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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
e7d274de-3f56-4485-ba4c-7a36650acd63 | journeydb-a-benchmark-for-generative-image | 2307.00716 | null | https://arxiv.org/abs/2307.00716v1 | https://arxiv.org/pdf/2307.00716v1.pdf | JourneyDB: A Benchmark for Generative Image Understanding | While recent advancements in vision-language models have revolutionized multi-modal understanding, it remains unclear whether they possess the capabilities of comprehending the generated images. Compared to real data, synthetic images exhibit a higher degree of diversity in both content and style, for which there are s... | ['Hongsheng Li', 'Yu Qiao', 'Jifeng Dai', 'Yi Wang', 'Zipeng Qin', 'Aojun Zhou', 'Renrui Zhang', 'Xiaoshi Wu', 'Haodong Duan', 'Hao Li', 'Yuying Ge', 'Keqiang Sun', 'Junting Pan'] | 2023-07-03 | null | null | null | null | ['visual-question-answering-1', 'image-captioning', 'retrieval', 'question-answering'] | ['computer-vision', 'computer-vision', 'methodology', 'natural-language-processing'] | [ 3.58096600e-01 2.30198905e-01 1.33814469e-01 -6.23517573e-01
-1.07073414e+00 -8.49734366e-01 9.29495931e-01 -1.96211070e-01
1.16667099e-01 4.54072833e-01 5.69548786e-01 -2.55283266e-01
2.16828749e-01 -7.70193040e-01 -1.07754135e+00 -2.84129739e-01
5.41662395e-01 6.07386649e-01 -1.80521369e-01 -2.81863242... | [11.031265258789062, 1.3205134868621826] |
b7ceb855-15ca-4e61-8870-bf9d15898a59 | densely-deformable-efficient-salient-object | 2102.06407 | null | https://arxiv.org/abs/2102.06407v1 | https://arxiv.org/pdf/2102.06407v1.pdf | Densely Deformable Efficient Salient Object Detection Network | Salient Object Detection (SOD) domain using RGB-D data has lately emerged with some current models' adequately precise results. However, they have restrained generalization abilities and intensive computational complexity. In this paper, inspired by the best background/foreground separation abilities of deformable conv... | ['Sung Wook Baik', 'Khan Muhammad', 'Amin Ullah', 'Saeed Anwar', 'Tanveer Hussain'] | 2021-02-12 | null | null | null | null | ['rgb-d-salient-object-detection', 'salient-object-detection'] | ['computer-vision', 'computer-vision'] | [ 3.25434297e-01 2.88890123e-01 7.53892213e-02 -3.50087762e-01
-2.93140382e-01 -4.12589341e-01 4.68441039e-01 -4.00227755e-01
-3.84801537e-01 7.53995299e-01 2.40624279e-01 -1.75528765e-01
-3.41630429e-02 -5.39113581e-01 -6.66635752e-01 -7.79690981e-01
-9.12651345e-02 -1.58454463e-01 9.77397919e-01 -5.26050448... | [9.776304244995117, -0.4616542160511017] |
db25b031-cc35-4f69-85c9-900d3b767d1f | deformirisnet-an-identity-preserving-model-of | 2207.08980 | null | https://arxiv.org/abs/2207.08980v2 | https://arxiv.org/pdf/2207.08980v2.pdf | DeformIrisNet: An Identity-Preserving Model of Iris Texture Deformation | Nonlinear iris texture deformations due to pupil size variations are one of the main factors responsible for within-class variance of genuine comparison scores in iris recognition. In dominant approaches to iris recognition, the size of a ring-shaped iris region is linearly scaled to a canonical rectangle, used further... | ['Adam Czajka', 'Patrick Tinsley', 'Siamul Karim Khan'] | 2022-07-18 | null | null | null | null | ['pupil-dilation'] | ['computer-vision'] | [ 2.41975278e-01 3.11305914e-02 -1.53314963e-01 -2.89572597e-01
7.86607563e-02 -5.14398456e-01 1.59038514e-01 -3.46465111e-01
-1.45094037e-01 2.74045199e-01 2.97788709e-01 -1.91234633e-01
-5.15764177e-01 -6.62031531e-01 -6.74371183e-01 -9.48499799e-01
5.84992906e-03 3.40921074e-01 -3.64136696e-01 -8.47596750... | [3.7424685955047607, -3.632662773132324] |
dc9c85b1-2004-44a6-8f7c-3a6279cdf2d2 | reducing-conservativeness-oriented-offline | 2103.00098 | null | https://arxiv.org/abs/2103.00098v1 | https://arxiv.org/pdf/2103.00098v1.pdf | Reducing Conservativeness Oriented Offline Reinforcement Learning | In offline reinforcement learning, a policy learns to maximize cumulative rewards with a fixed collection of data. Towards conservative strategy, current methods choose to regularize the behavior policy or learn a lower bound of the value function. However, exorbitant conservation tends to impair the policy's generaliz... | ['Xiangyang Ji', 'Shuncheng He', 'Yuhang Jiang', 'Jianzhun Shao', 'Hongchang Zhang'] | 2021-02-27 | null | null | null | null | ['d4rl'] | ['robots'] | [-1.38210759e-01 3.33818823e-01 -7.80390680e-01 -3.22111517e-01
-6.82155907e-01 -3.52828056e-01 1.47570014e-01 3.07996333e-01
-6.49756014e-01 1.25381851e+00 -1.75922409e-01 -1.83235288e-01
-2.75532335e-01 -8.20974767e-01 -7.47587323e-01 -1.01538622e+00
-9.36024636e-02 5.69444835e-01 5.73577061e-02 -2.97242314... | [4.115314960479736, 2.3282577991485596] |
29f11db7-a91a-4926-a03e-f6ab9475b7eb | a-sentence-interaction-network-for-modeling | null | null | https://aclanthology.org/P16-1053 | https://aclanthology.org/P16-1053.pdf | A Sentence Interaction Network for Modeling Dependence between Sentences | null | ['Minlie Huang', 'Biao Liu'] | 2016-08-01 | null | null | null | acl-2016-8 | ['sentence-pair-modeling'] | ['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... | [-7.479804992675781, 3.632047653198242] |
6b5fb9ef-4941-4876-8bd8-0daf801feb48 | 190909910 | 1909.09910 | null | https://arxiv.org/abs/1909.09910v2 | https://arxiv.org/pdf/1909.09910v2.pdf | Deep learning approach to control of prosthetic hands with electromyography signals | Natural muscles provide mobility in response to nerve impulses. Electromyography (EMG) measures the electrical activity of muscles in response to a nerve's stimulation. In the past few decades, EMG signals have been used extensively in the identification of user intention to potentially control assistive devices such a... | ['Mohsen Jafarzadeh', 'Daniel Curtiss Hussey', 'Yonas Tadesse'] | 2019-09-21 | null | null | null | null | ['electromyography-emg'] | ['medical'] | [ 8.04576948e-02 -1.02115117e-01 -3.81003439e-01 -1.24789648e-01
-9.48762894e-03 -2.90834427e-01 -2.97898483e-02 -9.42959905e-01
-5.67822218e-01 7.12935388e-01 1.75788984e-01 -1.78537920e-01
-1.09877087e-01 -7.17912734e-01 -5.47512770e-01 -5.85078239e-01
4.16186377e-02 1.56934336e-02 1.14835136e-01 -3.40402335... | [6.814927101135254, 0.17762640118598938] |
05c9fbbf-cf2f-423a-b216-1eb03e555e69 | towards-view-invariant-vehicle-speed | 2206.00343 | null | https://arxiv.org/abs/2206.00343v2 | https://arxiv.org/pdf/2206.00343v2.pdf | Towards view-invariant vehicle speed detection from driving simulator images | The use of cameras for vehicle speed measurement is much more cost effective compared to other technologies such as inductive loops, radar or laser. However, accurate speed measurement remains a challenge due to the inherent limitations of cameras to provide accurate range estimates. In addition, classical vision-based... | ['Iván García Daza', 'David Fernandez Llorca', 'Antonio Hernández Martínez'] | 2022-06-01 | null | null | null | null | ['vehicle-speed-estimation'] | ['computer-vision'] | [ 1.00023281e-02 -3.03218246e-01 -1.72129542e-01 -5.14763534e-01
-5.05982459e-01 -5.43408215e-01 7.72489846e-01 -1.98196724e-01
-7.49836981e-01 5.78226566e-01 -5.15995800e-01 -3.55968386e-01
1.40353162e-02 -9.18479085e-01 -9.51894343e-01 -6.52883887e-01
2.17063829e-01 6.92684948e-01 2.75698125e-01 -4.14495468... | [7.958740234375, -1.3945425748825073] |
12e83557-e797-4c5b-80d0-003674e68450 | orthogonal-attention-a-cloze-style-approach | 2103.04294 | null | https://arxiv.org/abs/2103.04294v1 | https://arxiv.org/pdf/2103.04294v1.pdf | Orthogonal Attention: A Cloze-Style Approach to Negation Scope Resolution | Negation Scope Resolution is an extensively researched problem, which is used to locate the words affected by a negation cue in a sentence. Recent works have shown that simply finetuning transformer-based architectures yield state-of-the-art results on this task. In this work, we look at Negation Scope Resolution as a ... | ['Vahida Attar', 'Aditya Khandelwal'] | 2021-03-07 | null | null | null | null | ['negation-scope-resolution'] | ['natural-language-processing'] | [ 3.56994003e-01 2.62431409e-02 -2.66514719e-01 -3.29687655e-01
-8.64957988e-01 -2.99027473e-01 4.32897925e-01 1.28603995e-01
-7.38500416e-01 8.87499571e-01 5.55386364e-01 -1.77427813e-01
1.43451616e-01 -5.45909107e-01 -9.38983679e-01 -2.78744072e-01
2.82345951e-01 3.41913998e-01 1.58639699e-01 -8.66289020... | [8.780658721923828, 8.799766540527344] |
0466f568-917a-45ad-ae4c-74669c615bce | understanding-and-mitigating-copying-in | 2305.20086 | null | https://arxiv.org/abs/2305.20086v1 | https://arxiv.org/pdf/2305.20086v1.pdf | Understanding and Mitigating Copying in Diffusion Models | Images generated by diffusion models like Stable Diffusion are increasingly widespread. Recent works and even lawsuits have shown that these models are prone to replicating their training data, unbeknownst to the user. In this paper, we first analyze this memorization problem in text-to-image diffusion models. While it... | ['Tom Goldstein', 'Jonas Geiping', 'Micah Goldblum', 'Vasu Singla', 'Gowthami Somepalli'] | 2023-05-31 | null | null | null | null | ['image-captioning', 'memorization'] | ['computer-vision', 'natural-language-processing'] | [ 4.48416501e-01 2.65335798e-01 -2.47218564e-01 -9.32376757e-02
-4.06614095e-01 -8.39421749e-01 1.05805695e+00 1.59645960e-01
-5.11080444e-01 9.43757296e-01 3.11221927e-01 -6.11620009e-01
7.86276013e-02 -6.78474903e-01 -1.14189625e+00 -8.22711945e-01
1.79064840e-01 5.47066808e-01 1.78741530e-01 1.13951616... | [11.443683624267578, -0.2229757308959961] |
5691eec6-3be3-4cde-a878-dfc540184494 | machine-unlearning-via-gan | 2111.11869 | null | https://arxiv.org/abs/2111.11869v1 | https://arxiv.org/pdf/2111.11869v1.pdf | Machine unlearning via GAN | Machine learning models, especially deep models, may unintentionally remember information about their training data. Malicious attackers can thus pilfer some property about training data by attacking the model via membership inference attack or model inversion attack. Some regulations, such as the EU's GDPR, have enact... | ['Yiwen Wang', 'Yao Huang', 'Kongyang Chen'] | 2021-11-22 | null | null | null | null | ['membership-inference-attack'] | ['computer-vision'] | [ 1.31950229e-01 5.34641027e-01 -2.84861028e-01 -3.13986510e-01
-4.13741022e-01 -7.96467066e-01 4.60867643e-01 -2.87064970e-01
-5.72859406e-01 1.06738734e+00 -7.09152371e-02 -6.76186502e-01
2.97451288e-01 -1.09670651e+00 -7.59678125e-01 -6.01198375e-01
3.57721061e-01 2.09570661e-01 -3.45209002e-01 2.89942890... | [5.9437055587768555, 7.143743991851807] |
fa662589-4bea-45df-8e3e-314b13c4037a | action-anticipation-by-predicting-future | 1808.00141 | null | http://arxiv.org/abs/1808.00141v1 | http://arxiv.org/pdf/1808.00141v1.pdf | Action Anticipation By Predicting Future Dynamic Images | Human action-anticipation methods predict what is the future action by
observing only a few portion of an action in progress. This is critical for
applications where computers have to react to human actions as early as
possible such as autonomous driving, human-robotic interaction, assistive
robotics among others. In t... | ['Basura Fernando', 'Hongdong Li', 'Cristian Rodriguez'] | 2018-08-01 | null | null | null | null | ['action-anticipation'] | ['computer-vision'] | [ 2.80011654e-01 5.82470715e-01 -4.84056860e-01 -3.21526051e-01
-3.93663853e-01 -1.65460035e-01 9.63853717e-01 -2.41994843e-01
-6.07698143e-01 8.62658978e-01 6.30854249e-01 -9.83695760e-02
2.62429953e-01 -3.67239237e-01 -7.23608494e-01 -3.31849396e-01
-3.48890901e-01 5.28585017e-01 5.09522617e-01 -2.38826230... | [7.827841758728027, 0.40753284096717834] |
42263546-08d4-4518-b8dc-23f2f24a36c2 | tie-a-framework-for-embedding-based | 2104.08419 | null | https://arxiv.org/abs/2104.08419v3 | https://arxiv.org/pdf/2104.08419v3.pdf | TIE: A Framework for Embedding-based Incremental Temporal Knowledge Graph Completion | Reasoning in a temporal knowledge graph (TKG) is a critical task for information retrieval and semantic search. It is particularly challenging when the TKG is updated frequently. The model has to adapt to changes in the TKG for efficient training and inference while preserving its performance on historical knowledge. R... | ['Jackie Chi Kit Cheung', 'Mark Coates', 'Chen Ma', 'Yingxue Zhang', 'Yishi Xu', 'Jiapeng Wu'] | 2021-04-17 | null | null | null | null | ['temporal-knowledge-graph-completion'] | ['knowledge-base'] | [-7.36985728e-02 2.53938109e-01 -4.68897581e-01 -2.00115010e-01
-4.83215332e-01 -3.92435044e-01 5.92828870e-01 3.26858610e-01
-4.33100045e-01 9.36365008e-01 4.27587122e-01 -3.07964206e-01
-4.76957142e-01 -7.79217243e-01 -9.47361648e-01 -4.58526105e-01
-3.09587598e-01 4.93927985e-01 2.12115422e-01 -1.24280415... | [8.613642692565918, 7.867116451263428] |
