting-propaganda-techniques-in-code | 2305.14534 | null | https://arxiv.org/abs/2305.14534v1 | https://arxiv.org/pdf/2305.14534v1.pdf | Detecting Propaganda Techniques in Code-Switched Social Media Text | Propaganda is a form of communication intended to influence the opinions and the mindset of the public to promote a particular agenda. With the rise of social media, propaganda has spread rapidly, leading to the need for automatic propaganda detection systems. Most work on propaganda detection has focused on high-resou... | ['Preslav Nakov', 'Shady Shehata', 'Asif Hanif', 'Muhammad Umar Salman'] | 2023-05-23 | null | null | null | null | ['propaganda-detection'] | ['natural-language-processing'] | [-1.32642195e-01 -2.00924277e-01 -3.05543542e-01 3.28589864e-02
-5.95314503e-01 -8.72331083e-01 1.00699914e+00 4.04844642e-01
-2.41158992e-01 4.61927593e-01 6.21774018e-01 -7.75101900e-01
5.49222887e-01 -6.73215628e-01 -3.89385909e-01 -5.11690378e-01
7.07910508e-02 1.12707233e-02 1.60788164e-01 -4.00349468... | [8.875791549682617, 10.499165534973145] |
cd6bb9b7-6368-45c5-a685-e8550e3236a4 | stock-price-prediction-under-anomalous | 2109.15059 | null | https://arxiv.org/abs/2109.15059v1 | https://arxiv.org/pdf/2109.15059v1.pdf | Stock Price Prediction Under Anomalous Circumstances | The stock market is volatile and complicated, especially in 2020. Because of a series of global and regional "black swans," such as the COVID-19 pandemic, the U.S. stock market triggered the circuit breaker three times within one week of March 9 to 16, which is unprecedented throughout history. Affected by the whole ci... | ['Jiebo Luo', 'Wei Wu', 'Jinlong Ruan'] | 2021-09-14 | null | null | null | null | ['stock-price-prediction'] | ['time-series'] | [-5.92525482e-01 -2.39796743e-01 -1.91035375e-01 5.12591004e-02
-2.76154667e-01 -7.61340797e-01 7.28480101e-01 1.51056617e-01
-9.99722928e-02 9.68895316e-01 2.47988433e-01 -6.95625484e-01
9.16534960e-02 -9.32694197e-01 -6.64783657e-01 -4.22546446e-01
-5.06218635e-02 9.62181091e-02 -2.46666395e-03 -4.45123941... | [4.533226013183594, 4.194178104400635] |
6b38d272-b123-4bbc-ad38-c6a70d31b757 | umsiforeseer-at-semeval-2020-task-11 | null | null | https://aclanthology.org/2020.semeval-1.242 | https://aclanthology.org/2020.semeval-1.242.pdf | UMSIForeseer at SemEval-2020 Task 11: Propaganda Detection by Fine-Tuning BERT with Resampling and Ensemble Learning | We describe our participation at the SemEval 2020 {``}Detection of Propaganda Techniques in News Articles{''} - Techniques Classification (TC) task, designed to categorize textual fragments into one of the 14 given propaganda techniques. Our solution leverages pre-trained BERT models. We present our model implementatio... | ['Qiaozhu Mei', 'Cristina Garbacea', 'Yunzhe Jiang'] | 2020-12-01 | null | null | null | semeval-2020 | ['propaganda-detection'] | ['natural-language-processing'] | [ 7.30690360e-02 4.67748120e-02 -7.55194843e-01 -4.10268456e-01
-1.18244827e+00 -6.58922076e-01 1.41847920e+00 6.79233193e-01
-4.81561661e-01 4.55918938e-01 1.08453619e+00 -9.41044092e-01
-4.24856786e-03 -6.61187470e-01 -4.62137669e-01 -2.34048545e-01
-1.02594711e-01 4.40390408e-01 -7.83727169e-02 -3.31473261... | [8.473834037780762, 10.669557571411133] |
6bc9383e-1d33-4a9f-94d2-6d06c4a4fd4e | muboost-an-effective-method-for-solving-indic | 2206.10280 | null | https://arxiv.org/abs/2206.10280v1 | https://arxiv.org/pdf/2206.10280v1.pdf | muBoost: An Effective Method for Solving Indic Multilingual Text Classification Problem | Text Classification is an integral part of many Natural Language Processing tasks such as sarcasm detection, sentiment analysis and many more such applications. Many e-commerce websites, social-media/entertainment platforms use such models to enhance user-experience to generate traffic and thus, revenue on their platfo... | ['Aditya Jain', 'Manish Pathak'] | 2022-06-21 | null | null | null | null | ['multilingual-text-classification'] | ['miscellaneous'] | [-4.43818808e-01 -2.79006660e-01 -3.35740894e-01 -2.15656832e-01
-1.07339692e+00 -6.46938026e-01 6.52294397e-01 1.43011734e-01
-3.49583894e-01 7.04038262e-01 4.66066897e-01 -3.79671305e-01
4.07415152e-01 -3.02888393e-01 -3.05430055e-01 -1.83720723e-01
2.35900298e-01 2.26926923e-01 1.39767453e-01 -7.72581816... | [8.975110054016113, 10.589537620544434] |
8d01266a-286f-403e-aebf-1772bb74dc27 | data-valuation-for-medical-imaging-using | 2010.08006 | null | https://arxiv.org/abs/2010.08006v1 | https://arxiv.org/pdf/2010.08006v1.pdf | Data Valuation for Medical Imaging Using Shapley Value: Application on A Large-scale Chest X-ray Dataset | The reliability of machine learning models can be compromised when trained on low quality data. Many large-scale medical imaging datasets contain low quality labels extracted from sources such as medical reports. Moreover, images within a dataset may have heterogeneous quality due to artifacts and biases arising from e... | ['Daniel L. Rubin', 'James Zou', 'Jared A. Dunnmon', 'Sameer Rehman', 'Rikiya Yamashita', 'Amirata Ghorbani', 'Siyi Tang'] | 2020-10-15 | null | null | null | null | ['pneumonia-detection'] | ['medical'] | [ 3.21569383e-01 -1.13156168e-02 -3.90916198e-01 -5.84223807e-01
-1.34322083e+00 -4.39953774e-01 8.45914856e-02 5.78201711e-01
-5.55568278e-01 7.38415718e-01 4.94134873e-01 -1.07749991e-01
-3.96077931e-01 -1.02045202e+00 -6.62114978e-01 -7.91250944e-01
2.53494143e-01 2.89452463e-01 -6.75698519e-02 6.32094383... | [15.001977920532227, -2.072826385498047] |
d82a626d-33b8-4753-bc71-36c4b2bc62af | end-to-end-nilm-system-using-high-frequency | 2004.13905 | null | http://arxiv.org/abs/2004.13905v1 | http://arxiv.org/pdf/2004.13905v1.pdf | End-to-end NILM System Using High Frequency Data and Neural Networks | Improving energy efficiency is a necessity in the fight against climate
change. Non Intrusive Load Monitoring (NILM) systems give important information
about the household consumption that can be used by the electric utility or the
end users. In this work the implementation of an end-to-end NILM system is
presented, wh... | [] | 2020-04-29 | null | null | null | null | ['non-intrusive-load-monitoring', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring'] | ['knowledge-base', 'miscellaneous', 'time-series'] | [ 6.44071773e-02 -9.75869596e-02 -2.36082748e-01 -7.45772898e-01
-3.08160305e-01 -2.82918632e-01 4.55919951e-01 9.92835909e-02
-1.82074860e-01 8.68600965e-01 7.43791983e-02 -2.00047508e-01
-3.77964437e-01 -1.13790214e+00 -6.04714826e-02 -9.86410797e-01
-2.77580112e-01 4.96264428e-01 -3.41571510e-01 -1.23161100... | [6.014370918273926, 2.588174819946289] |
4ed5a436-31f8-4e89-9fe0-8b8be508abf5 | generative-models-for-multi-illumination | 2109.00863 | null | https://arxiv.org/abs/2109.00863v1 | https://arxiv.org/pdf/2109.00863v1.pdf | Generative Models for Multi-Illumination Color Constancy | In this paper, the aim is multi-illumination color constancy. However, most of the existing color constancy methods are designed for single light sources. Furthermore, datasets for learning multiple illumination color constancy are largely missing. We propose a seed (physics driven) based multi-illumination color const... | ['Theo Gevers', 'Sezer Karaoglu', 'Yang Liu', 'Partha Das'] | 2021-09-02 | null | null | null | null | ['color-constancy'] | ['computer-vision'] | [ 2.96511114e-01 -5.96190333e-01 -9.49057266e-02 -4.15813923e-01
-7.28828907e-01 -5.80278337e-01 5.48139513e-01 -6.02673352e-01
-2.02750266e-01 8.94812405e-01 -3.23508590e-01 -2.19863746e-02
3.63649994e-01 -4.57063168e-01 -8.68090928e-01 -9.73503947e-01
8.96282077e-01 -1.59243010e-02 7.65111372e-02 -2.09651247... | [10.468344688415527, -2.505490303039551] |
dbf17254-95b2-481d-8e12-80858e97a147 | knowledge-prompting-for-few-shot-action | 2211.12030 | null | https://arxiv.org/abs/2211.12030v1 | https://arxiv.org/pdf/2211.12030v1.pdf | Knowledge Prompting for Few-shot Action Recognition | Few-shot action recognition in videos is challenging for its lack of supervision and difficulty in generalizing to unseen actions. To address this task, we propose a simple yet effective method, called knowledge prompting, which leverages commonsense knowledge of actions from external resources to prompt a powerful pre... | ['Hanxi Lin', 'Xinxiao wu', 'Yuheng Shi'] | 2022-11-22 | null | null | null | null | ['few-shot-action-recognition', 'action-recognition-in-videos-2'] | ['computer-vision', 'computer-vision'] | [ 3.78264040e-01 -1.24920145e-01 -7.10265040e-01 -5.41145802e-01
-8.55578601e-01 -2.60664880e-01 6.45686865e-01 -2.32931837e-01
-4.92452413e-01 4.06208068e-01 6.86763167e-01 -3.31732258e-03
4.28374946e-01 -3.92830491e-01 -8.71718168e-01 -5.42252600e-01
2.72055298e-01 -5.86546026e-04 7.40208805e-01 -3.17567140... | [8.589613914489746, 0.7278271913528442] |