c588ca27-817c-4984-8808-bd33847c29ef | joint-layout-analysis-character-detection-and | 2007.06890 | null | https://arxiv.org/abs/2007.06890v1 | https://arxiv.org/pdf/2007.06890v1.pdf | Joint Layout Analysis, Character Detection and Recognition for Historical Document Digitization | In this paper, we propose an end-to-end trainable framework for restoring historical documents content that follows the correct reading order. In this framework, two branches named character branch and layout branch are added behind the feature extraction network. The character branch localizes individual characters in... | ['Weihong Ma', 'Lianwen Jin', 'Hesuo Zhang', 'Sihang Wu', 'Yongpan Wang', 'Jiapeng Wang'] | 2020-07-14 | null | null | null | null | ['line-detection'] | ['computer-vision'] | [ 5.02755821e-01 -3.43357652e-01 -1.00809038e-01 -4.18801457e-01
-5.69153547e-01 -7.59411693e-01 4.41608131e-01 -8.72537047e-02
-3.22106928e-01 2.55066723e-01 1.42945245e-01 -4.59714323e-01
8.01915973e-02 -9.33472514e-01 -8.32853198e-01 -5.04452169e-01
5.31556904e-01 4.88059223e-02 4.32559490e-01 3.81958857... | [11.928507804870605, 2.229860544204712] |
dcf09d35-9ed3-4487-b696-37bba1282e3f | split-localized-conformal-prediction | 2206.13092 | null | https://arxiv.org/abs/2206.13092v2 | https://arxiv.org/pdf/2206.13092v2.pdf | Split Localized Conformal Prediction | Conformal prediction is a simple and powerful tool that can quantify uncertainty without any distributional assumptions. Many existing methods only address the average coverage guarantee, which is not ideal compared to the stronger conditional coverage guarantee. Existing methods of approximating conditional coverage r... | ['Qiang Liu', 'Joydeep Ghosh', 'Ziyang Tang', 'Xing Han'] | 2022-06-27 | null | null | null | null | ['prediction-intervals'] | ['miscellaneous'] | [-7.29355663e-02 2.74576366e-01 -5.81077456e-01 -5.09714544e-01
-1.30116868e+00 -5.23953497e-01 6.14315450e-01 2.88491547e-01
-1.58185840e-01 1.06898057e+00 4.84991670e-01 -1.64584666e-01
-3.03578138e-01 -1.05035436e+00 -7.97948241e-01 -6.58809066e-01
-9.51752663e-02 5.20190418e-01 6.31714284e-01 2.01006815... | [7.882724285125732, 4.429717540740967] |
fcc350a2-7096-4eb5-8f51-402061b97a53 | a-survey-on-phrase-structure-learning-methods | 1406.5598 | null | http://arxiv.org/abs/1406.5598v1 | http://arxiv.org/pdf/1406.5598v1.pdf | A survey on phrase structure learning methods for text classification | Text classification is a task of automatic classification of text into one of
the predefined categories. The problem of text classification has been widely
studied in different communities like natural language processing, data mining
and information retrieval. Text classification is an important constituent in
many in... | ['Mary Priya Sebastian', 'Reshma Prasad'] | 2014-06-21 | null | null | null | null | ['genre-classification'] | ['computer-vision'] | [ 5.90330958e-01 -8.35356191e-02 -5.14968157e-01 -3.33269268e-01
-4.23574150e-01 -6.13752842e-01 7.33719647e-01 1.03011513e+00
-3.34013581e-01 8.52659702e-01 4.00506318e-01 -7.86330998e-01
-4.33444470e-01 -9.05436516e-01 1.45538002e-01 -6.34275556e-01
-3.03427950e-02 8.62151444e-01 3.19571465e-01 -2.36792102... | [10.498367309570312, 8.375215530395508] |
795a1a0a-6d9d-427a-bc02-b44c5bb47b5c | samscore-a-semantic-structural-similarity | 2305.15367 | null | https://arxiv.org/abs/2305.15367v1 | https://arxiv.org/pdf/2305.15367v1.pdf | SAMScore: A Semantic Structural Similarity Metric for Image Translation Evaluation | Image translation has wide applications, such as style transfer and modality conversion, usually aiming to generate images having both high degrees of realism and faithfulness. These problems remain difficult, especially when it is important to preserve semantic structures. Traditional image-level similarity metrics ar... | ['You Zhang', 'Alan C. Bovik', 'Jun Ma', 'Kai Wang', 'Wenxuan Yang', 'Meixu Chen', 'Yunxiang Li'] | 2023-05-24 | null | null | null | null | ['style-transfer', 'semantic-textual-similarity', 'semantic-similarity'] | ['computer-vision', 'natural-language-processing', 'natural-language-processing'] | [ 5.08623481e-01 -7.18948767e-02 -2.98084825e-01 -3.52832139e-01
-8.80907476e-01 -7.09966004e-01 8.85959685e-01 1.29480273e-01
-3.39670151e-01 6.43923938e-01 4.11753446e-01 -1.36898279e-01
2.87882119e-01 -6.74142122e-01 -7.13393807e-01 -3.90566617e-01
3.97827625e-01 4.17408139e-01 2.05368742e-01 -4.54944789... | [11.494417190551758, -0.024170823395252228] |
3770f5c3-da93-4801-9a08-7d8ef7606646 | sequential-diagnosis-by-abstraction | 1401.3892 | null | http://arxiv.org/abs/1401.3892v1 | http://arxiv.org/pdf/1401.3892v1.pdf | Sequential Diagnosis by Abstraction | When a system behaves abnormally, sequential diagnosis takes a sequence of
measurements of the system until the faults causing the abnormality are
identified, and the goal is to reduce the diagnostic cost, defined here as the
number of measurements. To propose measurement points, previous work employs a
heuristic based... | ['Sajjad Ahmed Siddiqi', 'Jinbo Huang'] | 2014-01-16 | null | null | null | null | ['sequential-diagnosis'] | ['medical'] | [ 3.26491147e-01 5.83190024e-01 -1.35598570e-01 -1.60480246e-01
-6.46200001e-01 -4.13912535e-01 2.14710355e-01 2.03098044e-01
4.31982666e-01 5.47771811e-01 -4.26123977e-01 -6.93507195e-01
-5.63904881e-01 -1.09185064e+00 -3.52698833e-01 -5.67863762e-01
-1.73480242e-01 9.48651552e-01 8.44721258e-01 5.10823391... | [5.375762462615967, 2.7212107181549072] |
903b602f-6622-44d5-bb7f-b151a21307f7 | matrix-recovery-using-split-bregman | 1312.6872 | null | http://arxiv.org/abs/1312.6872v1 | http://arxiv.org/pdf/1312.6872v1.pdf | Matrix recovery using Split Bregman | In this paper we address the problem of recovering a matrix, with inherent
low rank structure, from its lower dimensional projections. This problem is
frequently encountered in wide range of areas including pattern recognition,
wireless sensor networks, control systems, recommender systems, image/video
reconstruction e... | ['Ankita Shukla', 'Anupriya Gogna', 'Angshul Majumdar'] | 2013-12-17 | null | null | null | null | ['video-reconstruction'] | ['computer-vision'] | [ 6.92969024e-01 -2.57799119e-01 -3.37203592e-02 -1.58711020e-02
-6.10729039e-01 -5.22605658e-01 4.06886965e-01 -6.01012073e-02
-5.41598856e-01 1.02366352e+00 4.73083735e-01 -7.42858574e-02
-7.37235188e-01 -6.66801691e-01 -4.30059642e-01 -9.04760957e-01
-2.25205317e-01 3.89355958e-01 -3.52075659e-02 -1.02483958... | [7.0486159324646, 4.577120780944824] |
89d0d111-7b02-4337-854e-1a0f2b591630 | multi-task-temporal-shift-attention-networks | 2006.03790 | null | https://arxiv.org/abs/2006.03790v2 | https://arxiv.org/pdf/2006.03790v2.pdf | Multi-Task Temporal Shift Attention Networks for On-Device Contactless Vitals Measurement | Telehealth and remote health monitoring have become increasingly important during the SARS-CoV-2 pandemic and it is widely expected that this will have a lasting impact on healthcare practices. These tools can help reduce the risk of exposing patients and medical staff to infection, make healthcare services more access... | ['Josh Fromm', 'Shwetak Patel', 'Xin Liu', 'Daniel McDuff'] | 2020-06-06 | null | http://proceedings.neurips.cc/paper/2020/hash/e1228be46de6a0234ac22ded31417bc7-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/e1228be46de6a0234ac22ded31417bc7-Paper.pdf | neurips-2020-12 | ['photoplethysmography-ppg-heart-rate'] | ['medical'] | [ 3.60485166e-01 -2.36337006e-01 1.05335504e-01 -3.34143102e-01
-9.30364490e-01 -5.21977901e-01 1.04181536e-01 4.68223244e-01
-6.72233462e-01 5.02096057e-01 2.94804275e-01 -7.57536948e-01
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-2.79301494e-01 3.13685328e-01 8.57944563e-02 2.43359923... | [13.929140090942383, 3.0280354022979736] |
15d9690c-6c2c-4593-9139-5da1da224629 | corgi-content-rich-graph-neural-networks-with | 2110.04866 | null | https://arxiv.org/abs/2110.04866v1 | https://arxiv.org/pdf/2110.04866v1.pdf | CoRGi: Content-Rich Graph Neural Networks with Attention | Graph representations of a target domain often project it to a set of entities (nodes) and their relations (edges). However, such projections often miss important and rich information. For example, in graph representations used in missing value imputation, items - represented as nodes - may contain rich textual informa... | ['Miltiadis Allamanis', 'Cheng Zheng', 'Simon Peyton Jones', 'Simon Woodhead', 'Angus Lamb', 'Jooyeon Kim'] | 2021-10-10 | null | null | null | null | ['value-prediction'] | ['computer-code'] | [ 3.04208934e-01 8.07194531e-01 -6.44757271e-01 -3.69669110e-01
-1.28596500e-01 -2.62404412e-01 4.29971009e-01 7.04666078e-01
-5.33175431e-02 6.40445292e-01 1.04768836e+00 -2.18653902e-01
-1.33803278e-01 -1.19991016e+00 -7.97853470e-01 -2.53108442e-01
-1.52799889e-01 5.10768592e-01 -4.55829114e-01 -3.29016417... | [8.03140926361084, 6.833065509796143] |
dbc297ae-85df-440e-80e5-af8edfde303b | parallel-sentence-level-explanation | 2302.10707 | null | https://arxiv.org/abs/2302.10707v1 | https://arxiv.org/pdf/2302.10707v1.pdf | Parallel Sentence-Level Explanation Generation for Real-World Low-Resource Scenarios | In order to reveal the rationale behind model predictions, many works have exploited providing explanations in various forms. Recently, to further guarantee readability, more and more works turn to generate sentence-level human language explanations. However, current works pursuing sentence-level explanations rely heav... | ['Qi Dai', 'Xiaokang Chen', 'Yan Liu'] | 2023-02-21 | null | null | null | null | ['explanation-generation'] | ['natural-language-processing'] | [ 5.43807805e-01 1.03750408e+00 -4.44483370e-01 -7.05047667e-01
-5.09325862e-01 -1.92180470e-01 5.73108137e-01 1.70438170e-01
1.11996412e-01 8.85003269e-01 3.88228416e-01 -7.90892601e-01
-1.71542335e-02 -5.80919564e-01 -5.15937924e-01 1.80465039e-02
2.96320438e-01 5.70019007e-01 -1.67620853e-01 -2.47373387... | [9.450061798095703, 6.679744243621826] |
af7db1a1-4195-4ff4-b8e8-ad0255113291 | ieee-big-data-cup-2022-privacy-preserving | 2211.11565 | null | https://arxiv.org/abs/2211.11565v1 | https://arxiv.org/pdf/2211.11565v1.pdf | IEEE Big Data Cup 2022: Privacy Preserving Matching of Encrypted Images with Deep Learning | Smart sensors, devices and systems deployed in smart cities have brought improved physical protections to their citizens. Enhanced crime prevention, and fire and life safety protection are achieved through these technologies that perform motion detection, threat and actors profiling, and real-time alerts. However, an i... | ['Vrizlynn L. L. Thing'] | 2022-11-18 | null | null | null | null | ['motion-detection'] | ['computer-vision'] | [ 2.21100956e-01 2.52807550e-02 6.85119852e-02 -4.85370725e-01
-5.96917391e-01 -6.59421146e-01 7.45406687e-01 2.99836218e-01
-8.04615796e-01 4.76639658e-01 5.26582599e-01 -3.04756016e-01
4.38791662e-02 -1.09402454e+00 -3.11335176e-01 -6.65849328e-01
4.12611067e-02 -1.52400851e-01 7.52881095e-02 -6.39810935... | [12.633734703063965, 0.8388638496398926] |
381dc9da-5398-455c-9e96-b3a2919b3aa1 | a-survey-on-distributed-evolutionary | 2304.05811 | null | https://arxiv.org/abs/2304.05811v1 | https://arxiv.org/pdf/2304.05811v1.pdf | A Survey on Distributed Evolutionary Computation | The rapid development of parallel and distributed computing paradigms has brought about great revolution in computing. Thanks to the intrinsic parallelism of evolutionary computation (EC), it is natural to implement EC on parallel and distributed computing systems. On the one hand, the computing power provided by paral... | ['Jun Zhang', 'Kay Chen Tan', 'Tian-Fang Zhao', 'Feng-Feng Wei', 'Wei-neng Chen'] | 2023-04-12 | null | null | null | null | ['distributed-optimization'] | ['methodology'] | [-3.61072749e-01 -7.65884995e-01 1.72383651e-01 -1.30015731e-01
-1.33839594e-02 -3.54249835e-01 8.33091885e-02 9.00686011e-02
-3.59391659e-01 8.17290545e-01 -1.15406644e-02 1.96632177e-01
-7.38193214e-01 -1.17066932e+00 -1.94471553e-01 -1.21782863e+00
-2.82713145e-01 5.12895763e-01 -1.37548417e-01 -3.35820973... | [5.765742301940918, 3.561239004135132] |
cd1bf0a6-5249-4410-9160-e083aa66a3c3 | effective-and-stable-role-based-multi-agent | 2304.00755 | null | https://arxiv.org/abs/2304.00755v1 | https://arxiv.org/pdf/2304.00755v1.pdf | Effective and Stable Role-Based Multi-Agent Collaboration by Structural Information Principles | Role-based learning is a promising approach to improving the performance of Multi-Agent Reinforcement Learning (MARL). Nevertheless, without manual assistance, current role-based methods cannot guarantee stably discovering a set of roles to effectively decompose a complex task, as they assume either a predefined role s... | ['Angsheng Li', 'Hao Peng', 'Xianghua Zeng'] | 2023-04-03 | null | null | null | null | ['starcraft-ii', 'starcraft'] | ['playing-games', 'playing-games'] | [ 2.81399619e-02 1.73242956e-01 -5.73951781e-01 1.53907999e-01
-6.89148664e-01 -6.61172807e-01 6.46772444e-01 2.42271766e-01
-4.55802649e-01 1.15006161e+00 4.83820364e-02 -2.18517795e-01
-8.70090544e-01 -6.71600997e-01 -5.31045794e-01 -1.13351083e+00
-3.73531342e-01 8.14756691e-01 1.11254588e-01 -4.91777927... | [3.738581657409668, 1.931173324584961] |
2503dcb6-d5d1-4aa9-9689-21f6493094a8 | transfool-an-adversarial-attack-against | 2302.00944 | null | https://arxiv.org/abs/2302.00944v2 | https://arxiv.org/pdf/2302.00944v2.pdf | TransFool: An Adversarial Attack against Neural Machine Translation Models | Deep neural networks have been shown to be vulnerable to small perturbations of their inputs, known as adversarial attacks. In this paper, we investigate the vulnerability of Neural Machine Translation (NMT) models to adversarial attacks and propose a new attack algorithm called TransFool. To fool NMT models, TransFool... | ['Pascal Frossard', 'Ljiljana Dolamic', 'Sahar Sadrizadeh'] | 2023-02-02 | null | null | null | null | ['nmt', 'semantic-textual-similarity'] | ['computer-code', 'natural-language-processing'] | [ 3.40913564e-01 6.80600628e-02 1.60308748e-01 -1.06134340e-01
-8.54278564e-01 -1.04357255e+00 9.08717036e-01 -3.76426995e-01
-3.11655521e-01 6.04711950e-01 -1.11763403e-01 -6.31348431e-01
3.78201991e-01 -7.13354826e-01 -1.10379577e+00 -5.21618426e-01
2.00394571e-01 5.28500915e-01 -2.55908996e-01 -5.92576087... | [6.019287586212158, 8.144308090209961] |
6c3104c4-02c7-4bf9-acc3-5f9e03daa974 | lea-meta-knowledge-driven-self-attentive | null | null | https://aclanthology.org/2022.naacl-main.7 | https://aclanthology.org/2022.naacl-main.7.pdf | LEA: Meta Knowledge-Driven Self-Attentive Document Embedding for Few-Shot Text Classification | Text classification has achieved great success with the prosperity of deep learning and pre-trained language models. However, we often encounter labeled data deficiency problems in real-world text-classification tasks. To overcome such challenging scenarios, interest in few-shot learning has increased, whereas most few... | ['Tae Young Jang', 'S. K. Hong'] | null | null | null | null | naacl-2022-7 | ['document-embedding', 'few-shot-text-classification'] | ['methodology', 'natural-language-processing'] | [ 2.77924538e-01 -2.26606414e-01 -4.37607765e-01 -3.84170055e-01
-6.92266762e-01 1.46892503e-01 1.02010584e+00 4.32357699e-01
-7.34407306e-01 5.50110936e-01 4.59853441e-01 5.53356670e-02
-1.41202947e-02 -8.41347694e-01 -1.22544900e-01 -5.20493746e-01
4.44689900e-01 3.44034433e-01 2.30178192e-01 -4.37008977... | [10.254741668701172, 3.608795166015625] |
946e8361-f282-40c5-9701-a93251b363d6 | occlusion-geodesics-for-online-multi-object | null | null | http://openaccess.thecvf.com/content_cvpr_2014/html/Possegger_Occlusion_Geodesics_for_2014_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2014/papers/Possegger_Occlusion_Geodesics_for_2014_CVPR_paper.pdf | Occlusion Geodesics for Online Multi-Object Tracking | Robust multi-object tracking-by-detection requires the correct assignment of noisy detection results to object trajectories. We address this problem by proposing an online approach based on the observation that object detectors primarily fail if objects are significantly occluded. In contrast to most existing work, we ... | ['Peter M. Roth', 'Thomas Mauthner', 'Horst Bischof', 'Horst Possegger'] | 2014-06-01 | null | null | null | cvpr-2014-6 | ['online-multi-object-tracking'] | ['computer-vision'] | [-1.21665619e-01 -5.85132003e-01 -6.19939156e-02 1.35227442e-01
-7.82636166e-01 -8.80300760e-01 3.69260162e-01 3.42861980e-01
-4.97466356e-01 5.43901622e-01 -4.16385621e-01 2.80875154e-03
5.62862912e-03 -3.18555862e-01 -7.20237076e-01 -5.94923973e-01
-2.67399158e-02 5.68131328e-01 1.06500673e+00 3.76868248... | [6.442364692687988, -2.0462772846221924] |
7f2a7f10-334b-4898-b6ec-62a11bee0fac | uncertain-label-correction-via-auxiliary | 2204.11053 | null | https://arxiv.org/abs/2204.11053v2 | https://arxiv.org/pdf/2204.11053v2.pdf | Uncertain Label Correction via Auxiliary Action Unit Graphs for Facial Expression Recognition | High-quality annotated images are significant to deep facial expression recognition (FER) methods. However, uncertain labels, mostly existing in large-scale public datasets, often mislead the training process. In this paper, we achieve uncertain label correction of facial expressions using auxiliary action unit (AU) gr... | ['Guoying Zhao', 'Janne Kauttonen', 'Xingming Zhang', 'Yang Liu'] | 2022-04-23 | null | null | null | null | ['facial-expression-recognition'] | ['computer-vision'] | [ 3.25596899e-01 4.20463055e-01 -1.30429506e-01 -1.14189112e+00
-4.13789868e-01 -1.97602719e-01 1.71932116e-01 -2.46010587e-01
-1.90546051e-01 7.62102425e-01 -1.77738965e-02 2.78120160e-01
4.30871546e-01 -4.13336217e-01 -6.71915174e-01 -6.13429487e-01
1.96929768e-01 1.04488775e-01 -3.26837510e-01 -1.85086265... | [13.630331039428711, 1.647863745689392] |
ca5adf63-4d7b-4cd5-b7f3-528a4685ff68 | anti-koopmanism | 2106.00106 | null | https://arxiv.org/abs/2106.00106v3 | https://arxiv.org/pdf/2106.00106v3.pdf | The kernel perspective on dynamic mode decomposition | This manuscript revisits theoretical assumptions concerning dynamic mode decomposition (DMD) of Koopman operators, including the existence of lattices of eigenfunctions, common eigenfunctions between Koopman operators, and boundedness and compactness of Koopman operators. Counterexamples that illustrate restrictiveness... | ['Joel A. Rosenfeld', 'Rushikesh Kamalapurkar', 'Michael Jury', 'Moad Abudia', 'Efrain Gonzalez'] | 2021-05-31 | null | null | null | null | ['misconceptions'] | ['miscellaneous'] | [-4.84169662e-01 1.84213266e-01 -2.44140495e-02 3.69659632e-01
-1.44802421e-01 -4.72211361e-01 -1.99963041e-02 -6.52088463e-01
7.60818943e-02 7.64434159e-01 7.48405419e-03 -3.26971024e-01
-6.39138758e-01 -2.53003597e-01 -4.19686794e-01 -1.26230252e+00
-7.92745531e-01 -1.46739720e-03 -2.75563329e-01 -3.20776910... | [7.39553689956665, 4.151216983795166] |
c6e3fbd3-ce8e-47c0-8385-a1e20778a47f | an-enhanced-object-detection-model-for-scene | null | null | https://link.springer.com/chapter/10.1007/978-3-031-20601-6_30 | https://rdcu.be/c0bJi | An Enhanced Object Detection Model for Scene Graph Generation | With computer vision improving, a higher level of understanding is needed to solve more complex problems such as semantic image retrieval, image captioning, and scene understanding. Scene understanding has been a long-studied problem due to its complexity and lack of proper data representation. A scene Graph is one of ... | ['Mohamed F. Tolba', 'Howida A. Shedeed', 'Dina Khattab', 'Mohammad Essam'] | 2022-11-18 | null | null | null | international-conference-on-advanced | ['scene-graph-generation'] | ['computer-vision'] | [ 3.65150541e-01 -8.70614797e-02 1.97679311e-01 -4.76923347e-01
-1.10709749e-01 -3.19645494e-01 7.34942734e-01 5.30374825e-01
-4.03533250e-01 3.62877607e-01 -1.49680927e-01 -7.41735771e-02
-2.74292052e-01 -1.00764549e+00 -7.11639702e-01 -5.35932958e-01
2.08006248e-01 4.43728179e-01 5.50984383e-01 -2.35135272... | [10.251348495483398, 1.5086472034454346] |
b2f81d79-9335-453b-a1be-edf816c3e7ed | see-better-before-looking-closer-weakly | 1901.09891 | null | http://arxiv.org/abs/1901.09891v2 | http://arxiv.org/pdf/1901.09891v2.pdf | See Better Before Looking Closer: Weakly Supervised Data Augmentation Network for Fine-Grained Visual Classification | Data augmentation is usually adopted to increase the amount of training data,
prevent overfitting and improve the performance of deep models. However, in
practice, random data augmentation, such as random image cropping, is
low-efficiency and might introduce many uncontrolled background noises. In this
paper, we propos... | ['Tao Hu', 'Yan Lu', 'Honggang Qi', 'Qingming Huang'] | 2019-01-26 | null | null | null | null | ['image-cropping'] | ['computer-vision'] | [ 1.01947613e-01 -5.42990267e-02 -2.17141241e-01 -3.30123067e-01
-2.35785693e-01 -1.57666981e-01 3.54006737e-01 3.13009024e-02
-4.10553992e-01 5.89840114e-01 3.81018072e-01 -9.41664074e-03
2.90700287e-01 -7.11355329e-01 -7.66676188e-01 -9.56458747e-01
3.79799247e-01 2.41401605e-02 3.92413527e-01 -4.23741043... | [9.539163589477539, 1.8534023761749268] |
3b5d86d5-1acf-476a-9ffc-c2a5f3a2d28b | vsql-variational-shadow-quantum-learning-for | 2012.08288 | null | https://arxiv.org/abs/2012.08288v1 | https://arxiv.org/pdf/2012.08288v1.pdf | VSQL: Variational Shadow Quantum Learning for Classification | Classification of quantum data is essential for quantum machine learning and near-term quantum technologies. In this paper, we propose a new hybrid quantum-classical framework for supervised quantum learning, which we call Variational Shadow Quantum Learning (VSQL). Our method in particular utilizes the classical shado... | ['Xin Wang', 'Zhixin Song', 'Guangxi Li'] | 2020-12-15 | null | null | null | null | ['handwritten-digit-recognition'] | ['computer-vision'] | [ 2.54982114e-01 -6.42120689e-02 3.92206898e-03 -3.48423958e-01
-8.73068094e-01 -5.21228552e-01 3.42614859e-01 3.11462004e-02
-5.05569279e-01 8.49148691e-01 -6.62336349e-01 -4.72011298e-01
-1.67958811e-01 -1.40490448e+00 -6.35222256e-01 -1.24052227e+00
5.68098187e-01 7.97807723e-02 -1.08777694e-01 -4.28257138... | [5.585540294647217, 4.95892858505249] |
ceba1c09-f039-4979-b413-94c07c8d3d69 | cross-linguistic-syntactic-difference-in | 2212.10879 | null | https://arxiv.org/abs/2212.10879v1 | https://arxiv.org/pdf/2212.10879v1.pdf | Cross-Linguistic Syntactic Difference in Multilingual BERT: How Good is It and How Does It Affect Transfer? | Multilingual BERT (mBERT) has demonstrated considerable cross-lingual syntactic ability, whereby it enables effective zero-shot cross-lingual transfer of syntactic knowledge. The transfer is more successful between some languages, but it is not well understood what leads to this variation and whether it fairly reflects... | ['Xuanjing Huang', 'Menghan Zhang', 'Jingting Ye', 'Qi Zhang', 'Ruotian Ma', 'Tao Gui', 'Ningyu Xu'] | 2022-12-21 | null | null | null | null | ['zero-shot-cross-lingual-transfer', 'cross-lingual-transfer'] | ['natural-language-processing', 'natural-language-processing'] | [-2.80887842e-01 -1.04757570e-01 -4.15383190e-01 -7.14347482e-01
-6.13489032e-01 -6.17188692e-01 6.18065000e-01 1.18853167e-01
-5.79804659e-01 7.13274181e-01 5.55052817e-01 -4.00577903e-01
-2.19309226e-01 -8.89568269e-01 -1.01320207e+00 -5.65082848e-01
9.89813060e-02 5.56428909e-01 1.20803952e-01 -7.98515856... | [10.873221397399902, 9.939891815185547] |
8eb6ca6c-bf18-4fa1-9673-cc85588d5ed5 | optimal-transport-graph-neural-networks | 2006.04804 | null | https://arxiv.org/abs/2006.04804v6 | https://arxiv.org/pdf/2006.04804v6.pdf | Optimal Transport Graph Neural Networks | Current graph neural network (GNN) architectures naively average or sum node embeddings into an aggregated graph representation -- potentially losing structural or semantic information. We here introduce OT-GNN, a model that computes graph embeddings using parametric prototypes that highlight key facets of different gr... | ['Octavian-Eugen Ganea', 'Gary Bécigneul', 'Benson Chen', 'Regina Barzilay', 'Tommi Jaakkola'] | 2020-06-08 | null | https://openreview.net/forum?id=o1O5nc48rn | https://openreview.net/pdf?id=o1O5nc48rn | null | ['graph-regression'] | ['graphs'] | [ 1.39291063e-01 4.94601190e-01 -1.80162221e-01 -1.03624128e-01
-3.74211639e-01 -7.16129005e-01 6.91031039e-01 6.15496159e-01
-9.77194235e-02 5.05194485e-01 2.19039679e-01 -5.09720743e-01
-4.81179386e-01 -1.01594996e+00 -1.10076308e+00 -7.92965949e-01
-5.77287853e-01 7.01878071e-01 -8.58884677e-02 -1.09897666... | [6.845322132110596, 6.121209144592285] |
5058e172-8910-4393-9ff4-460a024bcc3b | a-software-architecture-for-autonomous | 2010.12598 | null | https://arxiv.org/abs/2010.12598v1 | https://arxiv.org/pdf/2010.12598v1.pdf | A Software Architecture for Autonomous Vehicles: Team LRM-B Entry in the First CARLA Autonomous Driving Challenge | The objective of the first CARLA autonomous driving challenge was to deploy autonomous driving systems to lead with complex traffic scenarios where all participants faced the same challenging traffic situations. According to the organizers, this competition emerges as a way to democratize and to accelerate the research... | ['Fernando Santos Osório', 'Denis Fernando Wolf', 'Jean Amaro', 'Angelica Tiemi Mizuno Nakamura', 'Tiago Cesar dos Santos', 'Júnior Anderson Rodrigues da Silva', 'Iago Pacheco Gomes', 'Luis Alberto Rosero'] | 2020-10-23 | a-software-architecture-for-autonomous-1 | https://arxiv.org/abs/2010.12598 | https://arxiv.org/abs/2010.12598 | journal-of-systems-architecture-in-submission | ['carla-map-leaderboard'] | ['robots'] | [-5.66522598e-01 4.22178328e-01 2.77068704e-01 -4.18473989e-01
-3.32888961e-01 -3.88852566e-01 1.02037108e+00 -2.38222972e-01
-6.60115659e-01 3.46116424e-01 -3.41056317e-01 -1.01194978e+00
-3.20632339e-01 -1.03553760e+00 -4.79230165e-01 -3.89586568e-01