405a5cdb-938a-495b-94aa-9a44d933c71f | learning-a-structural-causal-model-for | 2305.17727 | null | https://arxiv.org/abs/2305.17727v1 | https://arxiv.org/pdf/2305.17727v1.pdf | Learning a Structural Causal Model for Intuition Reasoning in Conversation | Reasoning, a crucial aspect of NLP research, has not been adequately addressed by prevailing models including Large Language Model. Conversation reasoning, as a critical component of it, remains largely unexplored due to the absence of a well-designed cognitive model. In this paper, inspired by intuition theory on conv... | ['Xinyu Yang', 'Wenjing Zhu', 'Jing Luo', 'Bingyu Liao', 'Hang Chen'] | 2023-05-28 | null | null | null | null | ['causal-discovery'] | ['knowledge-base'] | [ 1.01118580e-01 8.13576937e-01 -5.03195226e-01 -3.69338840e-01
-4.10736471e-01 -5.44665277e-01 9.56989229e-01 6.28902987e-02
1.07514858e-01 9.32559967e-01 1.22653568e+00 -6.49505496e-01
-5.78556716e-01 -8.62339556e-01 -7.22402155e-01 -4.94326442e-01
1.05421580e-01 5.07684350e-01 -7.04938620e-02 -3.88157554... | [8.185128211975098, 5.563663959503174] |
04ebb097-fe4c-4cdb-9542-7de2636a25be | small-footprint-keyword-spotting-with-multi | 2010.09960 | null | https://arxiv.org/abs/2010.09960v1 | https://arxiv.org/pdf/2010.09960v1.pdf | Small-Footprint Keyword Spotting with Multi-Scale Temporal Convolution | Keyword Spotting (KWS) plays a vital role in human-computer interaction for smart on-device terminals and service robots. It remains challenging to achieve the trade-off between small footprint and high accuracy for KWS task. In this paper, we explore the application of multi-scale temporal modeling to the small-footpr... | ['Xiaowei Qin', 'Xiaodong Wei', 'Ximin Li'] | 2020-10-20 | null | null | null | null | ['small-footprint-keyword-spotting'] | ['speech'] | [-2.39474233e-02 -8.31799284e-02 -9.72261727e-02 -5.22553086e-01
-5.21070838e-01 -2.44176477e-01 3.12946290e-01 -4.50423330e-01
-7.59238243e-01 3.39789718e-01 -1.28582001e-01 -8.13519120e-01
5.34548797e-02 -4.30514753e-01 -6.70844257e-01 -6.89171016e-01
7.58285373e-02 5.50830783e-03 5.91719925e-01 -2.79583260... | [14.277644157409668, 6.381799697875977] |
f2edf70a-0521-4c35-8574-944c9172c786 | scene-understanding-networks-for-autonomous | 1805.07029 | null | http://arxiv.org/abs/1805.07029v1 | http://arxiv.org/pdf/1805.07029v1.pdf | Scene Understanding Networks for Autonomous Driving based on Around View Monitoring System | Modern driver assistance systems rely on a wide range of sensors (RADAR,
LIDAR, ultrasound and cameras) for scene understanding and prediction. These
sensors are typically used for detecting traffic participants and scene
elements required for navigation. In this paper we argue that relying on camera
based systems, spe... | ['Andrei Leica', 'Andrei Petreanu', 'Vlad Paunescu', 'Ioana Veronica Chelu', 'YunSung Soh', 'Alexandru Ghiuta', 'Livia Iordache', 'HyunJoo Ryu', 'ByeongMoon Jeon', 'JeongYeol Baek'] | 2018-05-18 | null | null | null | null | ['drivable-area-detection'] | ['computer-vision'] | [ 1.20623894e-01 1.54467717e-01 2.78611258e-02 -7.03336060e-01
-4.35434759e-01 -4.50174332e-01 6.43861532e-01 4.04672399e-02
-6.93320274e-01 3.21674109e-01 -2.38820076e-01 -5.96903205e-01
-2.67434537e-01 -8.13087106e-01 -2.87955672e-01 -1.69380307e-01
1.39048681e-01 4.72322732e-01 8.09377372e-01 -4.78878051... | [7.933638572692871, -1.209903597831726] |
fb860263-491f-4cdb-9586-a34ae133709c | learning-cross-context-entity-representations-1 | 2001.03765 | null | https://arxiv.org/abs/2001.03765v1 | https://arxiv.org/pdf/2001.03765v1.pdf | Learning Cross-Context Entity Representations from Text | Language modeling tasks, in which words, or word-pieces, are predicted on the basis of a local context, have been very effective for learning word embeddings and context dependent representations of phrases. Motivated by the observation that efforts to code world knowledge into machine readable knowledge bases or human... | ['David Weiss', 'Thibault Févry', 'Tom Kwiatkowski', 'Livio Baldini Soares', 'Nicholas FitzGerald', 'Jeffrey Ling', 'Zifei Shan'] | 2020-01-11 | null | https://openreview.net/forum?id=HygwvC4tPH | https://openreview.net/pdf?id=HygwvC4tPH | null | ['learning-word-embeddings'] | ['methodology'] | [-2.93940127e-01 1.62010968e-01 -6.69802189e-01 -2.76821386e-02
-8.27760577e-01 -8.44403565e-01 7.28555977e-01 8.69645596e-01
-8.58577430e-01 1.08191013e+00 6.05478466e-01 -3.98010939e-01
-9.09714773e-02 -1.14258587e+00 -1.01161444e+00 9.18058120e-03
-5.19907959e-02 9.61180210e-01 1.65113643e-01 -5.05101621... | [9.609786033630371, 8.765183448791504] |
d11c9142-a42f-4122-a38f-8e4f55f787a6 | utilizing-resource-rich-language-datasets-for | 2111.12276 | null | https://arxiv.org/abs/2111.12276v1 | https://arxiv.org/pdf/2111.12276v1.pdf | Utilizing Resource-Rich Language Datasets for End-to-End Scene Text Recognition in Resource-Poor Languages | This paper presents a novel training method for end-to-end scene text recognition. End-to-end scene text recognition offers high recognition accuracy, especially when using the encoder-decoder model based on Transformer. To train a highly accurate end-to-end model, we need to prepare a large image-to-text paired datase... | ['Ryo Masumura', 'Tomohiro Tanaka', 'Akihiko Takashima', 'Mana Ihori', 'Naoki Makishima', 'Yoshihiro Yamazaki', 'Shota Orihashi'] | 2021-11-24 | null | null | null | null | ['scene-text-recognition'] | ['computer-vision'] | [ 3.11272502e-01 -4.96221393e-01 -6.88667083e-03 -5.06820679e-01
-1.04797184e+00 -2.20793381e-01 5.31773448e-01 -4.73626971e-01
-7.87970483e-01 3.43963981e-01 4.42071795e-01 -2.01629609e-01
5.59781492e-01 -7.05611944e-01 -6.88951731e-01 -5.93711257e-01
8.26644301e-01 6.17043257e-01 1.74860775e-01 -5.29021434... | [11.830876350402832, 2.029492139816284] |
61de4378-7663-4313-92e2-35ceddc3a590 | low-rank-isomap-algorithm | 2103.04060 | null | https://arxiv.org/abs/2103.04060v1 | https://arxiv.org/pdf/2103.04060v1.pdf | Low-Rank Isomap Algorithm | The Isomap is a well-known nonlinear dimensionality reduction method that highly suffers from computational complexity. Its computational complexity mainly arises from two stages; a) embedding a full graph on the data in the ambient space, and b) a complete eigenvalue decomposition. Although the reduction of the comput... | ['Mohammad Hossein Kahaei', 'Eysan Mehrbani'] | 2021-03-06 | null | null | null | null | ['image-clustering'] | ['computer-vision'] | [ 4.75056350e-01 1.63816020e-01 3.24779421e-01 8.23395047e-03
-3.39126557e-01 -4.42370653e-01 5.94531357e-01 -2.61163861e-01
-3.63480777e-01 7.72570372e-02 2.23710939e-01 -1.24689251e-01
-7.10376382e-01 -6.57157362e-01 -1.66920930e-01 -9.98310030e-01
1.68055575e-02 5.36454976e-01 -1.41382232e-01 9.42972675... | [7.790088176727295, 4.255291938781738] |
d43dc5da-b18f-4e87-9683-50ad19f43429 | a-multiple-choices-reading-comprehension | 2303.18162 | null | https://arxiv.org/abs/2303.18162v1 | https://arxiv.org/pdf/2303.18162v1.pdf | A Multiple Choices Reading Comprehension Corpus for Vietnamese Language Education | Machine reading comprehension has been an interesting and challenging task in recent years, with the purpose of extracting useful information from texts. To attain the computer ability to understand the reading text and answer relevant information, we introduce ViMMRC 2.0 - an extension of the previous ViMMRC for the t... | ['Ngan Luu-Thuy Nguyen', 'Kiet Van Nguyen', 'Tuong Quang Pham', 'Khoi Trong Hoang', 'Son T. Luu'] | 2023-03-31 | null | null | null | null | ['reading-comprehension', 'machine-reading-comprehension'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.49227428e-01 2.95081615e-01 1.99263021e-01 -3.09181899e-01
-7.02941000e-01 -8.00028026e-01 2.52576679e-01 3.31395477e-01
-8.55789721e-01 7.33037233e-01 4.41476375e-01 -9.18650150e-01
-1.95272759e-01 -1.07194138e+00 -7.34399438e-01 -1.83367118e-01
6.75674558e-01 2.57050753e-01 4.84659761e-01 -7.16220677... | [11.392967224121094, 8.227363586425781] |
5a69650f-d171-4121-a8d4-f61ae46bae55 | a-cost-based-multi-layer-network-approach-for | 2209.09032 | null | https://arxiv.org/abs/2209.09032v2 | https://arxiv.org/pdf/2209.09032v2.pdf | A cost-based multi-layer network approach for the discovery of patient phenotypes | Clinical records frequently include assessments of the characteristics of patients, which may include the completion of various questionnaires. These questionnaires provide a variety of perspectives on a patient's current state of well-being. Not only is it critical to capture the heterogeneity given by these perspecti... | ['Myra Spiliopoulou', 'Winfried Schlee', 'Uli Niemann', 'Clara Puga'] | 2022-09-19 | null | null | null | null | ['community-detection'] | ['graphs'] | [ 4.08659056e-02 7.02149328e-03 -6.80625588e-02 -5.61801910e-01
-8.95482838e-01 -7.39338025e-02 -2.15233967e-01 9.54881072e-01
-4.13705856e-01 6.77998066e-01 6.29277289e-01 3.47263247e-01
-6.41235709e-01 -7.48860478e-01 7.15823025e-02 -7.36304641e-01
-2.88125187e-01 6.53915346e-01 -1.89758748e-01 1.62658885... | [13.623408317565918, 4.685906887054443] |