-2.42129683e-01 1.00218678e+00 6.98060751e-01 -1.11895502... | [5.652741432189941, 1.0372872352600098] |
443e5dbe-94c9-4950-83a6-8347bcded544 | tbgc-task-level-backbone-oriented-gradient | 2307.03465 | null | https://arxiv.org/abs/2307.03465v1 | https://arxiv.org/pdf/2307.03465v1.pdf | TBGC: Task-level Backbone-Oriented Gradient Clip for Multi-Task Foundation Model Learning | The AllInOne training paradigm squeezes a wide range of tasks into a unified model in a multi-task learning manner. However, optimization in multi-task learning is more challenge than single-task learning, as the gradient norm from different tasks may vary greatly, making the backbone overly biased towards one specific... | ['Xue Pan', 'Zelun Zhang'] | 2023-07-07 | null | null | null | null | ['data-augmentation', 'multi-task-learning'] | ['methodology', 'methodology'] | [ 4.58597928e-01 4.41371985e-02 -9.21817869e-02 -3.71667892e-01
-9.92034912e-01 -3.16189975e-01 5.50694048e-01 1.54209554e-01
-6.03296399e-01 6.05523169e-01 2.21687272e-01 -9.69609395e-02
3.71735580e-02 -1.18374936e-01 -7.23676205e-01 -5.80017805e-01
3.97145115e-02 2.57802784e-01 3.26727808e-01 -2.49642059... | [9.499124526977539, 2.658980131149292] |
2723da6a-8eac-41c7-b509-d6e503ba2dd0 | copner-contrastive-learning-with-prompt | null | null | https://aclanthology.org/2022.coling-1.222 | https://aclanthology.org/2022.coling-1.222.pdf | COPNER: Contrastive Learning with Prompt Guiding for Few-shot Named Entity Recognition | Distance metric learning has become a popular solution for few-shot Named Entity Recognition (NER). The typical setup aims to learn a similarity metric for measuring the semantic similarity between test samples and referents, where each referent represents an entity class. The effect of this setup may, however, be comp... | ['Chen Li', 'Rui Mao', 'Tieliang Gong', 'Xianli Zhang', 'Yige Wang', 'Kai He', 'YuCheng Huang'] | null | null | null | null | coling-2022-10 | ['few-shot-ner'] | ['natural-language-processing'] | [ 7.71023333e-02 -3.38672772e-02 -1.64187416e-01 -5.14253676e-01
-1.19022810e+00 -2.26878285e-01 6.07935131e-01 3.91027242e-01
-7.87017226e-01 5.04024208e-01 1.44080579e-01 2.61617303e-01
-2.27297708e-01 -8.85165453e-01 -3.89847994e-01 -5.30438364e-01
1.99064195e-01 4.56929415e-01 1.53515458e-01 -1.46227852... | [9.697123527526855, 9.328908920288086] |
7bfee4a4-c5dd-4a5f-8417-ab3f8ffff21b | batch-bayesian-optimization-via-particle | 2209.04722 | null | https://arxiv.org/abs/2209.04722v2 | https://arxiv.org/pdf/2209.04722v2.pdf | Batch Bayesian Optimization via Particle Gradient Flows | Bayesian Optimisation (BO) methods seek to find global optima of objective functions which are only available as a black-box or are expensive to evaluate. Such methods construct a surrogate model for the objective function, quantifying the uncertainty in that surrogate through Bayesian inference. Objective evaluations ... | ['Andrew B. Duncan', 'Konstantinos Zygalakis', 'Simon L. Cotter', 'Enrico Crovini'] | 2022-09-10 | null | null | null | null | ['bayesian-optimisation'] | ['methodology'] | [ 2.72624433e-01 2.45856971e-01 3.42309862e-01 -3.70932579e-01
-1.18787193e+00 -6.15099370e-01 5.63982010e-01 4.51549470e-01
-7.06004739e-01 8.25727940e-01 -1.42161697e-01 -2.73853660e-01
-8.50045741e-01 -6.03421390e-01 -8.06554258e-01 -1.00005841e+00
-2.46231079e-01 7.55606413e-01 -4.37072385e-03 7.63028348... | [6.324435710906982, 3.8555662631988525] |
72baa2f9-f1a9-4e03-b419-9d9c62c4ed15 | evaluating-machine-learning-models-with-nero | 2305.19889 | null | https://arxiv.org/abs/2305.19889v1 | https://arxiv.org/pdf/2305.19889v1.pdf | Evaluating Machine Learning Models with NERO: Non-Equivariance Revealed on Orbits | Proper evaluations are crucial for better understanding, troubleshooting, interpreting model behaviors and further improving model performance. While using scalar-based error metrics provides a fast way to overview model performance, they are often too abstract to display certain weak spots and lack information regardi... | ['Gordon L Kindlmann', 'Michael Maire', 'William Irvine', 'Takumi Matsuzawa', 'Zhuokai Zhao'] | 2023-05-31 | null | null | null | null | ['3d-point-cloud-classification', 'point-cloud-classification'] | ['computer-vision', 'computer-vision'] | [-3.50851685e-01 -4.05213714e-01 2.46970709e-02 -1.02229476e-01
-3.16691279e-01 -9.46633995e-01 7.72755384e-01 5.94822764e-01
1.11041710e-01 4.99330848e-01 -2.34023049e-01 -8.07290733e-01
-3.60982984e-01 -5.55274665e-01 -3.43808323e-01 -7.70855844e-01
-2.45112196e-01 5.36465228e-01 6.29240423e-02 -1.92337379... | [8.005343437194824, 4.600368499755859] |
c403333c-c5b8-41ea-94af-e0a49cc1d1d8 | segmentation-of-photovoltaic-module-cells-in | 1806.06530 | null | https://arxiv.org/abs/1806.06530v4 | https://arxiv.org/pdf/1806.06530v4.pdf | Segmentation of Photovoltaic Module Cells in Uncalibrated Electroluminescence Images | High resolution electroluminescence (EL) images captured in the infrared spectrum allow to visually and non-destructively inspect the quality of photovoltaic (PV) modules. Currently, however, such a visual inspection requires trained experts to discern different kinds of defects, which is time-consuming and expensive. ... | ['Christian Riess', 'Florian Gallwitz', 'Ansgar Steland', 'Claudia Buerhop-Lutz', 'Sergiu Deitsch', 'Andreas Maier', 'Evgenii Sovetkin'] | 2018-06-18 | null | null | null | null | ['solar-cell-segmentation'] | ['computer-vision'] | [ 6.65234149e-01 -7.96622112e-02 3.68140340e-01 -8.46865177e-02
-5.09932816e-01 -1.12221003e+00 2.58290619e-01 2.60418683e-01
-7.34769851e-02 8.24783504e-01 -4.89894420e-01 -1.93895936e-01
-1.48153141e-01 -8.64206970e-01 -5.69139242e-01 -1.07458103e+00
4.55706418e-01 2.99868524e-01 2.33764112e-01 3.26205492... | [7.2179341316223145, 1.8986417055130005] |
372cfb59-f032-4efb-a41c-13bf4e8b01e5 | the-clrs-algorithmic-reasoning-benchmark | 2205.15659 | null | https://arxiv.org/abs/2205.15659v2 | https://arxiv.org/pdf/2205.15659v2.pdf | The CLRS Algorithmic Reasoning Benchmark | Learning representations of algorithms is an emerging area of machine learning, seeking to bridge concepts from neural networks with classical algorithms. Several important works have investigated whether neural networks can effectively reason like algorithms, typically by learning to execute them. The common trend in ... | ['Charles Blundell', 'Raia Hadsell', 'Misha Dashevskiy', 'Andrea Banino', 'Razvan Pascanu', 'David Budden', 'Adrià Puigdomènech Badia', 'Petar Veličković'] | 2022-05-31 | null | null | null | null | ['learning-to-execute'] | ['computer-code'] | [ 3.74725342e-01 3.09825152e-01 -6.68212414e-01 -2.20038116e-01
-5.07585585e-01 -8.16158593e-01 6.30207717e-01 5.85721910e-01
-3.37893993e-01 4.54106510e-01 2.73886502e-01 -8.01951289e-01
-4.88074541e-01 -1.12049270e+00 -1.03804004e+00 -2.77131617e-01
5.81446514e-02 7.91576922e-01 -5.28044440e-03 -2.54829060... | [9.158180236816406, 7.157996654510498] |
9858b97b-e6b7-449f-b5bd-ce3eba2e8b5f | invertible-tree-embeddings-using-a | null | null | https://aclanthology.org/2020.coling-main.328 | https://aclanthology.org/2020.coling-main.328.pdf | Invertible Tree Embeddings using a Cryptographic Role Embedding Scheme | We present a novel method for embedding trees in a vector space based on Tensor-Product Representations (TPRs) which allows for inversion: the retrieval of the original tree structure and nodes from the vectorial embedding. Unlike previous attempts, this does not come at the cost of intractable representation size; we ... | ['Paul Smolensky', 'Coleman Haley'] | 2020-12-01 | null | null | null | coling-2020-8 | ['role-embedding'] | ['graphs'] | [ 4.52651024e-01 2.74926543e-01 -5.67408875e-02 -4.51195948e-02
-1.03141975e+00 -8.67756605e-01 4.33818072e-01 5.73733866e-01
-4.61285442e-01 6.13953114e-01 2.16847315e-01 -9.97360170e-01
-7.74419010e-02 -1.14437306e+00 -6.06626093e-01 -7.09380031e-01
-5.44808805e-01 5.20587444e-01 1.30347878e-01 -4.13316041... | [8.190569877624512, 4.099946975708008] |
aba6bfff-6ab2-4f66-967e-a3eabd22e694 | distilling-multi-level-x-vector-knowledge-for | 2303.01125 | null | https://arxiv.org/abs/2303.01125v1 | https://arxiv.org/pdf/2303.01125v1.pdf | Distilling Multi-Level X-vector Knowledge for Small-footprint Speaker Verification | Deep speaker models yield low error rates in speaker verification. Nonetheless, the high performance tends to be exchanged for model size and computation time, making these models challenging to run under limited conditions. We focus on small-footprint deep speaker embedding extraction, leveraging knowledge distillatio... | ['Tomi Kinnunen', 'Md Sahidullah', 'Xuechen Liu'] | 2023-03-02 | null | null | null | null | ['speaker-verification'] | ['speech'] | [ 1.56250671e-01 5.99007845e-01 -1.21472843e-01 -5.99281490e-01
-1.12333941e+00 -5.43419898e-01 5.46208978e-01 1.16486862e-01
-6.10219061e-01 2.11896464e-01 5.55010140e-01 -6.99387908e-01
2.14060917e-01 -2.05292806e-01 -3.66359115e-01 -6.30382240e-01
9.76302177e-02 4.07929085e-02 -2.46050969e-01 2.22656950... | [14.371315956115723, 6.158485412597656] |
aa1ee96f-422d-4437-bc48-35d76e85488d | cfear-radarodometry-conservative-filtering-1 | null | null | https://arxiv.org/abs/2105.01457 | https://arxiv.org/pdf/2105.01457.pdf | CFEAR Radarodometry - Conservative Filtering for Efficient and Accurate Radar Odometry | This paper presents the accurate, highly efficient, and learning-free method CFEAR Radarodometry for large-scale radar odometry estimation. By using a filtering technique that keeps the k strongest returns per azimuth and by additionally filtering the radar data in Cartesian space, we are able to compute a sparse set o... | ['Henrik Andreasson', 'Achim J. Lilienthal', 'Anas Alhashimi', 'Martin Magnusson', 'Daniel Adolfsson'] | 2021-09-16 | null | null | null | ieee-rsj-international-conference-on-3 | ['radar-odometry'] | ['robots'] | [ 1.42777503e-01 -2.05901340e-01 1.49909720e-01 -5.88601649e-01
-1.18913734e+00 -5.20310938e-01 7.18564689e-01 1.03944935e-01
-6.87674999e-01 6.74719572e-01 -5.27415015e-02 -1.24488346e-01
-4.38665420e-01 -1.06519485e+00 -7.21077204e-01 -3.95722598e-01
-6.66279137e-01 1.07929230e+00 5.16483963e-01 -4.97898698... | [7.379060745239258, -2.139268398284912] |
a331b2fc-1742-4ba7-b134-74d70a405319 | shapelet-based-sparse-representation-for | 1708.05974 | null | http://arxiv.org/abs/1708.05974v1 | http://arxiv.org/pdf/1708.05974v1.pdf | Shapelet-based Sparse Representation for Landcover Classification of Hyperspectral Images | This paper presents a sparse representation-based classification approach
with a novel dictionary construction procedure. By using the constructed
dictionary sophisticated prior knowledge about the spatial nature of the image
can be integrated. The approach is based on the assumption that each image
patch can be factor... | ['Björn Waske', 'Ribana Roscher'] | 2017-08-20 | null | null | null | null | ['classification-of-hyperspectral-images', 'sparse-representation-based-classification'] | ['computer-vision', 'computer-vision'] | [ 5.53587377e-01 -2.57386178e-01 -2.89421260e-01 -2.03155741e-01
-4.47339505e-01 -3.84792387e-01 4.76980507e-01 1.20903134e-01
-4.81877178e-02 7.19945312e-01 1.09991487e-02 1.41749710e-01
-4.97925073e-01 -9.06756759e-01 -3.78467530e-01 -1.16986573e+00
9.50583667e-02 1.68199554e-01 3.89440432e-02 -8.32004398... | [12.354206085205078, 0.30551379919052124] |
6d6fe063-a1e3-4304-9fa8-75da39436fd3 | single-image-super-resolution-based-on | 2210.03743 | null | https://arxiv.org/abs/2210.03743v1 | https://arxiv.org/pdf/2210.03743v1.pdf | Single Image Super-Resolution Based on Capsule Neural Networks | Single image super-resolution (SISR) is the process of obtaining one high-resolution version of a low-resolution image by increasing the number of pixels per unit area. This method has been actively investigated by the research community, due to the wide variety of real-world problems where it can be applied, from aeri... | ['Helio Pedrini', 'George Corrêa de Araújo'] | 2022-10-06 | null | null | null | null | ['video-enhancement'] | ['computer-vision'] | [ 4.42534983e-01 1.85384482e-01 1.56396732e-01 -1.40490249e-01
-2.17485741e-01 -3.34542811e-01 7.38319635e-01 -2.80287415e-01
-5.95460236e-01 9.08430994e-01 1.53110862e-01 1.74341910e-02
-5.02889752e-01 -9.28038776e-01 -5.75299621e-01 -7.73118734e-01
-3.31724674e-01 1.14991784e-01 5.97442448e-01 -6.00049734... | [10.641520500183105, -1.97036612033844] |
429f8b4d-857f-4983-b16a-d672ec17ac1f | class-weighted-convolutional-features-for | 1707.02581 | null | http://arxiv.org/abs/1707.02581v1 | http://arxiv.org/pdf/1707.02581v1.pdf | Class-Weighted Convolutional Features for Visual Instance Search | Image retrieval in realistic scenarios targets large dynamic datasets of
unlabeled images. In these cases, training or fine-tuning a model every time
new images are added to the database is neither efficient nor scalable.