4aa3309b-52a0-4d3c-a6f8-e78ed207a294 | energy-transformer | 2302.07253 | null | https://arxiv.org/abs/2302.07253v1 | https://arxiv.org/pdf/2302.07253v1.pdf | Energy Transformer | Transformers have become the de facto models of choice in machine learning, typically leading to impressive performance on many applications. At the same time, the architectural development in the transformer world is mostly driven by empirical findings, and the theoretical understanding of their architectural building... | ['Dmitry Krotov', 'Mohammed J. Zaki', 'Duen Horng Chau', 'Hendrik Strobelt', 'Rameswar Panda', 'Bao Pham', 'Yuchen Liang', 'Benjamin Hoover'] | 2023-02-14 | null | null | null | null | ['graph-anomaly-detection'] | ['graphs'] | [ 1.02742709e-01 2.37026423e-01 1.13349542e-01 -6.93725869e-02
2.96461403e-01 -2.19570220e-01 7.45847642e-01 5.57553582e-02
-2.35455737e-01 2.71436334e-01 1.15090735e-01 -2.69852579e-01
-2.47820735e-01 -1.02695167e+00 -7.06995845e-01 -9.49726582e-01
-1.61851227e-01 5.16755283e-01 4.30310786e-01 -4.65258896... | [7.013132095336914, 6.178494930267334] |
da9f4109-193f-410c-9ea4-fef3841e3219 | identifiability-of-the-simplex-volume | 1406.5273 | null | http://arxiv.org/abs/1406.5273v2 | http://arxiv.org/pdf/1406.5273v2.pdf | Identifiability of the Simplex Volume Minimization Criterion for Blind Hyperspectral Unmixing: The No Pure-Pixel Case | In blind hyperspectral unmixing (HU), the pure-pixel assumption is well-known
to be powerful in enabling simple and effective blind HU solutions. However,
the pure-pixel assumption is not always satisfied in an exact sense, especially
for scenarios where pixels are heavily mixed. In the no pure-pixel case, a good
blind... | ['Chong-Yung Chi', 'Wei-Chiang Li', 'Wing-Kin Ma', 'Chia-Hsiang Lin', 'ArulMurugan Ambikapathi'] | 2014-06-20 | null | null | null | null | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 4.34251219e-01 -2.22950846e-01 8.82421434e-02 2.95979649e-01
-5.24066269e-01 -5.56127012e-01 3.70804518e-01 -5.05280435e-01
-4.79334965e-02 9.73822236e-01 4.42374945e-02 -3.76310557e-01
-2.74607331e-01 -7.61683047e-01 -5.85003197e-01 -1.48152089e+00
2.01560587e-01 6.15575463e-02 -3.72656375e-01 -1.46170706... | [10.073919296264648, -2.0587317943573] |
bf7765a2-e4a9-4ed9-ae71-5ce4bf8074ec | chalearn-looking-at-people-and-faces-of-the | null | null | https://ieeexplore.ieee.org/document/7789583 | https://sergioescalera.com/wp-content/uploads/2016/05/18.pdf | ChaLearn Looking at People and Faces of the World: Face Analysis Workshop and Challenge 2016 | We present the 2016 ChaLearn Looking at People and Faces of the World Challenge and Workshop, which ran three competitions on the common theme of face analysis from still images. The first one, Looking at People, addressed age estimation, while the second and third competitions, Faces of the World, addressed accessory ... | ['Michel Valstar', 'Georgios Tzimiropoulos', 'Xavier Baró', 'Brais Martínez', 'Marc Oliu', 'Ciprian Corneanu', 'Sergio Escalera', 'Isabelle Guyon', 'Mohammad Ali Bagheri', 'Mercedes Torres Torres', 'Hugo Jair Escalante'] | 2016-12-19 | null | null | null | 2016-ieee-conference-on-computer-vision-and-1 | ['gender-prediction'] | ['computer-vision'] | [-2.78741658e-01 7.01392442e-02 1.44571558e-01 -6.48701847e-01
-4.95835871e-01 -5.40039659e-01 8.73407662e-01 -2.34188393e-01
-7.38197267e-01 3.60597551e-01 4.82102096e-01 4.79593217e-01
2.83709317e-01 -1.13131039e-01 -1.90627396e-01 -6.81075573e-01
-2.43010044e-01 7.46823132e-01 -5.85841984e-02 -5.81346937... | [13.815469741821289, 1.006756067276001] |
59aff169-3139-4d68-88ec-9f7421bb27d6 | robust-hate-speech-detection-in-social-media | 2307.01680 | null | https://arxiv.org/abs/2307.01680v1 | https://arxiv.org/pdf/2307.01680v1.pdf | Robust Hate Speech Detection in Social Media: A Cross-Dataset Empirical Evaluation | The automatic detection of hate speech online is an active research area in NLP. Most of the studies to date are based on social media datasets that contribute to the creation of hate speech detection models trained on them. However, data creation processes contain their own biases, and models inherently learn from the... | ['Jose Camacho-Collados', 'Dimosthenis Antypas'] | 2023-07-04 | null | null | null | null | ['hate-speech-detection'] | ['natural-language-processing'] | [-1.09750919e-01 -1.64925009e-01 -2.32811630e-01 -1.10383384e-01
-3.49267006e-01 -7.23824263e-01 9.47161317e-01 4.31633294e-01
-4.50570941e-01 3.52517128e-01 5.39877653e-01 -9.52635780e-02
-9.45524499e-03 -5.01402140e-01 -4.72187966e-01 -3.38845313e-01
8.83117765e-02 -5.57346158e-02 2.83848912e-01 -1.61409065... | [8.725679397583008, 10.486226081848145] |
558437cb-f8f7-47e1-b39a-1c98ebc30043 | metagenomic-analysis-using-phylogenetic | 2202.03534 | null | https://arxiv.org/abs/2202.03534v2 | https://arxiv.org/pdf/2202.03534v2.pdf | Metagenomic Analysis using Phylogenetic Placement -- A Review of the First Decade | Phylogenetic placement refers to a family of tools and methods to analyze, visualize, and interpret the tsunami of metagenomic sequencing data generated by high-throughput sequencing. Compared to alternative (e. g., similarity-based) methods, it puts metabarcoding sequences into a phylogenetic context using a set of kn... | ['Pierre Barbera', 'Micah Dunthorn', 'Alexandros Stamatakis', 'Lucas Czech'] | 2022-02-07 | null | null | null | null | ['misconceptions'] | ['miscellaneous'] | [ 6.83180034e-01 -7.99311280e-01 1.95144385e-01 2.07893580e-01
1.09017394e-01 -1.08327007e+00 5.47349036e-01 6.88495040e-01
-3.73187810e-01 7.10435987e-01 -4.34731469e-02 -6.99131250e-01
-4.08190817e-01 -6.35470688e-01 -2.07686216e-01 -1.13826978e+00
-6.32073045e-01 4.11475599e-01 1.88649625e-01 -1.28815576... | [4.937366962432861, 5.135030269622803] |
9fec7dff-47c0-451a-9108-9ee9f4618760 | isaac-gym-high-performance-gpu-based-physics | 2108.10470 | null | https://arxiv.org/abs/2108.10470v2 | https://arxiv.org/pdf/2108.10470v2.pdf | Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning | Isaac Gym offers a high performance learning platform to train policies for wide variety of robotics tasks directly on GPU. Both physics simulation and the neural network policy training reside on GPU and communicate by directly passing data from physics buffers to PyTorch tensors without ever going through any CPU bot... | ['Gavriel State', 'Ankur Handa', 'Arthur Allshire', 'Nikita Rudin', 'David Hoeller', 'Miles Macklin', 'Kier Storey', 'Michelle Lu', 'Yunrong Guo', 'Lukasz Wawrzyniak', 'Viktor Makoviychuk'] | 2021-08-24 | null | null | null | null | ['omniverse-isaac-gym', 'isaac-gym-preview'] | ['robots', 'robots'] | [-6.95932984e-01 -1.87639624e-01 4.09882888e-02 -8.35077390e-02
-4.73260581e-01 -6.81754827e-01 5.25186002e-01 -5.12918718e-02
-5.97368062e-01 7.43287086e-01 -3.65604758e-01 -7.70142496e-01
3.50455523e-01 -9.30073321e-01 -1.12097728e+00 -6.89098597e-01
-1.63372800e-01 5.79371631e-01 4.23214674e-01 -1.79459184... | [4.312175750732422, 1.0531892776489258] |
57f8c782-02c8-4dac-b8ea-11309bd10867 | an-efficient-speech-separation-network-based | 2306.05887 | null | https://arxiv.org/abs/2306.05887v1 | https://arxiv.org/pdf/2306.05887v1.pdf | An Efficient Speech Separation Network Based on Recurrent Fusion Dilated Convolution and Channel Attention | We present an efficient speech separation neural network, ARFDCN, which combines dilated convolutions, multi-scale fusion (MSF), and channel attention to overcome the limited receptive field of convolution-based networks and the high computational cost of transformer-based networks. The suggested network architecture i... | ['Junyu Wang'] | 2023-06-09 | null | null | null | null | ['speech-separation'] | ['speech'] | [ 6.66018277e-02 -2.84337372e-01 -2.19912767e-01 -2.34697253e-01
-5.06398618e-01 -1.72493190e-01 2.98443407e-01 -3.30874026e-01
-5.23855150e-01 4.89175677e-01 4.41217780e-01 -4.10288721e-01
1.10948741e-01 -6.03550196e-01 -3.80984902e-01 -7.95652688e-01
-9.06084757e-03 -3.55515301e-01 1.31695181e-01 -2.43488714... | [14.707359313964844, 5.881726264953613] |
97b5747a-3b1e-498f-a1e9-e439c46afb84 | on-the-connection-between-temperature-and | 2303.15164 | null | https://arxiv.org/abs/2303.15164v1 | https://arxiv.org/pdf/2303.15164v1.pdf | On the Connection between Temperature and Volatility in Ideal Agent Systems | Models for spin systems known from statistical physics are applied by analogy in econometrics in the form of agent-based models. Researchers suggest that the state variable temperature $T$ corresponds to volatility $\sigma$ in capital market theory problems. To the best of our knowledge, this has not yet been theoretic... | ['John H. Stiebel', 'Ingo Hoffmann', 'Christoph J. Börner'] | 2023-03-27 | null | null | null | null | ['econometrics'] | ['miscellaneous'] | [-4.77275789e-01 1.19594507e-01 -1.69825524e-01 -1.79836497e-01
2.35195637e-01 -3.74444813e-01 7.72179902e-01 -6.09307289e-02
-6.15513146e-01 1.06281304e+00 -5.65127015e-01 -4.26941246e-01
-6.15294337e-01 -6.78382456e-01 -1.71428204e-01 -7.95098066e-01
-1.79382548e-01 6.86187327e-01 -1.90329954e-01 -3.81081730... | [5.236969470977783, 4.124563694000244] |
e1be84e9-a1e9-4073-8ba5-6166036067e3 | supervised-opinion-aspect-extraction-by | 1612.07940 | null | http://arxiv.org/abs/1612.07940v1 | http://arxiv.org/pdf/1612.07940v1.pdf | Supervised Opinion Aspect Extraction by Exploiting Past Extraction Results | One of the key tasks of sentiment analysis of product reviews is to extract