Convolutional neural networks trained for image classification over large
datasets have been prove... | ['Xavier Giro-i-Nieto', 'Jose M. Alvarez', 'Albert Jimenez'] | 2017-07-09 | null | null | null | null | ['instance-search'] | ['computer-vision'] | [ 5.67013063e-02 -3.42488348e-01 -2.84250170e-01 -5.03828287e-01
-8.70647371e-01 -7.25691259e-01 7.50345826e-01 3.87233734e-01
-9.36119616e-01 4.56794232e-01 -1.40895113e-01 3.33647691e-02
-3.26092511e-01 -8.43566597e-01 -9.91242707e-01 -5.62238574e-01
9.52566341e-02 6.36261284e-01 3.51142406e-01 -2.32224599... | [10.657520294189453, 0.6769074201583862] |
4abb7bcb-2679-4a34-b8db-f6d6d8fed9b4 | are-chatgpt-and-gpt-4-general-purpose-solvers | 2305.05862 | null | https://arxiv.org/abs/2305.05862v1 | https://arxiv.org/pdf/2305.05862v1.pdf | Are ChatGPT and GPT-4 General-Purpose Solvers for Financial Text Analytics? An Examination on Several Typical Tasks | The most recent large language models such as ChatGPT and GPT-4 have garnered significant attention, as they are capable of generating high-quality responses to human input. Despite the extensive testing of ChatGPT and GPT-4 on generic text corpora, showcasing their impressive capabilities, a study focusing on financia... | ['Sameena Shah', 'Xiaomo Liu', 'Zhiqiang Ma', 'Xiaodan Zhu', 'Xianzhi Li'] | 2023-05-10 | null | null | null | null | ['named-entity-recognition-ner'] | ['natural-language-processing'] | [-2.05486521e-01 2.85524011e-01 8.95443931e-02 -4.90423441e-01
-1.06072354e+00 -7.53218174e-01 9.12647605e-01 -4.27229553e-02
-3.08458060e-01 7.97953963e-01 2.99589783e-01 -6.38665497e-01
4.06599371e-03 -9.15221632e-01 -6.22575462e-01 -2.44560540e-01
-3.34135965e-02 1.03782010e+00 -6.39974102e-02 -4.54040051... | [10.978813171386719, 8.471558570861816] |
7bebc731-7854-4a1d-9b9e-299f72096ee4 | diversity-promoting-gan-a-cross-entropy-based | null | null | https://aclanthology.org/D18-1428 | https://aclanthology.org/D18-1428.pdf | Diversity-Promoting GAN: A Cross-Entropy Based Generative Adversarial Network for Diversified Text Generation | Existing text generation methods tend to produce repeated and {''}boring{''} expressions. To tackle this problem, we propose a new text generation model, called Diversity-Promoting Generative Adversarial Network (DP-GAN). The proposed model assigns low reward for repeatedly generated text and high reward for {''}novel{... | ['Xu sun', 'Jingjing Xu', 'Junyang Lin', 'Xuancheng Ren'] | 2018-10-01 | null | null | null | emnlp-2018-10 | ['review-generation'] | ['natural-language-processing'] | [ 4.09660876e-01 4.88843173e-01 -1.69881545e-02 -2.32345730e-01
-1.11070585e+00 -5.63376248e-01 1.10092688e+00 -3.69434655e-01
-9.26806256e-02 1.56807160e+00 4.39972192e-01 -2.75033921e-01
4.46376950e-01 -9.35662448e-01 -2.73067296e-01 -5.48962057e-01
5.81958115e-01 6.42294228e-01 -2.53998280e-01 -5.95634699... | [11.905096054077148, 9.146808624267578] |
6ecbf7c5-153a-486a-92ac-65a2d4fefa95 | m2r2-missing-modality-robust-emotion | 2205.02524 | null | https://arxiv.org/abs/2205.02524v1 | https://arxiv.org/pdf/2205.02524v1.pdf | M2R2: Missing-Modality Robust emotion Recognition framework with iterative data augmentation | This paper deals with the utterance-level modalities missing problem with uncertain patterns on emotion recognition in conversation (ERC) task. Present models generally predict the speaker's emotions by its current utterance and context, which is degraded by modality missing considerably. Our work proposes a framework ... | ['Ning Wang'] | 2022-05-05 | null | null | null | null | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [ 4.94510323e-01 4.18894440e-01 -6.78606555e-02 -9.58015859e-01
-1.02544987e+00 -2.30890602e-01 8.61792803e-01 -5.64630270e-01
-2.15262324e-01 8.26431692e-01 8.03184450e-01 1.86187163e-01
4.43487704e-01 -1.55105904e-01 -5.06948233e-01 -8.31694722e-01
1.51533693e-01 2.46844843e-01 -8.95547032e-01 -3.97140443... | [13.196829795837402, 5.568807601928711] |
6192e5ac-1309-431f-968f-f57287a7fdae | inferring-preferences-from-demonstrations-in | 2304.14115 | null | https://arxiv.org/abs/2304.14115v1 | https://arxiv.org/pdf/2304.14115v1.pdf | Inferring Preferences from Demonstrations in Multi-objective Reinforcement Learning: A Dynamic Weight-based Approach | Many decision-making problems feature multiple objectives. In such problems, it is not always possible to know the preferences of a decision-maker for different objectives. However, it is often possible to observe the behavior of decision-makers. In multi-objective decision-making, preference inference is the process o... | ['Karl Mason', 'Patrick Mannion', 'Junlin Lu'] | 2023-04-27 | null | null | null | null | ['multi-objective-reinforcement-learning'] | ['methodology'] | [ 1.59319565e-02 -2.55943775e-01 -3.48217845e-01 -7.03456879e-01
-5.31878352e-01 -5.95701516e-01 4.35500056e-01 3.78591180e-01
-8.04623425e-01 9.35885727e-01 1.64759293e-01 -4.41136569e-01
-7.60988474e-01 -5.70210993e-01 -1.99793264e-01 -9.96419013e-01
-4.01359051e-01 1.21172380e+00 1.87312603e-01 -4.69334647... | [4.2480149269104, 2.4015700817108154] |
3852319c-6287-481b-9845-27e89e3b30dc | find-a-reasonable-ending-for-stories-does | 1812.05411 | null | http://arxiv.org/abs/1812.05411v1 | http://arxiv.org/pdf/1812.05411v1.pdf | Find a Reasonable Ending for Stories: Does Logic Relation Help the Story Cloze Test? | Natural language understanding is a challenging problem that covers a wide
range of tasks. While previous methods generally train each task separately, we
consider combining the cross-task features to enhance the task performance. In
this paper, we incorporate the logic information with the help of the Natural
Language... | ['Mingyue Shang', 'Hongzhi Yin', 'Zhenxin Fu', 'Rui Yan', 'Dongyan Zhao', 'Bo Tang'] | 2018-12-13 | null | null | null | null | ['cloze-test'] | ['natural-language-processing'] | [ 1.66861504e-01 3.73029783e-02 -5.27247488e-01 -7.88843095e-01
-4.97194290e-01 -5.50982356e-01 8.79024148e-01 1.82723567e-01
-7.85160437e-02 7.21915364e-01 7.27202058e-01 -1.70611873e-01
-1.14408910e-01 -9.86240625e-01 -8.69316578e-01 -1.47214830e-01
4.29456502e-01 2.74564028e-01 3.30466509e-01 -5.15652180... | [11.085335731506348, 8.857656478881836] |
b9359e12-44ca-4f4f-a31f-0e960e0ca274 | mutual-adaptive-reasoning-for-monocular-3d | 2207.07900 | null | https://arxiv.org/abs/2207.07900v1 | https://arxiv.org/pdf/2207.07900v1.pdf | Mutual Adaptive Reasoning for Monocular 3D Multi-Person Pose Estimation | Inter-person occlusion and depth ambiguity make estimating the 3D poses of monocular multiple persons as camera-centric coordinates a challenging problem. Typical top-down frameworks suffer from high computational redundancy with an additional detection stage. By contrast, the bottom-up methods enjoy low computational ... | ['Jingyi Yu', 'Lan Xu', 'Fei Gao', 'Ye Shi', 'Jingya Wang', 'Juze Zhang'] | 2022-07-16 | null | null | null | null | ['3d-multi-person-pose-estimation', 'multi-person-pose-estimation'] | ['computer-vision', 'computer-vision'] | [-2.18421683e-01 1.08983237e-02 -9.59517807e-02 -4.10595715e-01
-6.00277305e-01 -4.83932495e-01 3.89224976e-01 -1.78183451e-01
-1.48958117e-01 2.90576935e-01 3.87285858e-01 2.60213882e-01
2.12277561e-01 -6.52280331e-01 -5.89323997e-01 -2.53894657e-01
2.93206483e-01 5.65102041e-01 2.67507792e-01 -1.33143499... | [7.024882793426514, -1.0023162364959717] |
42bcc423-f316-4336-9963-00e5efe26428 | inference-and-sampling-of-point-processes | 2306.00762 | null | https://arxiv.org/abs/2306.00762v1 | https://arxiv.org/pdf/2306.00762v1.pdf | Inference and Sampling of Point Processes from Diffusion Excursions | Point processes often have a natural interpretation with respect to a continuous process. We propose a point process construction that describes arrival time observations in terms of the state of a latent diffusion process. In this framework, we relate the return times of a diffusion in a continuous path space to new a... | ['Vahid Tarokh', 'Anderson Schneider', 'Mohamed Abdelghani', 'Yuting Ng', 'Yu Chen', 'Ali Hasan'] | 2023-06-01 | null | null | null | null | ['point-processes'] | ['methodology'] | [ 3.01335514e-01 -1.12561911e-01 3.19516025e-02 1.69457924e-02
-2.80350298e-02 -5.80359161e-01 1.05738401e+00 3.01082015e-01
-4.33299929e-01 7.16671824e-01 1.66837156e-01 -1.81436762e-01
-4.27315086e-01 -9.16028917e-01 -6.92580342e-01 -1.01054013e+00
-8.10812339e-02 5.76950908e-01 -8.81717131e-02 -4.13281880... | [6.7687859535217285, 3.805321455001831] |
a8b614fd-2cf1-4a7c-945c-f4a60423a889 | twise-at-semeval-2017-task-4-five-point | null | null | https://aclanthology.org/S17-2127 | https://aclanthology.org/S17-2127.pdf | TwiSe at SemEval-2017 Task 4: Five-point Twitter Sentiment Classification and Quantification | The paper describes the participation of the team {``}TwiSE{''} in the SemEval-2017 challenge. Specifically, I participated at Task 4 entitled {``}Sentiment Analysis in Twitter{''} for which I implemented systems for five-point tweet classification (Subtask C) and five-point tweet quantification (Subtask E) for English... | ['Georgios Balikas'] | 2017-08-01 | null | null | null | semeval-2017-8 | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [ 7.35760629e-02 1.88981369e-02 1.70317236e-02 -7.46592045e-01
-1.09626877e+00 -7.20056832e-01 8.98522973e-01 8.33905458e-01
-8.48281145e-01 5.27011096e-01 1.16145089e-01 -3.80967468e-01
-1.26276925e-01 -6.04028761e-01 -1.95091173e-01 -3.83669376e-01
3.01454127e-01 5.90470016e-01 6.03686161e-02 -8.38572562... | [11.166139602661133, 6.943508148193359] |
4df8b434-55a6-4001-9362-e85775a49d95 | nonparametric-probabilistic-regression-with | 2210.16247 | null | https://arxiv.org/abs/2210.16247v1 | https://arxiv.org/pdf/2210.16247v1.pdf | Nonparametric Probabilistic Regression with Coarse Learners | Probabilistic Regression refers to predicting a full probability density function for the target conditional on the features. We present a nonparametric approach to this problem which combines base classifiers (typically gradient boosted forests) trained on different coarsenings of the target value. By combining such c... | ['Brian Lucena'] | 2022-10-28 | null | null | null | null | ['prediction-intervals'] | ['miscellaneous'] | [ 1.37773842e-01 1.98022529e-01 -4.99654770e-01 -5.14476657e-01
-1.19158840e+00 -4.10676152e-01 9.15830493e-01 2.49961048e-01
-2.51248926e-01 1.17025137e+00 1.19521491e-01 -2.94598013e-01
-7.24223554e-02 -8.65477324e-01 -8.17150891e-01 -8.35283160e-01
-3.47282380e-01 6.46400154e-01 3.11164349e-01 2.31660187... | [7.342067718505859, 4.1162028312683105] |
107119e9-0138-4f5d-9731-8c571ae8b30b | curvature-based-feature-selection-with | 2101.03581 | null | https://arxiv.org/abs/2101.03581v3 | https://arxiv.org/pdf/2101.03581v3.pdf | Curvature-based Feature Selection with Application in Classifying Electronic Health Records | Disruptive technologies provides unparalleled opportunities to contribute to the identifications of many aspects in pervasive healthcare, from the adoption of the Internet of Things through to Machine Learning (ML) techniques. As a powerful tool, ML has been widely applied in patient-centric healthcare solutions. To fu... | ['Noura Al Moubayed', 'Han Xu', 'Jie Li', 'Zheming Zuo'] | 2021-01-10 | null | null | null | null | ['breast-cancer-detection', 'cervical-cancer-biopsy-identification', 'breast-cancer-detection', 'diabetic-retinopathy-detection', 'breast-tissue-identification'] | ['knowledge-base', 'medical', 'medical', 'medical', 'medical'] | [ 7.64529780e-02 -2.34123856e-01 8.57741460e-02 -9.23656300e-02
-5.90007901e-01 -1.05235822e-01 3.99857730e-01 6.33508980e-01
-1.87472209e-01 5.36684573e-01 3.54054242e-01 -3.68710250e-01
-6.96583450e-01 -6.53799713e-01 8.82306024e-02 -9.26558912e-01
-6.37056306e-02 3.75249416e-01 -2.81851739e-01 5.70673831... | [8.439895629882812, 4.840684413909912] |
c74bc84f-ec38-4933-979d-a830497f6cd1 | a-hybrid-system-of-sound-event-detection | 2210.09529 | null | https://arxiv.org/abs/2210.09529v1 | https://arxiv.org/pdf/2210.09529v1.pdf | A Hybrid System of Sound Event Detection Transformer and Frame-wise Model for DCASE 2022 Task 4 | In this paper, we describe in detail our system for DCASE 2022 Task4. The system combines two considerably different models: an end-to-end Sound Event Detection Transformer (SEDT) and a frame-wise model, Metric Learning and Focal Loss CNN (MLFL-CNN). The former is an event-wise model which learns event-level representa... | ['Kazushige Ouchi', 'Long Yan', 'Rui Tao', 'Yueliang Qian', 'Hong Liu', 'Xiangdong Wang', 'Zhirong Ye', 'Zhifang Guo', 'Yiming Li'] | 2022-10-18 | null | null | null | null | ['sound-event-detection'] | ['audio'] | [-9.43470374e-03 1.19102411e-01 1.98272243e-01 -4.51374203e-01
-1.48252368e+00 -2.83979416e-01 4.02236879e-01 1.13999687e-01
-6.20268583e-01 6.00199699e-01 8.08701888e-02 -1.40685067e-01
1.87343106e-01 -6.10088825e-01 -7.04269171e-01 -8.40908170e-01
-1.55858219e-01 2.52295792e-01 7.36046135e-01 3.40198934... | [15.184829711914062, 5.065791606903076] |
651207aa-82ad-4858-bbc7-48f2a620aeb4 | cs-tgn-community-search-via-temporal-graph | 2303.08964 | null | https://arxiv.org/abs/2303.08964v1 | https://arxiv.org/pdf/2303.08964v1.pdf | CS-TGN: Community Search via Temporal Graph Neural Networks | Searching for local communities is an important research challenge that allows for personalized community discovery and supports advanced data analysis in various complex networks, such as the World Wide Web, social networks, and brain networks. The evolution of these networks over time has motivated several recent stu... | ['Milad Rezaei Hajidehi', 'Ali Behrouz', 'Farnoosh Hashemi'] | 2023-03-15 | null | null | null | null | ['community-search'] | ['graphs'] | [-2.83935726e-01 -9.64869708e-02 -2.28421465e-01 9.41012353e-02