product aspects or features that users have expressed opinions on. In this
work, we focus on using supervised sequence labeling as the base approach to
performing the task. Although several extraction methods using sequence
labeling methods suc... | ['Annice Kim', 'Bing Liu', 'Hu Xu', 'Lei Shu'] | 2016-12-23 | null | null | null | null | ['aspect-extraction'] | ['natural-language-processing'] | [ 5.16246498e-01 3.51252019e-01 -4.47027832e-01 -6.99431896e-01
-3.23770255e-01 -7.64809370e-01 6.38435423e-01 5.42235434e-01
-3.21909577e-01 8.32305312e-01 8.14162120e-02 -5.13538420e-01
4.50660020e-01 -7.06904471e-01 -4.44235951e-01 -4.38851476e-01
3.08082193e-01 2.43128046e-01 3.76541018e-01 -2.49648646... | [11.24921703338623, 6.781399726867676] |
26170773-b2af-4aa1-b8de-e1d2cd9776f0 | fpnn-field-probing-neural-networks-for-3d | 1605.06240 | null | http://arxiv.org/abs/1605.06240v3 | http://arxiv.org/pdf/1605.06240v3.pdf | FPNN: Field Probing Neural Networks for 3D Data | Building discriminative representations for 3D data has been an important
task in computer graphics and computer vision research. Convolutional Neural
Networks (CNNs) have shown to operate on 2D images with great success for a
variety of tasks. Lifting convolution operators to 3D (3DCNNs) seems like a
plausible and pro... | ['Yangyan Li', 'Leonidas J. Guibas', 'Hao Su', 'Soeren Pirk', 'Charles R. Qi'] | 2016-05-20 | fpnn-field-probing-neural-networks-for-3d-1 | http://papers.nips.cc/paper/6416-fpnn-field-probing-neural-networks-for-3d-data | http://papers.nips.cc/paper/6416-fpnn-field-probing-neural-networks-for-3d-data.pdf | neurips-2016-12 | ['3d-object-recognition'] | ['computer-vision'] | [ 6.66243806e-02 -3.32633182e-02 -1.11041311e-03 -2.71546304e-01
-2.59002745e-01 -5.76140344e-01 4.27806169e-01 2.12392822e-01
-4.79738742e-01 8.98471773e-02 1.12475410e-01 -5.59241891e-01
1.07908279e-01 -1.10982108e+00 -7.63246596e-01 -4.57064182e-01
-2.78653890e-01 4.66699481e-01 4.09533143e-01 8.17510039... | [8.025181770324707, -3.65952467918396] |
6717453d-ef76-4333-931b-84f03a73b8bb | sgg-learning-to-select-guide-and-generate-for | 2105.02544 | null | https://arxiv.org/abs/2105.02544v2 | https://arxiv.org/pdf/2105.02544v2.pdf | SGG: Learning to Select, Guide, and Generate for Keyphrase Generation | Keyphrases, that concisely summarize the high-level topics discussed in a document, can be categorized into present keyphrase which explicitly appears in the source text, and absent keyphrase which does not match any contiguous subsequence but is highly semantically related to the source. Most existing keyphrase genera... | ['BoWen Zhou', 'Xiaodong He', 'Youzheng Wu', 'Yifan Wang', 'Junwei Bao', 'Jing Zhao'] | 2021-05-06 | null | https://aclanthology.org/2021.naacl-main.455 | https://aclanthology.org/2021.naacl-main.455.pdf | naacl-2021-4 | ['keyphrase-generation'] | ['natural-language-processing'] | [ 3.29747945e-01 1.65522844e-01 -3.18801075e-01 1.62127301e-01
-1.03848660e+00 -8.57001781e-01 1.33503866e+00 4.74129736e-01
-1.42853171e-01 1.00347912e+00 9.12821770e-01 -4.60381359e-01
1.73515901e-01 -1.10070395e+00 -7.36844599e-01 -4.89734352e-01
2.48042136e-01 2.55122334e-01 3.10785860e-01 -5.06325722... | [12.318641662597656, 8.954726219177246] |
42a64134-d68f-4c48-9055-38abad965ec5 | medleyvox-an-evaluation-dataset-for-multiple | 2211.07302 | null | https://arxiv.org/abs/2211.07302v2 | https://arxiv.org/pdf/2211.07302v2.pdf | MedleyVox: An Evaluation Dataset for Multiple Singing Voices Separation | Separation of multiple singing voices into each voice is a rarely studied area in music source separation research. The absence of a benchmark dataset has hindered its progress. In this paper, we present an evaluation dataset and provide baseline studies for multiple singing voices separation. First, we introduce Medle... | ['Kyogu Lee', 'Ben Sangbae Chon', 'Keunwoo Choi', 'Hyeongi Moon', 'Chang-Bin Jeon'] | 2022-11-14 | null | null | null | null | ['music-source-separation'] | ['music'] | [ 1.18727408e-01 -5.88236392e-01 -1.99315604e-02 2.22158045e-01
-1.24898469e+00 -8.86416554e-01 2.15108901e-01 -6.89479172e-01
-1.51933944e-02 3.70457232e-01 3.45694602e-01 -1.28911007e-02
-3.83495659e-01 2.14052238e-02 -3.04025859e-01 -8.58720779e-01
1.30526289e-01 1.32107750e-01 1.90993752e-02 -1.05007000... | [15.374407768249512, 5.517920970916748] |
da4e4966-be48-4f4a-851e-61be646987bf | lidarmutlinet-unifying-lidar-semantic | 2206.11428 | null | https://arxiv.org/abs/2206.11428v2 | https://arxiv.org/pdf/2206.11428v2.pdf | LidarMultiNet: Unifying LiDAR Semantic Segmentation, 3D Object Detection, and Panoptic Segmentation in a Single Multi-task Network | This technical report presents the 1st place winning solution for the Waymo Open Dataset 3D semantic segmentation challenge 2022. Our network, termed LidarMultiNet, unifies the major LiDAR perception tasks such as 3D semantic segmentation, object detection, and panoptic segmentation in a single framework. At the core o... | ['Hassan Foroosh', 'Panqu Wang', 'Yu Wang', 'Yufei Xie', 'Zixiang Zhou', 'Weijia Chen', 'Dongqiangzi Ye'] | 2022-06-23 | null | null | null | null | ['lidar-semantic-segmentation'] | ['computer-vision'] | [ 3.13747942e-01 2.97138333e-01 -2.37645879e-01 -7.29559422e-01
-1.19971085e+00 -6.12885714e-01 5.90787530e-01 -2.48824824e-02
-4.31393236e-01 2.09416717e-01 -1.15495302e-01 -3.59633297e-01
2.12365404e-01 -7.66581297e-01 -7.80178726e-01 -2.44082049e-01
1.20760590e-01 9.11455452e-01 7.30320394e-01 8.86156186... | [8.074877738952637, -2.7602667808532715] |
262b3bab-51f5-40a4-8501-7072eb79908f | a-survey-on-anti-spoofing-methods-for-face | 2010.04145 | null | https://arxiv.org/abs/2010.04145v1 | https://arxiv.org/pdf/2010.04145v1.pdf | A Survey On Anti-Spoofing Methods For Face Recognition with RGB Cameras of Generic Consumer Devices | The widespread deployment of face recognition-based biometric systems has made face Presentation Attack Detection (face anti-spoofing) an increasingly critical issue. This survey thoroughly investigates the face Presentation Attack Detection (PAD) methods, that only require RGB cameras of generic consumer devices, over... | ['Jean-Christophe Burie', 'Muhammad Muzzamil Luqman', 'Muriel Visani', 'Zuheng Ming'] | 2020-10-08 | null | null | null | null | ['face-presentation-attack-detection'] | ['computer-vision'] | [ 5.37968218e-01 -1.95027366e-01 -1.53010219e-01 -6.26563579e-02
-7.13912100e-02 -7.80834734e-01 5.58972418e-01 -7.73307681e-01
5.14535122e-02 4.19411689e-01 -6.92291185e-02 -2.80800164e-01
-7.76319951e-02 -4.52850312e-01 -7.80381188e-02 -9.21014547e-01
-3.17606688e-01 -1.91865802e-01 -1.18804343e-01 -2.37708747... | [13.124380111694336, 1.1485364437103271] |
beed1761-cf4d-40f1-aed7-263dbd94c57e | differentiable-divergences-between-time | 2010.08354 | null | https://arxiv.org/abs/2010.08354v3 | https://arxiv.org/pdf/2010.08354v3.pdf | Differentiable Divergences Between Time Series | Computing the discrepancy between time series of variable sizes is notoriously challenging. While dynamic time warping (DTW) is popularly used for this purpose, it is not differentiable everywhere and is known to lead to bad local optima when used as a "loss". Soft-DTW addresses these issues, but it is not a positive d... | ['Jean-Philippe Vert', 'Arthur Mensch', 'Mathieu Blondel'] | 2020-10-16 | null | null | null | null | ['time-series-averaging'] | ['time-series'] | [ 2.40996137e-01 -2.71020204e-01 4.76024747e-02 -2.51196861e-01
-7.68073738e-01 -8.33998919e-01 4.18843567e-01 4.24242496e-01
-5.64309537e-01 8.13150406e-01 -9.10643190e-02 -1.59227803e-01
-4.03793395e-01 -5.19946277e-01 -6.23396397e-01 -9.47573364e-01
-6.04654849e-01 2.46103495e-01 2.89593846e-01 -3.90940517... | [7.3428425788879395, 3.394909143447876] |
48142a91-eb4b-419e-91a5-d26173abcc99 | softpool-an-encoder-decoder-network-for-point | 2205.03899 | null | https://arxiv.org/abs/2205.03899v1 | https://arxiv.org/pdf/2205.03899v1.pdf | SoftPool++: An Encoder-Decoder Network for Point Cloud Completion | We propose a novel convolutional operator for the task of point cloud completion. One striking characteristic of our approach is that, conversely to related work it does not require any max-pooling or voxelization operation. Instead, the proposed operator used to learn the point cloud embedding in the encoder extracts ... | ['Federico Tombari', 'Nassir Navab', 'David Joseph Tan', 'Yida Wang'] | 2022-05-08 | null | null | null | null | ['point-cloud-completion'] | ['computer-vision'] | [ 2.28583738e-01 3.80889803e-01 3.34212393e-01 -5.72163403e-01
-5.64640701e-01 -3.74887764e-01 7.15098262e-01 2.94797689e-01
-5.10259032e-01 3.71161997e-01 7.89257511e-02 1.64740473e-01
-2.10645348e-01 -1.10249686e+00 -1.18483305e+00 -5.20841122e-01
-2.06067830e-01 4.29960877e-01 3.00384790e-01 -8.95160139... | [8.226722717285156, -3.496453046798706] |
69a26e92-fb2b-454a-87b8-2e53781ee872 | leverage-unlabeled-data-for-abstractive | 2007.15296 | null | https://arxiv.org/abs/2007.15296v2 | https://arxiv.org/pdf/2007.15296v2.pdf | Leverage Unlabeled Data for Abstractive Speech Summarization with Self-Supervised Learning and Back-Summarization | Supervised approaches for Neural Abstractive Summarization require large annotated corpora that are costly to build. We present a French meeting summarization task where reports are predicted based on the automatic transcription of the meeting audio recordings. In order to build a corpus for this task, it is necessary ... | ['Yannick Estève', 'François Hernandez', 'Vincent Nguyen', 'Paul Tardy', 'Louis de Seynes', 'David Janiszek'] | 2020-07-30 | null | null | null | null | ['meeting-summarization'] | ['natural-language-processing'] | [ 7.09016621e-01 6.22099936e-01 1.04103900e-01 -3.64414006e-01
-1.75621331e+00 -7.25302696e-01 5.52444816e-01 4.14693177e-01
-4.79079187e-01 9.67477024e-01 7.51154959e-01 3.29827964e-02
4.26176131e-01 -3.19700748e-01 -9.33182478e-01 -3.72240961e-01
1.13615461e-01 7.31982827e-01 -7.35694692e-02 -1.34335667... | [12.449281692504883, 9.458551406860352] |
45d2b4b2-af66-4f47-89da-447af327dda6 | lesion-guided-explainable-few-weak-shot | 2211.08732 | null | https://arxiv.org/abs/2211.08732v2 | https://arxiv.org/pdf/2211.08732v2.pdf | Lesion Guided Explainable Few Weak-shot Medical Report Generation | Medical images are widely used in clinical practice for diagnosis. Automatically generating interpretable medical reports can reduce radiologists' burden and facilitate timely care. However, most existing approaches to automatic report generation require sufficient labeled data for training. In addition, the learned mo... | ['Yefeng Zheng', 'Liansheng Wang', 'Dong Wei', 'Jinghan Sun'] | 2022-11-16 | null | null | null | null | ['medical-report-generation'] | ['medical'] | [ 3.76269788e-01 7.48890996e-01 -3.86543691e-01 -5.46715558e-01
-1.33809292e+00 -4.60501999e-01 5.36986053e-01 5.20393610e-01
2.15696413e-02 6.59139276e-01 7.06198454e-01 -5.24439476e-02
9.50338915e-02 -6.94833815e-01 -5.28273344e-01 -6.49688423e-01
1.30932435e-01 5.32762706e-01 -1.32294260e-02 2.73824751... | [15.04632568359375, -1.4070838689804077] |
bb4b69a3-431e-4d98-ba13-f4c5cabe4f61 | vtc-improving-video-text-retrieval-with-user | 2210.10820 | null | https://arxiv.org/abs/2210.10820v1 | https://arxiv.org/pdf/2210.10820v1.pdf | VTC: Improving Video-Text Retrieval with User Comments | Multi-modal retrieval is an important problem for many applications, such as recommendation and search. Current benchmarks and even datasets are often manually constructed and consist of mostly clean samples where all modalities are well-correlated with the content. Thus, current video-text retrieval literature largely... | ['Christian Rupprecht', 'Yuki M. Asano', 'James Thewlis', 'Laura Hanu'] | 2022-10-19 | null | null | null | null | ['video-text-retrieval'] | ['computer-vision'] | [ 1.00932576e-01 -3.04775059e-01 -6.66007221e-01 -1.41496718e-01
-1.26580954e+00 -5.80859363e-01 8.47375691e-01 2.56748438e-01
-2.38640040e-01 4.62131470e-01 8.66949260e-01 6.11604750e-02
-1.20879456e-01 -1.24199413e-01 -6.66780412e-01 -5.07105052e-01
3.32113765e-02 7.10877851e-02 -1.41761065e-01 -6.21760823... | [10.360360145568848, 0.9670970439910889] |
90180a8d-a635-4522-85ef-3f2970d1a987 | underground-diagnosis-based-on-gpr-and | 2211.15480 | null | https://arxiv.org/abs/2211.15480v1 | https://arxiv.org/pdf/2211.15480v1.pdf | Underground Diagnosis Based on GPR and Learning in the Model Space | Ground Penetrating Radar (GPR) has been widely used in pipeline detection and underground diagnosis. In practical applications, the characteristics of the GPR data of the detected area and the likely underground anomalous structures could be rarely acknowledged before fully analyzing the obtained GPR data, causing chal... | ['Huanhuan Chen', 'Yizhan Fan', 'Xiren Zhou', 'Ao Chen'] | 2022-11-25 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [ 1.16749197e-01 1.72744974e-01 3.06947500e-01 -5.69929898e-01
-4.59833920e-01 4.31190938e-01 -2.47532308e-01 -7.52009451e-02
1.10972188e-01 4.14693236e-01 -1.49616851e-02 -2.87001431e-01
-3.90974045e-01 -9.81343985e-01 -1.69525281e-01 -8.87492299e-01
-5.30481637e-01 3.91702533e-01 4.61247236e-01 -1.94824990... | [6.838143825531006, 1.484887957572937] |
666688f9-47d9-4abc-a1de-b7d1c0ff5de5 | negative-data-augmentation-1 | 2102.05113 | null | https://arxiv.org/abs/2102.05113v1 | https://arxiv.org/pdf/2102.05113v1.pdf | Negative Data Augmentation | Data augmentation is often used to enlarge datasets with synthetic samples generated in accordance with the underlying data distribution. To enable a wider range of augmentations, we explore negative data augmentation strategies (NDA)that intentionally create out-of-distribution samples. We show that such negative out-... | ['Stefano Ermon', 'Hongxia Jin', 'Burak Uzkent', 'Jiaming Song', 'Kumar Ayush', 'Abhishek Sinha'] | 2021-02-09 | negative-data-augmentation | https://openreview.net/forum?id=Ovp8dvB8IBH | https://openreview.net/pdf?id=Ovp8dvB8IBH | iclr-2021-1 | ['conditional-image-generation'] | ['computer-vision'] | [ 9.18719769e-01 5.93516648e-01 -5.29150307e-01 -3.83945227e-01
-7.08012044e-01 -5.45292795e-01 8.89756918e-01 -1.42347902e-01
-2.03243241e-01 7.17359543e-01 3.41237307e-01 -1.14294633e-01
4.01457399e-01 -8.99020672e-01 -9.36037242e-01 -9.13764656e-01
2.39516169e-01 4.90815908e-01 -2.41373137e-01 -1.05090410... | [11.566633224487305, -0.12281964719295502] |
80569c0c-1c7c-4c95-a34f-2b2964b899a9 | lrs3-ted-a-large-scale-dataset-for-visual | 1809.00496 | null | http://arxiv.org/abs/1809.00496v2 | http://arxiv.org/pdf/1809.00496v2.pdf | LRS3-TED: a large-scale dataset for visual speech recognition | This paper introduces a new multi-modal dataset for visual and audio-visual
speech recognition. It includes face tracks from over 400 hours of TED and TEDx
videos, along with the corresponding subtitles and word alignment boundaries.
The new dataset is substantially larger in scale compared to other public
datasets tha... | ['Triantafyllos Afouras', 'Joon Son Chung', 'Andrew Zisserman'] | 2018-09-03 | null | null | null | null | ['audio-visual-speech-recognition'] | ['speech'] | [-5.69254085e-02 -2.16292500e-01 -6.06512487e-01 -8.35589051e-01
-9.15040970e-01 -5.31803191e-01 6.90733135e-01 -5.49389899e-01
-2.19080150e-01 5.48479080e-01 4.39386040e-01 2.31561944e-01
1.25908375e-01 1.24677002e-01 -3.57943743e-01 -4.71847206e-01
-9.57676470e-02 5.20338953e-01 -2.77666569e-01 -8.56114030... | [14.282187461853027, 1.3014838695526123] |
620f96bb-536d-40f3-afcb-32d805c07853 | tracking-legislators-expressed-policy-agendas | null | null | https://osf.io/preprints/socarxiv/ync87/ | https://files.osf.io/v1/resources/ync87/providers/osfstorage/61e71a74bc925b0694d4bc4d | Tracking Legislators’ Expressed Policy Agendas in Real Time | We develop a real-time scalable method to analyze strategic communication by political actors on salient policy issues through their tweets. Using word embeddings and supervised machine learning models, we classify legislators' tweets according to whether or not they reference policy issues as well as what positions th... | ['Jens Hainmueller', 'Jeremy Weinstein', 'Duncan Lawrence', 'David Laitin', 'Alexandra Siegel'] | 2022-01-18 | null | null | null | socarxiv-2022-1 | ['political-salient-issue-orientation-detection'] | ['natural-language-processing'] | [-1.61263049e-01 -7.83841535e-02 -9.14551079e-01 -2.24297523e-01
-8.76981616e-01 -1.04986966e+00 1.23110175e+00 9.33984816e-01
-7.10223436e-01 5.77665925e-01 1.57676172e+00 -1.14157319e+00
-4.48312648e-02 -1.03408539e+00 -2.57623464e-01 -3.40202481e-01
3.80192429e-01 4.67544675e-01 -2.29940519e-01 -6.17027700... | [8.873345375061035, 9.964974403381348] |
e38d0112-8ce1-4e15-b93b-1874642c314d | fast-private-kernel-density-estimation-via | 2307.01877 | null | https://arxiv.org/abs/2307.01877v1 | https://arxiv.org/pdf/2307.01877v1.pdf | Fast Private Kernel Density Estimation via Locality Sensitive Quantization | We study efficient mechanisms for differentially private kernel density estimation (DP-KDE). Prior work for the Gaussian kernel described algorithms that run in time exponential in the number of dimensions $d$. This paper breaks the exponential barrier, and shows how the KDE can privately be approximated in time linear... | ['Nina Mishra', 'Yonatan Naamad', 'Tal Wagner'] | 2023-07-04 | null | null | null | null | ['quantization', 'density-estimation'] | ['methodology', 'methodology'] | [-7.59664655e-01 -3.49647641e-01 -3.43523234e-01 -3.61627340e-01
-1.60428250e+00 -7.08909392e-01 2.16244847e-01 3.12701732e-01
-7.43098974e-01 7.85918951e-01 2.39704549e-01 -2.38946632e-01
9.12636071e-02 -1.24014199e+00 -9.65087175e-01 -1.01974034e+00
-7.60067821e-01 5.67244768e-01 5.55917203e-01 2.63903230... | [6.177935600280762, 6.35955810546875] |