2.90272593e-01 -9.44569767e-01 4.07893986e-01 6.15904570e-01
-2.44520873e-01 2.77393937e-01 1.19653605e-01 -1.30390793e-01
-5.59731781e-01 -1.12703514e+00 -3.43670398e-01 -4.83125001e-01
-8.46253395e-01 6.56872213e-01 7.71996021e-01 -3.14041823... | [7.179445266723633, 5.980305194854736] |
460df0c7-1382-4e11-b8e5-956c13b65983 | fame-for-sale-efficient-detection-of-fake | 1509.04098 | null | http://arxiv.org/abs/1509.04098v2 | http://arxiv.org/pdf/1509.04098v2.pdf | Fame for sale: efficient detection of fake Twitter followers | $\textit{Fake followers}$ are those Twitter accounts specifically created to
inflate the number of followers of a target account. Fake followers are
dangerous for the social platform and beyond, since they may alter concepts
like popularity and influence in the Twittersphere - hence impacting on
economy, politics, and ... | ['Marinella Petrocchi', 'Angelo Spognardi', 'Stefano Cresci', 'Roberto Di Pietro', 'Maurizio Tesconi'] | 2015-09-14 | null | null | null | null | ['spam-detection'] | ['natural-language-processing'] | [-2.29081213e-02 2.59353608e-01 -1.17580101e-01 -2.53748715e-01
-4.62089330e-01 -6.74536526e-01 1.17569411e+00 6.83735967e-01
-4.73441482e-01 9.09135163e-01 -7.98048526e-02 -3.90169859e-01
-7.94797465e-02 -1.07325876e+00 -5.49435675e-01 -7.13989258e-01
-3.61305848e-02 3.64830941e-01 4.37772214e-01 -5.92642248... | [8.026815414428711, 10.113916397094727] |
eb8cf580-476a-4981-9fa2-727fc9ad137f | don-t-stop-pretraining-make-prompt-based-fine | 2305.01711 | null | https://arxiv.org/abs/2305.01711v2 | https://arxiv.org/pdf/2305.01711v2.pdf | Don't Stop Pretraining? Make Prompt-based Fine-tuning Powerful Learner | Language models (LMs) trained on vast quantities of unlabelled data have greatly advanced the field of natural language processing (NLP). In this study, we re-visit the widely accepted notion in NLP that continued pre-training LMs on task-related texts improves the performance of fine-tuning (FT) in downstream tasks. T... | ['Aldo Lipani', 'Zhengxiang Shi'] | 2023-05-02 | null | null | null | null | ['unsupervised-pre-training'] | ['methodology'] | [ 5.85191309e-01 1.67288885e-01 -1.75317928e-01 -5.72354317e-01
-1.24870825e+00 -8.14602315e-01 9.51137424e-01 3.66080731e-01
-8.26357305e-01 7.28669465e-01 1.49018705e-01 -5.59987843e-01
-1.30597249e-01 -2.16644481e-01 -6.62072778e-01 -3.65741432e-01
1.52116016e-01 6.43729210e-01 3.96541446e-01 -4.03681666... | [10.729679107666016, 8.490121841430664] |
ca7aacbe-22d9-41ea-a6de-ae8d482d8355 | a-probabilistic-deep-learning-approach-to | 2103.16664 | null | https://arxiv.org/abs/2103.16664v1 | https://arxiv.org/pdf/2103.16664v1.pdf | A probabilistic deep learning approach to automate the interpretation of multi-phase diffraction spectra | Autonomous synthesis and characterization of inorganic materials requires the automatic and accurate analysis of X-ray diffraction spectra. For this task, we designed a probabilistic deep learning algorithm to identify complex multi-phase mixtures. At the core of this algorithm lies an ensemble convolutional neural net... | ['Gerbrand Ceder', 'Qingsong Tu', 'Yan Zeng', 'Christopher J. Bartel', 'Nathan J. Szymanski'] | 2021-03-30 | null | null | null | null | ['probabilistic-deep-learning'] | ['computer-vision'] | [ 5.69822669e-01 -5.36602885e-02 2.89123148e-01 -4.37114835e-01
-8.14084411e-01 -3.78361732e-01 6.53126657e-01 5.13226867e-01
-4.35677767e-01 8.69329810e-01 -1.21476434e-01 -3.99871379e-01
-1.74187720e-01 -6.85507357e-01 -8.75505745e-01 -9.94848371e-01
1.70276657e-01 1.40490711e+00 1.01739913e-01 4.61188629... | [5.297346591949463, 5.209017276763916] |
490a69e7-f6b4-49c8-808b-91d72e77f620 | persuasion-for-good-towards-a-personalized | 1906.06725 | null | https://arxiv.org/abs/1906.06725v2 | https://arxiv.org/pdf/1906.06725v2.pdf | Persuasion for Good: Towards a Personalized Persuasive Dialogue System for Social Good | Developing intelligent persuasive conversational agents to change people's opinions and actions for social good is the frontier in advancing the ethical development of automated dialogue systems. To do so, the first step is to understand the intricate organization of strategic disclosures and appeals employed in human ... | ['Zhou Yu', 'Sijia Yang', 'Yoojung Oh', 'Xuewei Wang', 'Jingwen Zhang', 'Weiyan Shi', 'Richard Kim'] | 2019-06-16 | persuasion-for-good-towards-a-personalized-1 | https://aclanthology.org/P19-1566 | https://aclanthology.org/P19-1566.pdf | acl-2019-7 | ['persuasion-strategies'] | ['computer-vision'] | [ 1.89239860e-01 7.93107212e-01 -2.75618404e-01 -9.58058655e-01
-4.24413741e-01 -4.78544027e-01 9.76163447e-01 4.03316259e-01
-6.33148134e-01 1.14100468e+00 1.11471415e+00 -4.53538358e-01
8.68312493e-02 -6.62499487e-01 2.16375247e-01 -4.35192376e-01
5.20387530e-01 3.38192195e-01 -2.46762395e-01 -7.41683185... | [12.795148849487305, 7.9718546867370605] |
aaffcddc-a2ce-4023-8f66-9949f98f5b98 | why-do-cnns-excel-at-feature-extraction-a | 2307.00919 | null | https://arxiv.org/abs/2307.00919v1 | https://arxiv.org/pdf/2307.00919v1.pdf | Why do CNNs excel at feature extraction? A mathematical explanation | Over the past decade deep learning has revolutionized the field of computer vision, with convolutional neural network models proving to be very effective for image classification benchmarks. However, a fundamental theoretical questions remain answered: why can they solve discrete image classification tasks that involve... | ['Tongliang Liu', 'Arush Tagade', 'Vinoth Nandakumar'] | 2023-07-03 | null | null | null | null | ['classification-1'] | ['methodology'] | [ 5.08777440e-01 2.55138546e-01 -3.15405913e-02 -2.85630673e-01
-3.19493592e-01 -5.42477369e-01 4.17450905e-01 4.26165722e-02
-4.62669373e-01 4.91012275e-01 -4.75815445e-01 -4.78874892e-01
-2.93487102e-01 -1.02819097e+00 -1.00919712e+00 -7.42940485e-01
-2.30325013e-01 -3.28166597e-02 3.34308930e-02 -4.01137620... | [9.207416534423828, 2.274465560913086] |
90a9cd81-db90-4417-9c86-23991c5c557c | robust-self-supervised-audio-visual-speech | 2201.01763 | null | https://arxiv.org/abs/2201.01763v3 | https://arxiv.org/pdf/2201.01763v3.pdf | Robust Self-Supervised Audio-Visual Speech Recognition | Audio-based automatic speech recognition (ASR) degrades significantly in noisy environments and is particularly vulnerable to interfering speech, as the model cannot determine which speaker to transcribe. Audio-visual speech recognition (AVSR) systems improve robustness by complementing the audio stream with the visual... | ['Abdelrahman Mohamed', 'Wei-Ning Hsu', 'Bowen Shi'] | 2022-01-05 | null | null | null | null | ['lipreading', 'audio-visual-speech-recognition'] | ['computer-vision', 'speech'] | [ 3.51891577e-01 2.38442689e-01 8.86225607e-03 -1.82269543e-01
-1.47015572e+00 -6.21208012e-01 7.01332569e-01 1.08874485e-01
-2.95292586e-01 3.03086311e-01 3.86501610e-01 -5.43509781e-01
4.33854222e-01 -1.34752512e-01 -5.94772220e-01 -6.53795242e-01
3.54041129e-01 2.29914859e-01 3.66526753e-01 -2.61206061... | [14.371739387512207, 5.172881126403809] |
c52397e0-fc74-4a3a-b504-124a4ed37a39 | a-model-to-search-for-synthesizable-molecules | 1906.05221 | null | https://arxiv.org/abs/1906.05221v2 | https://arxiv.org/pdf/1906.05221v2.pdf | A Model to Search for Synthesizable Molecules | Deep generative models are able to suggest new organic molecules by generating strings, trees, and graphs representing their structure. While such models allow one to generate molecules with desirable properties, they give no guarantees that the molecules can actually be synthesized in practice. We propose a new molecu... | ['José Miguel Hernández-Lobato', 'Marwin H. S. Segler', 'Matt J. Kusner', 'Brooks Paige', 'John Bradshaw'] | 2019-06-12 | a-model-to-search-for-synthesizable-molecules-1 | http://papers.nips.cc/paper/9007-a-model-to-search-for-synthesizable-molecules | http://papers.nips.cc/paper/9007-a-model-to-search-for-synthesizable-molecules.pdf | neurips-2019-12 | ['retrosynthesis'] | ['medical'] | [ 6.84163868e-01 4.47218359e-01 -2.78573781e-01 -2.47284435e-02
-3.29695374e-01 -1.17151415e+00 1.00305223e+00 4.56335634e-01
1.82676703e-01 1.04225457e+00 2.92488426e-01 -4.85300481e-01
3.91942799e-01 -1.37028658e+00 -9.31189299e-01 -7.33642578e-01
5.95844164e-02 7.08476841e-01 -8.90782028e-02 -3.28029960... | [4.541945934295654, 6.071860313415527] |
8d211068-5f5a-4d5d-bbd3-3350297db420 | henry-core-domain-adaptation-and-stacking-for | null | null | https://aclanthology.org/S13-1013 | https://aclanthology.org/S13-1013.pdf | HENRY-CORE: Domain Adaptation and Stacking for Text Similarity | null | ['Michael Heilman', 'Nitin Madnani'] | 2013-06-01 | null | null | null | semeval-2013-6 | ['video-description'] | ['computer-vision'] | [-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... | [-7.20218563079834, 3.8159279823303223] |
e3e8e002-e4fe-4dd5-a0c2-b8a881c923ee | exploring-intra-and-inter-video-relation-for | 2203.15251 | null | https://arxiv.org/abs/2203.15251v2 | https://arxiv.org/pdf/2203.15251v2.pdf | Exploring Intra- and Inter-Video Relation for Surgical Semantic Scene Segmentation | Automatic surgical scene segmentation is fundamental for facilitating cognitive intelligence in the modern operating theatre. Previous works rely on conventional aggregation modules (e.g., dilated convolution, convolutional LSTM), which only make use of the local context. In this paper, we propose a novel framework STs... | ['Danail Stoyanov', 'Pheng-Ann Heng', 'Zixu Zhao', 'Cheng Chen', 'Yang Yu', 'Yueming Jin'] | 2022-03-29 | null | null | null | null | ['scene-segmentation'] | ['computer-vision'] | [ 2.82361895e-01 2.39793789e-02 -5.32987416e-01 -2.77893662e-01
-7.62527883e-01 -3.25875640e-01 3.74289572e-01 2.32112497e-01
-7.01411009e-01 4.53542292e-01 5.85160077e-01 -2.39961892e-01
-2.70292640e-01 -5.56049228e-01 -6.52732551e-01 -9.48731065e-01
7.15259388e-02 -5.44740081e-01 1.90505534e-01 -1.42861754... | [14.25064468383789, -3.142805814743042] |
03475fa5-8737-4551-b5de-277eb9d2eac5 | probing-the-information-encoded-in-x-vectors | 1909.06351 | null | https://arxiv.org/abs/1909.06351v2 | https://arxiv.org/pdf/1909.06351v2.pdf | Probing the Information Encoded in X-vectors | Deep neural network based speaker embeddings, such as x-vectors, have been shown to perform well in text-independent speaker recognition/verification tasks. In this paper, we use simple classifiers to investigate the contents encoded by x-vector embeddings. We probe these embeddings for information related to the speak... | ['Sanjeev Khudanpur', 'Daniel Povey', 'David Snyder', 'Desh Raj'] | 2019-09-13 | null | null | null | null | ['text-independent-speaker-recognition'] | ['speech'] | [ 1.00990281e-01 -9.13521349e-02 -1.85181364e-01 -6.80205286e-01
-7.97273993e-01 -6.03475213e-01 8.83433044e-01 3.08081329e-01
-4.31762606e-01 2.32967123e-01 9.55267966e-01 -5.84276259e-01
2.86301404e-01 -1.88368767e-01 -4.03552532e-01 -6.92342401e-01
-3.93157601e-01 4.12240215e-02 -5.17343879e-01 -4.12267596... | [14.349690437316895, 6.145889759063721] |
d6779e7d-0216-4253-b39a-c9f592d965e1 | positive-negative-and-neutral-modeling | 2205.06058 | null | https://arxiv.org/abs/2205.06058v1 | https://arxiv.org/pdf/2205.06058v1.pdf | Positive, Negative and Neutral: Modeling Implicit Feedback in Session-based News Recommendation | News recommendation for anonymous readers is a useful but challenging task for many news portals, where interactions between readers and articles are limited within a temporary login session. Previous works tend to formulate session-based recommendation as a next item prediction task, while they neglect the implicit fe... | ['Kenny Q. Zhu', 'Shansan Gong'] | 2022-05-12 | null | null | null | null | ['session-based-recommendations'] | ['miscellaneous'] | [-4.48781341e-01 -7.23271370e-02 -1.01785123e+00 -6.51326537e-01
1.16195427e-02 -5.19299209e-01 7.20775902e-01 2.59641647e-01
-4.92746443e-01 6.10350788e-01 5.63698232e-01 -5.12084067e-01
-7.36227483e-02 -7.32960224e-01 -5.60134470e-01 -3.09982568e-01
2.77068973e-01 2.21591637e-01 5.11128008e-01 -5.22148132... | [10.110187530517578, 5.678277969360352] |
e1b9cd0c-0957-49c2-9d61-ab9dda951802 | tv-regularized-ct-reconstruction-and-metal | 1810.03275 | null | http://arxiv.org/abs/1810.03275v1 | http://arxiv.org/pdf/1810.03275v1.pdf | TV-regularized CT Reconstruction and Metal Artifact Reduction Using Inequality Constraints with Preconditioning | Total variation(TV) regularization is applied to X-Ray computed
tomography(CT) in an effort to reduce metal artifacts. Tikhonov regularization
with $L^2$ data fidelity term and total variation regularization is augmented
in this novel model by inequality constraints on sinogram data affected by
metal to model errors ca... | ['Clemens Schiffer'] | 2018-10-08 | null | null | null | null | ['metal-artifact-reduction'] | ['medical'] | [ 2.54434109e-01 1.30625710e-01 2.03856006e-01 -2.09670946e-01
-8.48050594e-01 6.80739209e-02 1.02017745e-01 8.94671157e-02
-5.11224449e-01 1.15616751e+00 2.73899823e-01 -1.94484755e-01
-4.82322693e-01 -2.74134040e-01 -3.36035728e-01 -8.64316463e-01
-8.86052847e-02 3.74001563e-01 1.16589718e-01 6.42798096... | [13.118269920349121, -2.6102638244628906] |
6e75d3cc-ecff-4fae-9518-cc2063149c84 | distilling-token-pruned-pose-transformer-for | 2304.05548 | null | https://arxiv.org/abs/2304.05548v1 | https://arxiv.org/pdf/2304.05548v1.pdf | Distilling Token-Pruned Pose Transformer for 2D Human Pose Estimation | Human pose estimation has seen widespread use of transformer models in recent years. Pose transformers benefit from the self-attention map, which captures the correlation between human joint tokens and the image. However, training such models is computationally expensive. The recent token-Pruned Pose Transformer (PPT) ... | ['Feixiang Ren'] | 2023-04-12 | null | null | null | null | ['2d-human-pose-estimation'] | ['computer-vision'] | [ 1.49652809e-01 3.18428576e-01 3.90249230e-02 -1.77363873e-01