6484ab77-0b07-4836-94f3-4e64ac01efc5 | senpoi-at-semeval-2022-task-10-point-me-to | null | null | https://aclanthology.org/2022.semeval-1.183 | https://aclanthology.org/2022.semeval-1.183.pdf | SenPoi at SemEval-2022 Task 10: Point me to your Opinion, SenPoi | Structured Sentiment Analysis is the task of extracting sentiment tuples in a graph structure commonly from review texts. We adapt the Aspect-Based Sentiment Analysis pointer network BARTABSA to model this tuple extraction as a sequence prediction task and extend their output grammar to account for the increased comple... | ['Andreas Hotho', 'Sebastian Wankerl', 'Jan Pfister'] | null | null | null | null | semeval-naacl-2022-7 | ['aspect-based-sentiment-analysis'] | ['natural-language-processing'] | [ 5.31483769e-01 3.87010336e-01 -4.49366540e-01 -5.08165836e-01
-7.71918893e-01 -1.08687043e+00 5.20463943e-01 5.45428395e-01
-2.24352971e-01 6.77932799e-01 4.22314286e-01 -5.95630825e-01
2.99612701e-01 -7.04166472e-01 -6.86091185e-01 -7.94324651e-02
1.57659203e-01 5.33324957e-01 2.45609522e-01 -4.31629717... | [11.375160217285156, 6.834201812744141] |
84d9c4c6-0c0e-4571-815a-fc4d07a2cdba | testing-the-reliability-of-chatgpt-for-text | 2304.11085 | null | https://arxiv.org/abs/2304.11085v1 | https://arxiv.org/pdf/2304.11085v1.pdf | Testing the Reliability of ChatGPT for Text Annotation and Classification: A Cautionary Remark | Recent studies have demonstrated promising potential of ChatGPT for various text annotation and classification tasks. However, ChatGPT is non-deterministic which means that, as with human coders, identical input can lead to different outputs. Given this, it seems appropriate to test the reliability of ChatGPT. Therefor... | ['Michael V. Reiss'] | 2023-04-17 | null | null | null | null | ['text-annotation'] | ['natural-language-processing'] | [ 2.31354311e-01 7.73497745e-02 2.37631544e-01 -4.44426686e-01
-8.41810226e-01 -7.19495893e-01 5.53179026e-01 5.45291722e-01
-5.50689697e-01 4.38695610e-01 3.45579952e-01 -7.04921544e-01
-6.05984367e-02 -3.60097319e-01 -2.90741795e-04 -5.26733458e-01
5.28650522e-01 3.27191800e-01 2.81062573e-01 -1.44838363... | [11.859488487243652, 8.848124504089355] |
8ffe5b1d-668c-48a6-8a26-427a6b745f4f | multi-scale-hourglass-hierarchical-fusion | 2104.12100 | null | https://arxiv.org/abs/2104.12100v2 | https://arxiv.org/pdf/2104.12100v2.pdf | Multi-Scale Hourglass Hierarchical Fusion Network for Single Image Deraining | Rain streaks bring serious blurring and visual quality degradation, which often vary in size, direction and density. Current CNN-based methods achieve encouraging performance, while are limited to depict rain characteristics and recover image details in the poor visibility environment. To address these issues, we prese... | ['Lei Xu', 'Yufeng Huang', 'Xiang Chen'] | 2021-04-25 | null | null | null | null | ['single-image-deraining'] | ['computer-vision'] | [-3.24409544e-01 -5.25357485e-01 4.47060198e-01 -6.16783679e-01
-6.47138715e-01 -2.27863327e-01 2.84355521e-01 -2.25044668e-01
-1.47776172e-01 9.12177682e-01 3.48935157e-01 -1.58510823e-02
-1.32905826e-01 -7.72282839e-01 -5.30225813e-01 -1.07959521e+00
-2.45145991e-01 -2.82065719e-01 2.10251048e-01 -2.77020782... | [10.914379119873047, -3.194096565246582] |
2e09eef9-cae7-4a44-a61c-9313a760b0f4 | instaindoor-and-multi-modal-deep-learning-for | 2112.12409 | null | https://arxiv.org/abs/2112.12409v1 | https://arxiv.org/pdf/2112.12409v1.pdf | InstaIndoor and Multi-modal Deep Learning for Indoor Scene Recognition | Indoor scene recognition is a growing field with great potential for behaviour understanding, robot localization, and elderly monitoring, among others. In this study, we approach the task of scene recognition from a novel standpoint, using multi-modal learning and video data gathered from social media. The accessibilit... | ['Estefania Talavera', 'Andreea Glavan'] | 2021-12-23 | null | null | null | null | ['scene-recognition'] | ['computer-vision'] | [ 3.54573816e-01 -1.87526718e-01 -8.11848864e-02 -5.99636018e-01
-8.98928344e-01 -2.24424124e-01 5.82114279e-01 8.67905617e-02
-5.93308032e-01 6.61592007e-01 4.89020258e-01 7.68613964e-02
4.90007214e-02 -5.18811405e-01 -7.68522084e-01 -5.69280028e-01
-1.62532791e-01 -1.80623040e-01 1.32651612e-01 -1.18345015... | [7.8868560791015625, 0.5383169651031494] |
ea2b677d-70ba-4e7d-b45a-7230821c688f | affine-medical-image-registration-with-coarse | 2203.15216 | null | https://arxiv.org/abs/2203.15216v2 | https://arxiv.org/pdf/2203.15216v2.pdf | Affine Medical Image Registration with Coarse-to-Fine Vision Transformer | Affine registration is indispensable in a comprehensive medical image registration pipeline. However, only a few studies focus on fast and robust affine registration algorithms. Most of these studies utilize convolutional neural networks (CNNs) to learn joint affine and non-parametric registration, while the standalone... | ['Albert C. S. Chung', 'Tony C. W. Mok'] | 2022-03-29 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Mok_Affine_Medical_Image_Registration_With_Coarse-To-Fine_Vision_Transformer_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Mok_Affine_Medical_Image_Registration_With_Coarse-To-Fine_Vision_Transformer_CVPR_2022_paper.pdf | cvpr-2022-1 | ['template-matching'] | ['computer-vision'] | [-1.16531640e-01 -2.18838975e-01 -3.06023061e-01 -5.99701047e-01
-9.62639570e-01 -6.56130910e-01 6.20159268e-01 5.04556857e-02
-5.07542610e-01 1.39091790e-01 3.78299385e-01 2.55811587e-02
-2.74778634e-01 -5.65217853e-01 -5.29364288e-01 -7.12463915e-01
7.55867139e-02 4.83532518e-01 2.24142909e-01 -3.03203672... | [13.958051681518555, -2.599989414215088] |
f868b5ae-44ff-401a-be95-681ebc872a88 | universal-semantic-annotator-the-first | null | null | https://aclanthology.org/2022.lrec-1.282 | https://aclanthology.org/2022.lrec-1.282.pdf | Universal Semantic Annotator: the First Unified API for WSD, SRL and Semantic Parsing | In this paper, we present the Universal Semantic Annotator (USeA), which offers the first unified API for high-quality automatic annotations of texts in 100 languages through state-of-the-art systems for Word Sense Disambiguation, Semantic Role Labeling and Semantic Parsing. Together, such annotations can be used to pr... | ['Roberto Navigli', 'Stefano Faralli', 'Simone Conia', 'Riccardo Orlando'] | null | null | null | null | lrec-2022-6 | ['word-sense-disambiguation', 'semantic-role-labeling'] | ['natural-language-processing', 'natural-language-processing'] | [ 8.98523852e-02 4.72751647e-01 -4.73248243e-01 -4.29522514e-01
-7.16132343e-01 -1.05412102e+00 5.80055535e-01 8.09267342e-01
-8.07433784e-01 7.99573779e-01 3.75880569e-01 -2.88547128e-01
-7.68914297e-02 -6.76180243e-01 -7.69764110e-02 -1.13489345e-01
6.06009901e-01 8.51704240e-01 8.06533277e-01 -8.16608608... | [10.344127655029297, 9.437080383300781] |
48d8dc82-97a6-48b1-ba61-b37cefddcca0 | dynamic-causal-explanation-based-diffusion | 2305.09703 | null | https://arxiv.org/abs/2305.09703v1 | https://arxiv.org/pdf/2305.09703v1.pdf | Dynamic Causal Explanation Based Diffusion-Variational Graph Neural Network for Spatio-temporal Forecasting | Graph neural networks (GNNs), especially dynamic GNNs, have become a research hotspot in spatio-temporal forecasting problems. While many dynamic graph construction methods have been developed, relatively few of them explore the causal relationship between neighbour nodes. Thus, the resulting models lack strong explain... | ['Fernando Alonso-Fernandez', 'Stefan Byttner', 'Sławomir Nowaczyk', 'Prayag Tiwari', 'Guojun Liang'] | 2023-05-16 | null | null | null | null | ['graph-construction', 'spatio-temporal-forecasting'] | ['graphs', 'time-series'] | [-8.43083039e-02 1.38077170e-01 -1.74177080e-01 -1.63123399e-01
1.06304884e-01 -3.17397743e-01 8.05343270e-01 -4.67578173e-02
1.24990225e-01 5.41659057e-01 2.37151057e-01 -5.22470653e-01
-4.68212396e-01 -1.10371196e+00 -8.35390210e-01 -8.70816350e-01
-5.18332183e-01 3.93911690e-01 2.91613549e-01 -1.89815149... | [6.812277317047119, 2.9794228076934814] |
2cd7130b-c066-4ee5-af2b-a0aea6f1b97e | marble-music-audio-representation-benchmark | 2306.10548 | null | https://arxiv.org/abs/2306.10548v2 | https://arxiv.org/pdf/2306.10548v2.pdf | MARBLE: Music Audio Representation Benchmark for Universal Evaluation | In the era of extensive intersection between art and Artificial Intelligence (AI), such as image generation and fiction co-creation, AI for music remains relatively nascent, particularly in music understanding. This is evident in the limited work on deep music representations, the scarcity of large-scale datasets, and ... | ['Roger Dannenbert', 'Norbert Gyenge', 'Anton Ragni', 'Emmanouil Benetos', 'Chenghua Lin', 'Jie Fu', 'Yike Guo', 'Ruibo Liu', 'Shi Wang', 'Si Liu', 'Wei Xue', 'Gus Xia', 'Wenhu Chen', 'Ningzhi Wang', 'Binyue Deng', 'Zeyue Tian', 'Jiawen Huang', 'Yiqi Liu', 'Le Zhuo', 'Hanzhi Yin', 'Xingran Chen', 'Ge Zhang', 'Yizhi Li'... | 2023-06-18 | null | null | null | null | ['music-information-retrieval', 'information-retrieval'] | ['music', 'natural-language-processing'] | [ 4.05045629e-01 -2.86267281e-01 -1.34583309e-01 2.70555556e-01
-1.30986309e+00 -8.35022330e-01 6.83658302e-01 -1.77641228e-01
-2.62443542e-01 4.31915879e-01 7.33641386e-01 1.68838456e-01
-5.09914398e-01 -3.48284483e-01 -5.62486470e-01 -4.67355907e-01
-7.36876354e-02 5.49839854e-01 -3.26592207e-01 -3.13230395... | [15.899913787841797, 5.375647068023682] |
38ae11bd-4eb7-4ce8-9826-c8dd05e1ca3f | beam-search-decoding-using-manner-of | 1811.07720 | null | http://arxiv.org/abs/1811.07720v1 | http://arxiv.org/pdf/1811.07720v1.pdf | Beam Search Decoding using Manner of Articulation Detection Knowledge Derived from Connectionist Temporal Classification | Manner of articulation detection using deep neural networks require a priori
knowledge of the attribute discriminative features or the decent phoneme
alignments. However generating an appropriate phoneme alignment is complex and
its performance depends on the choice of optimal number of senones, Gaussians,
etc. In the ... | ['Sreenivasa Rao K', 'Pradeep Rangan'] | 2018-11-16 | null | null | null | null | ['manner-of-articulation-detection'] | ['speech'] | [ 2.49702290e-01 -1.08507842e-01 8.74314755e-02 -1.86533809e-01
-9.79474068e-01 -6.75965071e-01 7.48472810e-01 -1.01295315e-01
-7.35301256e-01 5.19255221e-01 1.99828595e-01 -3.82383943e-01
1.58938468e-01 -2.99249411e-01 -6.02895617e-01 -7.71095753e-01
2.11912334e-01 6.61007047e-01 4.97497022e-01 -1.04576528... | [14.500338554382324, 6.626946926116943] |
202bb707-33a7-4afe-bc94-7fc04f2aa32f | convolutional-neural-network-cnn-to-reduce | 2209.03475 | null | https://arxiv.org/abs/2209.03475v2 | https://arxiv.org/pdf/2209.03475v2.pdf | Convolutional Neural Network (CNN) to reduce construction loss in JPEG compression caused by Discrete Fourier Transform (DFT) | In recent decades, digital image processing has gained enormous popularity. Consequently, a number of data compression strategies have been put forth, with the goal of minimizing the amount of information required to represent images. Among them, JPEG compression is one of the most popular methods that has been widely ... | ['Suman Kunwar'] | 2022-08-26 | null | null | null | null | ['data-compression'] | ['time-series'] | [ 2.17484087e-01 -3.12161118e-01 -7.18485788e-02 -1.86251104e-01
1.87790692e-01 3.13860297e-01 4.10332620e-01 2.36422345e-01
-4.77232188e-01 5.67372620e-01 2.57737428e-01 2.33582318e-01
-2.07615241e-01 -1.01926470e+00 -3.44192266e-01 -8.68090868e-01
9.41828340e-02 -4.05776739e-01 1.22250013e-01 -1.53907418... | [11.261025428771973, -1.657738447189331] |
0fe5c81a-11c0-4d70-86d4-49c1e5f54d45 | deep-rts-a-game-environment-for-deep | 1808.05032 | null | http://arxiv.org/abs/1808.05032v1 | http://arxiv.org/pdf/1808.05032v1.pdf | Deep RTS: A Game Environment for Deep Reinforcement Learning in Real-Time Strategy Games | Reinforcement learning (RL) is an area of research that has blossomed
tremendously in recent years and has shown remarkable potential for artificial
intelligence based opponents in computer games. This success is primarily due
to the vast capabilities of convolutional neural networks, that can extract
useful features f... | ['Ole-Christoffer Granmo', 'Per-Arne Andersen', 'Morten Goodwin'] | 2018-08-15 | null | null | null | null | ['real-time-strategy-games'] | ['playing-games'] | [-2.61292696e-01 8.83502364e-02 -1.51238471e-01 6.70757815e-02
-5.30309916e-01 -5.78901708e-01 4.00755733e-01 -3.82181555e-01
-1.00095892e+00 8.22369456e-01 -1.94815069e-01 -7.63643384e-01
-3.50342482e-01 -8.73625457e-01 -4.89720106e-01 -6.72061384e-01
-6.72797859e-01 8.94300044e-01 4.29562151e-01 -1.06279576... | [3.592869281768799, 1.4518905878067017] |
3dfdf28e-55a5-464b-8202-bc96709ea3d6 | sentence-classification-with-imbalanced-data | null | null | https://aclanthology.org/2020.smm4h-1.25 | https://aclanthology.org/2020.smm4h-1.25.pdf | Sentence Classification with Imbalanced Data for Health Applications | Identifying and extracting reports of medications, their abuse or adverse effects from social media is a challenging task. In social media, relevant reports are very infrequent, causes imbalanced class distribution for machine learning algorithms. Learning algorithms typically designed to optimize the overall accuracy ... | ['Farhana Ferdousi Liza'] | null | null | null | null | smm4h-coling-2020-12 | ['sentence-classification'] | ['natural-language-processing'] | [ 6.20669834e-02 2.55017638e-01 -6.24154508e-01 -2.10949421e-01
-8.95091116e-01 -3.53423625e-01 3.31104577e-01 9.68847692e-01
-4.84459609e-01 1.20074964e+00 1.37289569e-01 -4.93211262e-02
-2.42666140e-01 -5.79184473e-01 -4.12454665e-01 -2.74243653e-01
-1.00231797e-01 3.82108957e-01 -3.24002326e-01 4.49257977... | [8.459711074829102, 8.869782447814941] |
9f853034-1201-47dd-87ff-26775673b5c1 | improving-contextual-representation-with-1 | 2205.06603 | null | https://arxiv.org/abs/2205.06603v1 | https://arxiv.org/pdf/2205.06603v1.pdf | Improving Contextual Representation with Gloss Regularized Pre-training | Though achieving impressive results on many NLP tasks, the BERT-like masked language models (MLM) encounter the discrepancy between pre-training and inference. In light of this gap, we investigate the contextual representation of pre-training and inference from the perspective of word probability distribution. We disco... | ['Zejun Ma', 'Peihao Wu', 'Zhecheng An', 'Yu Lin'] | 2022-05-13 | null | https://aclanthology.org/2022.findings-naacl.68 | https://aclanthology.org/2022.findings-naacl.68.pdf | findings-naacl-2022-7 | ['word-similarity'] | ['natural-language-processing'] | [ 3.78862351e-01 3.83301169e-01 -4.14465606e-01 -5.97086430e-01
-7.88743973e-01 -2.85424858e-01 5.48036337e-01 1.33563966e-01
-6.91983104e-01 4.61795807e-01 7.85483539e-01 -5.52904546e-01
2.62112498e-01 -7.11405396e-01 -6.48382664e-01 -4.50069934e-01
4.71490294e-01 5.58497667e-01 3.08620542e-01 -2.60061622... | [10.842757225036621, 8.73044490814209] |
6d4f5b76-ac94-41a4-b3b7-32bc3278abad | interactive-control-over-temporal-consistency | 2301.00750 | null | https://arxiv.org/abs/2301.00750v2 | https://arxiv.org/pdf/2301.00750v2.pdf | Interactive Control over Temporal Consistency while Stylizing Video Streams | Image stylization has seen significant advancement and widespread interest over the years, leading to the development of a multitude of techniques. Extending these stylization techniques, such as Neural Style Transfer (NST), to videos is often achieved by applying them on a per-frame basis. However, per-frame stylizati... | ['Matthias Trapp', 'Jürgen Döllner', 'Amir Semmo', 'Moritz Hilscher', 'Max Reimann', 'Sumit Shekhar'] | 2023-01-02 | null | null | null | null | ['image-stylization', 'video-temporal-consistency', 'video-stabilization'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 2.45043457e-01 -4.21807557e-01 -1.24306120e-01 -8.46194252e-02
-2.16782898e-01 -6.39347494e-01 5.78684092e-01 2.57976744e-02
-3.50356549e-01 6.05161250e-01 -1.79985955e-01 -1.21891834e-01
-1.50629193e-01 -5.39241254e-01 -5.91216922e-01 -4.23926800e-01
9.69655216e-02 4.61622849e-02 5.28674781e-01 -2.11084157... | [10.940997123718262, -1.2879600524902344] |
448cee51-4f3b-4d1d-8b5b-0c0eb83bcef9 | dynamic-kernels-and-channel-attention-with | 2211.02000 | null | https://arxiv.org/abs/2211.02000v2 | https://arxiv.org/pdf/2211.02000v2.pdf | Dynamic Kernels and Channel Attention for Low Resource Speaker Verification | State-of-the-art speaker verification frameworks have typically focused on developing models with increasingly deeper (more layers) and wider (number of channels) models to improve their verification performance. Instead, this paper proposes an approach to increase the model resolution capability using attention-based ... | ['Thomas Hain', 'Md Asif Jalal', 'Anna Ollerenshaw'] | 2022-11-03 | null | null | null | null | ['speaker-verification'] | ['speech'] | [-2.35912371e-02 2.86439717e-01 4.45727371e-02 -5.37460268e-01
-7.96390295e-01 -2.83073634e-01 3.60541463e-01 -3.12392294e-01
-5.00370800e-01 5.40392637e-01 2.74419665e-01 -3.27606112e-01
1.42280802e-01 -3.15284431e-01 -4.30010855e-01 -6.19037628e-01
-5.90235963e-02 2.19572019e-02 -4.23116982e-03 -8.10820982... | [14.340503692626953, 6.1243133544921875] |
0c1cf55f-c555-4464-88d3-bc4381f21312 | reducing-sequence-length-by-predicting-edit | 2305.11862 | null | https://arxiv.org/abs/2305.11862v1 | https://arxiv.org/pdf/2305.11862v1.pdf | Reducing Sequence Length by Predicting Edit Operations with Large Language Models | Large Language Models (LLMs) have demonstrated remarkable performance in various tasks and gained significant attention. LLMs are also used for local sequence transduction tasks, including grammatical error correction (GEC) and formality style transfer, where most tokens in a source text are kept unchanged. However, it... | ['Naoaki Okazaki', 'Masahiro Kaneko'] | 2023-05-19 | null | null | null | null | ['style-transfer', 'grammatical-error-correction', 'formality-style-transfer'] | ['computer-vision', 'natural-language-processing', 'natural-language-processing'] | [ 7.75971115e-01 4.28333022e-02 9.58089978e-02 -4.18840647e-01
-7.99550533e-01 -3.08279634e-01 2.17182994e-01 5.38278639e-01
-7.46140480e-01 8.22824001e-01 6.04628287e-02 -4.32542831e-01
5.56519926e-01 -6.78972840e-01 -1.27340436e+00 -3.54043543e-01
2.14173838e-01 3.07621598e-01 2.54091203e-01 -4.36602205... | [11.15947151184082, 10.330574989318848] |