-7.50187457e-01 -2.12360248e-01 3.88032466e-01 -1.64619699e-01
-4.43870276e-01 4.70712006e-01 4.89301175e-01 3.17057580e-01
2.75669396e-01 -7.30731606e-01 -1.06792498e+00 -4.83095407e-01
-2.58133262e-02 6.28923416e-01 4.90516245e-01 9.53752548... | [7.125339508056641, -0.7544150352478027] |
2520f5c5-bed4-4b51-9ff9-46453ca1a2a8 | look-ma-no-hands-agent-environment | 2305.16301 | null | https://arxiv.org/abs/2305.16301v1 | https://arxiv.org/pdf/2305.16301v1.pdf | Look Ma, No Hands! Agent-Environment Factorization of Egocentric Videos | The analysis and use of egocentric videos for robotic tasks is made challenging by occlusion due to the hand and the visual mismatch between the human hand and a robot end-effector. In this sense, the human hand presents a nuisance. However, often hands also provide a valuable signal, e.g. the hand pose may suggest wha... | ['Saurabh Gupta', 'Aditya Prakash', 'Matthew Chang'] | 2023-05-25 | null | null | null | null | ['video-inpainting', '3d-reconstruction'] | ['computer-vision', 'computer-vision'] | [-1.63842775e-02 8.33287835e-02 -6.80442452e-02 1.23769499e-01
-1.85632244e-01 -5.78782141e-01 5.20016432e-01 -4.56478029e-01
-3.77617598e-01 3.56677324e-01 6.01896048e-01 1.76779568e-01
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-9.55237076e-03 2.29649678e-01 3.15332673e-02 -2.51558647... | [4.721270561218262, 0.7644671201705933] |
6e4ca0d3-6c93-4f46-8588-ec2ae86ed0de | that-slepen-al-the-nyght-with-open-ye-cross-2 | 2209.02967 | null | https://arxiv.org/abs/2209.02967v1 | https://arxiv.org/pdf/2209.02967v1.pdf | That Slepen Al the Nyght with Open Ye! Cross-era Sequence Segmentation with Switch-memory | The evolution of language follows the rule of gradual change. Grammar, vocabulary, and lexical semantic shifts take place over time, resulting in a diachronic linguistic gap. As such, a considerable amount of texts are written in languages of different eras, which creates obstacles for natural language processing tasks... | ['Jun Wang', 'Qi Su', 'Xuemei Tang'] | 2022-09-07 | that-slepen-al-the-nyght-with-open-ye-cross-1 | https://aclanthology.org/2022.acl-long.540 | https://aclanthology.org/2022.acl-long.540.pdf | acl-2022-5 | ['chinese-word-segmentation'] | ['natural-language-processing'] | [ 3.15812752e-02 -6.08489275e-01 -3.92758489e-01 -4.70700711e-01
-3.47553164e-01 -6.67457998e-01 2.34008551e-01 7.02676401e-02
-6.67480886e-01 4.58913714e-01 1.96763322e-01 -6.81498468e-01
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3.49855900e-01 3.45735580e-01 4.31575388e-01 -4.45224077... | [9.951775550842285, 10.155074119567871] |
413d8185-b95a-4b31-922b-e1eaf8aebbe4 | continuous-pseudo-label-rectified-domain | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Gong_Continuous_Pseudo-Label_Rectified_Domain_Adaptive_Semantic_Segmentation_With_Implicit_Neural_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Gong_Continuous_Pseudo-Label_Rectified_Domain_Adaptive_Semantic_Segmentation_With_Implicit_Neural_CVPR_2023_paper.pdf | Continuous Pseudo-Label Rectified Domain Adaptive Semantic Segmentation With Implicit Neural Representations | Unsupervised domain adaptation (UDA) for semantic segmentation aims at improving the model performance on the unlabeled target domain by leveraging a labeled source domain. Existing approaches have achieved impressive progress by utilizing pseudo-labels on the unlabeled target-domain images. Yet the low-quality pse... | ['Luc van Gool', 'Dengxin Dai', 'Martin Danelljan', 'Qin Wang', 'Rui Gong'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['unsupervised-domain-adaptation', 'pseudo-label'] | ['methodology', 'miscellaneous'] | [ 3.84759098e-01 2.25756764e-01 -2.24258214e-01 -8.63226473e-01
-1.25725043e+00 -6.00954533e-01 4.43771154e-01 -3.83535236e-01
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5.94741344e-01 6.06601179e-01 9.37613100e-02 7.22236335... | [9.688780784606934, 1.2715874910354614] |
0cc80727-2706-423f-849c-4cab8dba3769 | a-rational-distributed-process-level-account | 1801.10186 | null | http://arxiv.org/abs/1801.10186v1 | http://arxiv.org/pdf/1801.10186v1.pdf | A Rational Distributed Process-level Account of Independence Judgment | It is inconceivable how chaotic the world would look to humans, faced with
innumerable decisions a day to be made under uncertainty, had they been lacking
the capacity to distinguish the relevant from the irrelevant---a capacity which
computationally amounts to handling probabilistic independence relations. The
highly ... | ['Ioannis N. Psaromiligkos', 'Ardavan S. Nobandegani'] | 2018-01-30 | null | null | null | null | ['detection-of-dependencies'] | ['methodology'] | [ 7.56449476e-02 4.48580474e-01 4.30663377e-01 -4.78250086e-01
-2.31371745e-01 -5.40435493e-01 7.75097191e-01 4.23662007e-01
-5.87197721e-01 6.83593154e-01 -1.87879652e-01 -8.21517766e-01
-1.02333474e+00 -8.20744634e-01 -5.45526266e-01 -6.67574883e-01
-3.11342925e-01 6.63491428e-01 1.72886357e-01 -2.43060097... | [8.74931526184082, 6.474265098571777] |
2af986f5-55b7-4b9e-a5da-5295c7653e12 | promix-combating-label-noise-via-maximizing | 2207.10276 | null | https://arxiv.org/abs/2207.10276v2 | https://arxiv.org/pdf/2207.10276v2.pdf | ProMix: Combating Label Noise via Maximizing Clean Sample Utility | The ability to train deep neural networks under label noise is appealing, as imperfectly annotated data are relatively cheaper to obtain. State-of-the-art approaches are based on semi-supervised learning(SSL), which selects small loss examples as clean and then applies SSL techniques for boosted performance. However, t... | ['Junbo Zhao', 'Lei Feng', 'Yiwen Dong', 'Ruixuan Xiao', 'Haobo Wang'] | 2022-07-21 | null | null | null | null | ['learning-with-noisy-labels', 'learning-with-noisy-labels'] | ['computer-vision', 'natural-language-processing'] | [-1.30496621e-01 6.67437315e-02 -2.86191821e-01 -6.85989022e-01
-1.54033947e+00 -3.77235860e-01 4.46494311e-01 2.10970774e-01
-5.38812399e-01 9.18873966e-01 -1.12723470e-01 -7.02944249e-02
3.72169018e-02 -6.75506234e-01 -8.86405349e-01 -7.31510043e-01
9.21469480e-02 3.65394592e-01 -2.04410050e-02 2.08266884... | [9.365472793579102, 3.885223150253296] |
b675c815-00bb-4194-9126-3cd032e2f622 | clustering-individuals-based-on-multivariate | 2212.01159 | null | https://arxiv.org/abs/2212.01159v1 | https://arxiv.org/pdf/2212.01159v1.pdf | Clustering individuals based on multivariate EMA time-series data | In the field of psychopathology, Ecological Momentary Assessment (EMA) methodological advancements have offered new opportunities to collect time-intensive, repeated and intra-individual measurements. This way, a large amount of data has become available, providing the means for further exploring mental disorders. Cons... | ['Anne Roefs', 'Lourens Waldorp', 'Gerasimos Spanakis', 'Mandani Ntekouli'] | 2022-12-02 | null | null | null | null | ['clustering-multivariate-time-series'] | ['time-series'] | [-1.75801039e-01 -4.48137760e-01 -2.06584319e-01 -5.07822573e-01
-4.13616151e-01 -2.55802095e-01 4.03533399e-01 8.77422631e-01
-3.84587109e-01 3.12375218e-01 -5.02980836e-02 4.08981666e-02
-7.50632346e-01 -4.94256705e-01 2.85371393e-01 -8.67899477e-01
-7.83777893e-01 4.20967847e-01 -3.20101321e-01 1.36133954... | [7.280033588409424, 3.449646472930908] |
b8414cf7-e66e-4bd8-bd9a-99de27d0b25e | self-supervised-one-shot-learning-for | 2303.05639 | null | https://arxiv.org/abs/2303.05639v2 | https://arxiv.org/pdf/2303.05639v2.pdf | Self-Supervised One-Shot Learning for Automatic Segmentation of StyleGAN Images | We propose a framework for the automatic one-shot segmentation of synthetic images generated by a StyleGAN. Our framework is based on the observation that the multi-scale hidden features in the GAN generator hold useful semantic information that can be utilized for automatic on-the-fly segmentation of the generated ima... | ['Avinash C. Kak', 'Ankit Manerikar'] | 2023-03-10 | null | null | null | null | ['one-shot-segmentation', 'one-shot-learning'] | ['computer-vision', 'methodology'] | [ 7.68046796e-01 6.91951990e-01 -5.24063520e-02 -4.49769467e-01
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3.03740531e-01 -9.56043601e-01 -1.15595150e+00 -1.07005310e+00
1.55187294e-01 8.66039515e-01 5.20901799e-01 -1.17178569... | [11.351344108581543, -0.31183692812919617] |
298e93bb-217b-400a-9891-8394b3403776 | stock-movement-prediction-based-on-bi-typed-1 | 2201.04965 | null | https://arxiv.org/abs/2201.04965v2 | https://arxiv.org/pdf/2201.04965v2.pdf | Stock Movement Prediction Based on Bi-typed Hybrid-relational Market Knowledge Graph via Dual Attention Networks | Stock Movement Prediction (SMP) aims at predicting listed companies' stock future price trend, which is a challenging task due to the volatile nature of financial markets. Recent financial studies show that the momentum spillover effect plays a significant role in stock fluctuation. However, previous studies typically ... | ['Ji Liu', 'Gang Kou', 'Fuzhen Zhuang', 'Qing Li', 'Xingyan Chen', 'Shaopeng Wei', 'Ying Liu', 'Huaming Du', 'Yu Zhao'] | 2022-01-11 | null | null | null | null | ['implicit-relations', 'stock-prediction'] | ['natural-language-processing', 'time-series'] | [-9.51033711e-01 5.23718037e-02 -7.45274186e-01 -5.48822880e-02
-6.03774562e-02 -6.54126644e-01 6.08272910e-01 -4.66664970e-01
5.71097806e-02 8.80299151e-01 5.09177923e-01 -6.18589342e-01
-1.91893354e-01 -1.34176600e+00 -6.61249518e-01 -2.30546266e-01
-1.42696396e-01 5.12844384e-01 2.85394341e-01 -4.93257165... | [4.30248498916626, 4.315986156463623] |
f14d4c04-472a-4b2f-9c2b-806994a799f6 | pyramid-region-based-slot-attention-network | 2206.10095 | null | https://arxiv.org/abs/2206.10095v1 | https://arxiv.org/pdf/2206.10095v1.pdf | Pyramid Region-based Slot Attention Network for Temporal Action Proposal Generation | It has been found that temporal action proposal generation, which aims to discover the temporal action instances within the range of the start and end frames in the untrimmed videos, can largely benefit from proper temporal and semantic context exploitation. The latest efforts were dedicated to considering the temporal... | ['Jun Hou', 'Lingbo Liu', 'Kunlin Yang', 'Rui Feng', 'Rui-Wei Zhao', 'Feng Zhang', 'Shuaicheng Li'] | 2022-06-21 | null | null | null | null | ['temporal-action-proposal-generation'] | ['computer-vision'] | [ 5.15230656e-01 6.17119335e-02 -5.24352014e-01 -2.20534623e-01
-7.86826849e-01 -1.77211687e-01 6.66563988e-01 -8.39962214e-02
-4.65086251e-01 6.94312274e-01 4.53220159e-01 5.12772202e-02
-1.22059602e-03 -6.66183472e-01 -7.80986249e-01 -6.95633352e-01
6.29314408e-02 -6.09537913e-03 7.67016649e-01 -2.24806681... | [8.47105884552002, 0.47273802757263184] |
2065fb0a-f7c4-4523-a0e4-400f663cfacc | classify-respiratory-abnormality-in-lung | 2208.13943 | null | https://arxiv.org/abs/2208.13943v1 | https://arxiv.org/pdf/2208.13943v1.pdf | Classify Respiratory Abnormality in Lung Sounds Using STFT and a Fine-Tuned ResNet18 Network | Recognizing patterns in lung sounds is crucial to detecting and monitoring respiratory diseases. Current techniques for analyzing respiratory sounds demand domain experts and are subject to interpretation. Hence an accurate and automatic respiratory sound classification system is desired. In this work, we took a data-d... | ['Xilin Liu', 'Chia-Hui Yeh', 'Hongliang Wang', 'Zizhao Chen'] | 2022-08-30 | null | null | null | null | ['sound-classification'] | ['audio'] | [ 3.90455395e-01 -2.94935405e-01 4.75000411e-01 -7.01750815e-02
-7.22169697e-01 -4.15674448e-01 2.36327380e-01 8.45985040e-02
-4.70018446e-01 4.86328632e-01 4.08124924e-01 -1.21791206e-01
-2.20905185e-01 -4.18842733e-01 -1.04570344e-01 -5.19829929e-01
-1.07668545e-02 6.94304109e-02 5.96046925e-01 -1.44436851... | [14.546807289123535, 3.912923574447632] |
728c8f28-bb58-490e-93d8-aabd1789c79d | toward-unsupervised-multi-object-discovery-in | 2007.02662 | null | https://arxiv.org/abs/2007.02662v2 | https://arxiv.org/pdf/2007.02662v2.pdf | Toward unsupervised, multi-object discovery in large-scale image collections | This paper addresses the problem of discovering the objects present in a collection of images without any supervision. We build on the optimization approach of Vo et al. (CVPR'19) with several key novelties: (1) We propose a novel saliency-based region proposal algorithm that achieves significantly higher overlap with ... | ['Patrick Pérez', 'Jean Ponce', 'Huy V. Vo'] | 2020-07-06 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/4433_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123680766.pdf | eccv-2020-8 | ['single-object-discovery', 'multi-object-colocalization', 'multi-object-discovery'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 4.97049838e-01 3.25912207e-01 -2.57665068e-01 -1.34068325e-01
-8.72668803e-01 -5.50910711e-01 6.95886075e-01 5.27479589e-01
-5.75459540e-01 6.22965276e-01 1.52653351e-01 2.00578012e-02
-1.13468260e-01 -5.47295392e-01 -9.27891314e-01 -5.85525870e-01
-9.93764102e-02 5.88223696e-01 9.01223958e-01 -1.94182009... | [9.404946327209473, 0.8826706409454346] |