32da5d47-687d-4ee3-8b4f-64efb68e591e | auto-avsr-audio-visual-speech-recognition | 2303.14307 | null | https://arxiv.org/abs/2303.14307v3 | https://arxiv.org/pdf/2303.14307v3.pdf | Auto-AVSR: Audio-Visual Speech Recognition with Automatic Labels | Audio-visual speech recognition has received a lot of attention due to its robustness against acoustic noise. Recently, the performance of automatic, visual, and audio-visual speech recognition (ASR, VSR, and AV-ASR, respectively) has been substantially improved, mainly due to the use of larger models and training sets... | ['Maja Pantic', 'Stavros Petridis', 'Honglie Chen', 'Adriana Fernandez-Lopez', 'Alexandros Haliassos', 'Pingchuan Ma'] | 2023-03-25 | null | null | null | null | ['lipreading', 'audio-visual-speech-recognition'] | ['computer-vision', 'speech'] | [ 3.96112084e-01 1.25099421e-01 1.83259994e-01 -3.36842984e-01
-1.44444442e+00 -5.35744905e-01 6.96659625e-01 1.03221573e-02
-4.38098729e-01 5.97578168e-01 2.41726667e-01 -3.94028127e-01
5.52947879e-01 -1.75378248e-01 -5.12713909e-01 -7.04527497e-01
6.56552613e-01 5.30487239e-01 2.61512280e-01 -1.03407986... | [14.336575508117676, 5.153361797332764] |
57784deb-8e11-4eb5-a3fe-3c6e4372c5f9 | zs-mstm-zero-shot-style-transfer-for-gesture | 2305.12887 | null | https://arxiv.org/abs/2305.12887v1 | https://arxiv.org/pdf/2305.12887v1.pdf | ZS-MSTM: Zero-Shot Style Transfer for Gesture Animation driven by Text and Speech using Adversarial Disentanglement of Multimodal Style Encoding | In this study, we address the importance of modeling behavior style in virtual agents for personalized human-agent interaction. We propose a machine learning approach to synthesize gestures, driven by prosodic features and text, in the style of different speakers, even those unseen during training. Our model incorporat... | ['Nicolas Obin', 'Catherine Pelachaud', 'Mireille Fares'] | 2023-05-22 | null | null | null | null | ['style-transfer', 'disentanglement'] | ['computer-vision', 'methodology'] | [ 2.98789054e-01 3.23207766e-01 -6.62851557e-02 -6.63374484e-01
-4.03728217e-01 -9.91538703e-01 1.07219255e+00 -5.31759262e-01
-3.39121133e-01 4.08797294e-01 9.49321628e-01 4.17421669e-01
3.81535381e-01 -2.88073361e-01 -4.06956077e-01 -4.85635906e-01
1.25648126e-01 6.98846519e-01 -2.89378434e-01 -5.05468369... | [5.615222454071045, -0.11411942541599274] |
0bcf272a-d87c-499a-a83f-f20c05ac6135 | semi-supervised-auto-encoder-graph-network | null | null | https://ieeexplore.ieee.org/abstract/document/9567704 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9567704 | Semi-Supervised Auto-Encoder Graph Network for Diabetic Retinopathy Grading | Diabetic Retinopathy (DR) causes quite a few blindness worldwide, which can be refrained by the timely diagnosis on retinal images. Recently, researches on deep learning-based retinal image classification have accelerated outstanding improvements in DR grading task. However, existing DR grading works are mostly limited... | ['Wenpei Kang', 'Sungtae Jung', 'Sunkyoung Kang', 'Zhang Song', 'Yujie Li'] | 2021-10-11 | null | null | null | ieee-2021-10 | ['diabetic-retinopathy-grading'] | ['medical'] | [-9.73277315e-02 2.02287495e-01 -2.28245661e-01 -6.62164867e-01
-4.54468250e-01 -9.90435407e-02 1.25444546e-01 -2.64066786e-01
-1.10093504e-01 7.04609811e-01 2.90606886e-01 -2.48165175e-01
-2.87307769e-01 -8.61429870e-01 -1.77533969e-01 -7.01116264e-01
2.35323325e-01 2.68173665e-01 1.28692195e-01 6.57608211... | [15.800296783447266, -3.9700846672058105] |
59043bce-6271-4758-aef2-5b0c60492815 | mind-reasoning-manners-enhancing-type | 2301.02983 | null | https://arxiv.org/abs/2301.02983v1 | https://arxiv.org/pdf/2301.02983v1.pdf | Mind Reasoning Manners: Enhancing Type Perception for Generalized Zero-shot Logical Reasoning over Text | Logical reasoning task involves diverse types of complex reasoning over text, based on the form of multiple-choice question answering. Given the context, question and a set of options as the input, previous methods achieve superior performances on the full-data setting. However, the current benchmark dataset has the id... | ['Lingling Zhang', 'Jian Zhang', 'Tianzhe Zhao', 'Qika Lin', 'Jun Liu', 'Fangzhi Xu'] | 2023-01-08 | null | null | null | null | ['logical-reasoning', 'type'] | ['reasoning', 'speech'] | [ 1.37949735e-01 3.22260052e-01 -2.98195273e-01 -4.95104700e-01
-7.00724542e-01 -2.88316548e-01 2.78892487e-01 -8.62770751e-02
-2.39966854e-01 6.26325488e-01 1.04179785e-01 -4.74481165e-01
-5.53338885e-01 -1.17481256e+00 -5.18694937e-01 -4.42153037e-01
7.06812203e-01 7.49444008e-01 7.64385402e-01 -7.46262670... | [9.883309364318848, 7.565319538116455] |
e0958397-6c39-4001-ad9c-c6897a543352 | query-based-instance-discrimination-network | 2211.01797 | null | https://arxiv.org/abs/2211.01797v1 | https://arxiv.org/pdf/2211.01797v1.pdf | Query-based Instance Discrimination Network for Relational Triple Extraction | Joint entity and relation extraction has been a core task in the field of information extraction. Recent approaches usually consider the extraction of relational triples from a stereoscopic perspective, either learning a relation-specific tagger or separate classifiers for each relation type. However, they still suffer... | ['Yueting Zhuang', 'Weiming Lu', 'Xiaoxia Cheng', 'Wenqi Zhang', 'Xuming Hu', 'Yongliang Shen', 'Zeqi Tan'] | 2022-11-03 | null | null | null | null | ['joint-entity-and-relation-extraction'] | ['natural-language-processing'] | [-1.98174253e-01 3.11221302e-01 -5.57775676e-01 -3.72944683e-01
-7.04982042e-01 -4.06911314e-01 6.92684472e-01 9.05441701e-01
-4.01388705e-01 6.39223814e-01 1.77499712e-01 -8.11965540e-02
-2.78303593e-01 -1.36112761e+00 -7.58164763e-01 -4.71056730e-01
-1.10647097e-01 5.15886128e-01 5.57901502e-01 -1.58221200... | [8.882803916931152, 7.9945855140686035] |
e0db9046-52ba-4b43-a63f-66f3358beed1 | framerank-a-text-processing-approach-to-video | 1904.05544 | null | http://arxiv.org/abs/1904.05544v2 | http://arxiv.org/pdf/1904.05544v2.pdf | FrameRank: A Text Processing Approach to Video Summarization | Video summarization has been extensively studied in the past decades.
However, user-generated video summarization is much less explored since there
lack large-scale video datasets within which human-generated video summaries
are unambiguously defined and annotated. Toward this end, we propose a
user-generated video sum... | ['Qian Zhang', 'Guoping Qiu', 'Zhuo Lei', 'Chao Zhang'] | 2019-04-11 | null | null | null | null | ['unsupervised-video-summarization'] | ['computer-vision'] | [ 2.70935953e-01 1.96295734e-02 -3.74091297e-01 -1.79415777e-01
-1.01339078e+00 -5.93074858e-01 5.36091506e-01 4.27489817e-01
-2.38725051e-01 7.41875291e-01 9.87458825e-01 2.15863585e-01
1.57890648e-01 -3.08792055e-01 -5.76272666e-01 -3.58465701e-01
-2.51834631e-01 -8.18884000e-02 5.41578531e-01 1.44857779... | [10.434121131896973, 0.49073442816734314] |
1a668995-263f-4f44-a196-33f927115d87 | bag-of-color-features-for-color-constancy | 1906.04445 | null | https://arxiv.org/abs/1906.04445v1 | https://arxiv.org/pdf/1906.04445v1.pdf | Bag of Color Features For Color Constancy | In this paper, we propose a novel color constancy approach, called Bag of Color Features (BoCF), building upon Bag-of-Features pooling. The proposed method substantially reduces the number of parameters needed for illumination estimation. At the same time, the proposed method is consistent with the color constancy assu... | ['Alexandros Iosifidis', 'Nikolaos Passalis', 'Moncef Gabbouj', 'Jenni Raitoharju', 'Jarno Nikkanen', 'Firas Laakom', 'Anastasios Tefas'] | 2019-06-11 | null | null | null | null | ['color-constancy'] | ['computer-vision'] | [-1.09963328e-01 -5.94691753e-01 5.60590029e-02 -5.88570952e-01
-4.75244820e-01 -3.45188797e-01 5.86911201e-01 1.38178095e-01
-4.76950377e-01 6.71134531e-01 -3.06467749e-02 -7.26367012e-02
7.37774149e-02 -6.55689538e-01 -6.90675735e-01 -9.05978024e-01
2.23192364e-01 -2.57886082e-01 3.29627484e-01 3.62626393... | [10.467157363891602, -2.5729849338531494] |
1b6e22c9-eee5-4fcd-9b61-9261635cae93 | evolutionary-game-theoretical-analysis-for | 2206.11114 | null | https://arxiv.org/abs/2206.11114v1 | https://arxiv.org/pdf/2206.11114v1.pdf | Evolutionary Game-Theoretical Analysis for General Multiplayer Asymmetric Games | Evolutionary game theory has been a successful tool to combine classical game theory with learning-dynamical descriptions in multiagent systems. Provided some symmetric structures of interacting players, many studies have been focused on using a simplified heuristic payoff table as input to analyse the dynamics of inte... | ['Wenxin Li', 'Haifeng Wang', 'Yushan Zhou', 'Peng Peng', 'Xinyu Zhang'] | 2022-06-22 | null | null | null | null | ['starcraft-ii'] | ['playing-games'] | [-2.61635572e-01 1.69953872e-02 4.99869347e-01 3.96687597e-01
-6.45047352e-02 -5.87702990e-01 3.99486303e-01 1.35829285e-01
-8.26503396e-01 1.19771004e+00 -6.03239954e-01 -3.80381048e-01
-7.02900529e-01 -1.06260061e+00 -2.51554877e-01 -7.17631340e-01
-4.79222983e-01 7.99791157e-01 5.03305614e-01 -1.19801927... | [3.55193829536438, 1.7414524555206299] |
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