f2fdb6b2-f109-4db8-a20e-1044a4c6bda4 | follow-us-and-become-famous-insights-and | 2301.06815 | null | https://arxiv.org/abs/2301.06815v1 | https://arxiv.org/pdf/2301.06815v1.pdf | Follow Us and Become Famous! Insights and Guidelines From Instagram Engagement Mechanisms | With 1.3 billion users, Instagram (IG) has also become a business tool. IG influencer marketing, expected to generate $33.25 billion in 2022, encourages companies and influencers to create trending content. Various methods have been proposed for predicting a post's popularity, i.e., how much engagement (e.g., Likes) it... | ['Ahmad-Reza Sadeghi', 'Mauro Conti', 'Marco Chilese', 'Pier Paolo Tricomi'] | 2023-01-17 | null | null | null | null | ['marketing'] | ['miscellaneous'] | [-3.37896198e-01 2.31520191e-01 -5.92553854e-01 -2.77590573e-01
-4.24060583e-01 -4.57790494e-01 8.89605165e-01 4.17311162e-01
-3.63147557e-01 7.67800152e-01 5.76270878e-01 -3.35183829e-01
7.77945593e-02 -1.19496500e+00 -7.77193427e-01 -2.95322537e-01
2.32210562e-01 3.91834021e-01 -3.41709256e-01 -1.98498473... | [10.18502426147461, 6.530834197998047] |
c96c6d86-61ba-464c-ac40-4da90164eef3 | a-geometrical-imaging-of-the-real-gap-between | 1701.05114 | null | http://arxiv.org/abs/1701.05114v2 | http://arxiv.org/pdf/1701.05114v2.pdf | A geometrical imaging of the real gap between economies of China and the United States | GDP of China is about 11 trillion dollars and GDP of the United States is
about 18 trillion dollars. Suppose that we know for the coming years, economy
of the US will experience a real growth rate equal to \%3 and economy of China
will experience a real growth as of \%6. Now, the question is how long does it
take for e... | [] | 2019-04-24 | null | null | null | null | ['geometrical-view'] | ['computer-vision'] | [-7.18542099e-01 3.03005546e-01 -1.79528937e-01 1.72802750e-02
2.41389852e-02 -7.48597503e-01 6.20099664e-01 -3.52287382e-01
-5.57815254e-01 9.06023204e-01 3.31479132e-01 -1.10486269e+00
-1.18354581e-01 -1.14586353e+00 -1.63881376e-01 -6.41765296e-01
-1.28434934e-02 4.53157037e-01 -2.64252815e-02 -7.91667759... | [5.7362895011901855, 4.111849308013916] |
fb03d5d8-afab-4058-8c8e-7fa9428d3a40 | machine-learning-in-and-out-of-equilibrium | 2306.03521 | null | https://arxiv.org/abs/2306.03521v1 | https://arxiv.org/pdf/2306.03521v1.pdf | Machine learning in and out of equilibrium | The algorithms used to train neural networks, like stochastic gradient descent (SGD), have close parallels to natural processes that navigate a high-dimensional parameter space -- for example protein folding or evolution. Our study uses a Fokker-Planck approach, adapted from statistical physics, to explore these parall... | ['Michael Hinczewski', 'Deniz Yuret', 'Alexander Strang', 'Alkan Kabakçıoğlu', 'Shishir Adhikari'] | 2023-06-06 | null | null | null | null | ['protein-folding', 'navigate'] | ['natural-language-processing', 'reasoning'] | [ 1.00012682e-01 -6.71968609e-02 8.24246258e-02 -2.50777841e-01
-4.08614874e-02 -4.98209029e-01 8.88572216e-01 -1.17684975e-01
-8.69958401e-01 1.23871505e+00 -3.11285645e-01 -5.56782782e-01
-3.45505744e-01 -8.03606927e-01 -8.32456112e-01 -1.48894703e+00
-1.75559521e-01 4.79451835e-01 4.19474095e-01 -4.45570678... | [6.139919281005859, 4.214087963104248] |
b13ed99d-f158-4e35-b9f3-8a8099ec80ea | raft-stereo-multilevel-recurrent-field | 2109.07547 | null | https://arxiv.org/abs/2109.07547v1 | https://arxiv.org/pdf/2109.07547v1.pdf | RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo Matching | We introduce RAFT-Stereo, a new deep architecture for rectified stereo based on the optical flow network RAFT. We introduce multi-level convolutional GRUs, which more efficiently propagate information across the image. A modified version of RAFT-Stereo can perform accurate real-time inference. RAFT-stereo ranks first o... | ['Jia Deng', 'Zachary Teed', 'Lahav Lipson'] | 2021-09-15 | null | null | null | null | ['stereo-depth-estimation'] | ['computer-vision'] | [-2.86513597e-01 -2.38902956e-01 4.60136775e-03 -4.87169564e-01
-3.71903449e-01 -5.49407482e-01 4.75621939e-01 -5.29969752e-01
-5.75690091e-01 7.69770503e-01 5.37424386e-01 -3.88785750e-02
1.71218440e-01 -6.07579708e-01 -8.02318394e-01 -1.85651422e-01
-2.24962402e-02 2.86550224e-01 3.49191844e-01 -3.63671809... | [8.615640640258789, -1.9343127012252808] |
c5ac6ecc-0e81-49bc-9e72-3dcd9eff9c02 | age-and-gender-classification-using | null | null | https://talhassner.github.io/home/publication/2015_CVPR | https://talhassner.github.io/home/projects/cnn_agegender/CVPR2015_CNN_AgeGenderEstimation.pdf | Age and Gender Classification using Convolutional Neural Networks | Automatic age and gender classification has become relevant to an increasing amount of applications, particularly since the rise of social platforms and social media. Nevertheless, performance of existing methods on real-world images is still significantly lacking, especially when compared to the tremendous leaps in pe... | ['Tal Hassner', 'Gil Levi'] | 2015-10-26 | null | null | null | 2015-ieee-conference-on-computer-vision-and-1 | ['age-and-gender-estimation', 'age-and-gender-classification'] | ['computer-vision', 'computer-vision'] | [-7.24944174e-02 1.28922224e-01 -8.37620050e-02 -6.69168532e-01
-2.71296412e-01 -1.55956954e-01 8.46368372e-01 2.03469813e-01
-7.08279431e-01 7.18863428e-01 6.85794130e-02 -2.74972636e-02
5.61544113e-02 -8.18718970e-01 -4.62680161e-01 -5.30528903e-01
-2.76370287e-01 4.93347168e-01 -2.78595716e-01 -2.15931714... | [13.523804664611816, 0.9667657017707825] |
1a3361d5-862c-4c46-add9-49f0bc396f65 | efficient-linear-attention-for-fast-and | 2204.07731 | null | https://arxiv.org/abs/2204.07731v3 | https://arxiv.org/pdf/2204.07731v3.pdf | Efficient Linear Attention for Fast and Accurate Keypoint Matching | Recently Transformers have provided state-of-the-art performance in sparse matching, crucial to realize high-performance 3D vision applications. Yet, these Transformers lack efficiency due to the quadratic computational complexity of their attention mechanism. To solve this problem, we employ an efficient linear attent... | ['Satoshi Komorita', 'Suwichaya Suwanwimolkul'] | 2022-04-16 | null | null | null | null | ['image-matching'] | ['computer-vision'] | [-2.18914151e-01 -2.40572289e-01 -1.75286591e-01 -2.24496752e-01
-1.08307338e+00 -2.12052941e-01 6.23215973e-01 8.80585462e-02
-3.84146631e-01 1.32440150e-01 1.00971349e-01 1.55925512e-01
-2.82370567e-01 -6.15085840e-01 -8.08744967e-01 -6.19779050e-01
-9.26126838e-02 7.24636376e-01 3.71959776e-01 9.68144508... | [8.032221794128418, -1.9441312551498413] |
a3db831c-721b-48f2-b12f-07241a78842c | deep-no-reference-tone-mapped-image-quality | 2002.03165 | null | https://arxiv.org/abs/2002.03165v1 | https://arxiv.org/pdf/2002.03165v1.pdf | Deep No-reference Tone Mapped Image Quality Assessment | The process of rendering high dynamic range (HDR) images to be viewed on conventional displays is called tone mapping. However, tone mapping introduces distortions in the final image which may lead to visual displeasure. To quantify these distortions, we introduce a novel no-reference quality assessment technique for t... | ['Sumohana S. Channappayya', 'Sathya Veera Reddy Dendi', 'Chandra Sekhar Ravuri', 'Shanmuganathan Raman', 'Rajesh Sureddi'] | 2020-02-08 | null | null | null | null | ['tone-mapping'] | ['computer-vision'] | [ 6.33441627e-01 -1.70958325e-01 3.07707429e-01 -5.43576598e-01
-7.24468052e-01 -4.57771331e-01 7.19792962e-01 -2.42351085e-01
-1.15310907e-01 5.02915442e-01 1.33920833e-01 -1.79032728e-01
2.65431292e-02 -9.50553060e-01 -7.84154117e-01 -5.97392440e-01
2.13586971e-01 6.90395460e-02 3.44411165e-01 -3.55661541... | [11.039560317993164, -2.2052266597747803] |
8731860f-b77c-4e04-b7e6-189b5a799c4b | codet5-identifier-aware-unified-pre-trained | 2109.00859 | null | https://arxiv.org/abs/2109.00859v1 | https://arxiv.org/pdf/2109.00859v1.pdf | CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation | Pre-trained models for Natural Languages (NL) like BERT and GPT have been recently shown to transfer well to Programming Languages (PL) and largely benefit a broad set of code-related tasks. Despite their success, most current methods either rely on an encoder-only (or decoder-only) pre-training that is suboptimal for ... | ['Steven C. H. Hoi', 'Shafiq Joty', 'Weishi Wang', 'Yue Wang'] | 2021-09-02 | null | https://aclanthology.org/2021.emnlp-main.685 | https://aclanthology.org/2021.emnlp-main.685.pdf | emnlp-2021-11 | ['code-translation', 'text-to-code-generation'] | ['computer-code', 'computer-code'] | [ 2.93867081e-01 1.30791426e-01 -5.54036021e-01 -2.38769144e-01
-1.17211306e+00 -6.73288703e-01 3.34919661e-01 2.68682867e-01
2.10227340e-01 1.43734753e-01 2.26116836e-01 -7.64140248e-01
4.99150395e-01 -6.23321354e-01 -9.60728884e-01 3.20196035e-03
2.33306676e-01 2.58323342e-01 1.42624183e-02 -1.83338076... | [7.671043395996094, 7.9166951179504395] |
fae3dbb0-205d-4e5d-95a3-e098de1a7dda | unsupervised-domain-adaptation-by-learning | 2303.09350 | null | https://arxiv.org/abs/2303.09350v2 | https://arxiv.org/pdf/2303.09350v2.pdf | Unsupervised domain adaptation by learning using privileged information | Successful unsupervised domain adaptation (UDA) is guaranteed only under strong assumptions such as covariate shift and overlap between input domains. The latter is often violated in high-dimensional applications such as image classification which, despite this challenge, continues to serve as inspiration and benchmark... | ['Fredrik D. Johansson', 'Anton Matsson', 'Adam Breitholtz'] | 2023-03-16 | null | null | null | null | ['multi-label-image-classification'] | ['computer-vision'] | [ 9.39936399e-01 2.58951753e-01 -5.39952874e-01 -6.29505932e-01
-9.76473927e-01 -7.74417400e-01 5.61615646e-01 4.19764876e-01
-6.86468720e-01 1.05966806e+00 -3.97675633e-02 -3.39053184e-01
-2.57168680e-01 -4.10562247e-01 -8.44574630e-01 -7.60465682e-01
9.60085019e-02 7.57093906e-01 3.17834839e-02 2.45177642... | [10.328304290771484, 3.246525287628174] |
7efa04dd-d2f6-42f7-8483-d2da0bd7aeb9 | accurate-and-robust-pulmonary-nodule | 1907.11704 | null | https://arxiv.org/abs/1907.11704v1 | https://arxiv.org/pdf/1907.11704v1.pdf | Accurate and Robust Pulmonary Nodule Detection by 3D Feature Pyramid Network with Self-supervised Feature Learning | Accurate detection of pulmonary nodules with high sensitivity and specificity is essential for automatic lung cancer diagnosis from CT scans. Although many deep learning-based algorithms make great progress for improving the accuracy of nodule detection, the high false positive rate is still a challenging problem which... | ['YingLi Tian', 'Jingya Liu', 'Oguz Akin', 'Liangliang Cao'] | 2019-07-25 | null | null | null | null | ['lung-cancer-diagnosis'] | ['medical'] | [ 7.36228302e-02 1.42144235e-02 -2.83731908e-01 -1.94300354e-01
-8.17941070e-01 -2.55544603e-01 2.30297029e-01 -2.09447965e-02
-3.96432430e-01 3.29665482e-01 -6.43261820e-02 -2.17392430e-01
-3.92094910e-01 -8.98591280e-01 -3.50061089e-01 -8.54455233e-01
-8.17196295e-02 6.24426961e-01 9.34309483e-01 2.08643079... | [15.369974136352539, -2.1442806720733643] |
2a6c5222-7d3c-457c-9cc3-4546d4736f95 | revise-self-supervised-speech-resynthesis | 2212.11377 | null | https://arxiv.org/abs/2212.11377v1 | https://arxiv.org/pdf/2212.11377v1.pdf | ReVISE: Self-Supervised Speech Resynthesis with Visual Input for Universal and Generalized Speech Enhancement | Prior works on improving speech quality with visual input typically study each type of auditory distortion separately (e.g., separation, inpainting, video-to-speech) and present tailored algorithms. This paper proposes to unify these subjects and study Generalized Speech Enhancement, where the goal is not to reconstruc... | ['Yossi Adi', 'Jacob Donley', 'Bowen Shi', 'Tal Remez', 'Wei-Ning Hsu'] | 2022-12-21 | null | null | null | null | ['video-synchronization', 'text-to-speech-synthesis', 'audio-visual-speech-recognition'] | ['computer-vision', 'speech', 'speech'] | [ 2.90184617e-01 -2.33800814e-01 1.73212335e-01 -6.94436282e-02
-1.22966313e+00 -3.71195287e-01 4.15485620e-01 -3.08126807e-01
-1.52866185e-01 4.35381383e-01 5.99419534e-01 -3.24868381e-01
3.17958981e-01 -9.19867754e-02 -8.97643745e-01 -7.47873545e-01
2.39853874e-01 -3.54991078e-01 8.26958716e-02 -2.32763246... | [14.586451530456543, 5.479820251464844] |
9d79bffa-d83c-4079-97c6-536e31889997 | social-media-medical-concept-normalization | null | null | https://aclanthology.org/2020.knlp-1.3 | https://aclanthology.org/2020.knlp-1.3.pdf | Social Media Medical Concept Normalization using RoBERTa in Ontology Enriched Text Similarity Framework | Pattisapu et al. (2020) formulate medical concept normalization (MCN) as text similarity problem and propose a model based on RoBERTa and graph embedding based target concept vectors. However, graph embedding techniques ignore valuable information available in the clinical ontology like concept description and synonyms... | ['Sivanesan Sangeetha', 'Katikapalli Subramanyam Kalyan'] | null | null | null | null | aacl-knlp-2020-12 | ['medical-concept-normalization'] | ['medical'] | [ 2.72689581e-01 3.77091169e-01 -3.32607925e-01 -1.40446827e-01
-2.58123428e-01 -3.44961852e-01 6.50206029e-01 1.04361534e+00
-7.43217528e-01 4.22870785e-01 6.98112309e-01 -1.15248822e-01
-2.03631729e-01 -1.00632513e+00 -1.42000020e-01 -1.42034367e-01
4.24659438e-02 4.49930012e-01 2.02156916e-01 -7.13528156... | [8.545105934143066, 8.51929759979248] |
d2d1034f-4142-4d35-8d99-0936aa7de338 | trajectoryformer-3d-object-tracking | 2306.05888 | null | https://arxiv.org/abs/2306.05888v1 | https://arxiv.org/pdf/2306.05888v1.pdf | TrajectoryFormer: 3D Object Tracking Transformer with Predictive Trajectory Hypotheses | 3D multi-object tracking (MOT) is vital for many applications including autonomous driving vehicles and service robots. With the commonly used tracking-by-detection paradigm, 3D MOT has made important progress in recent years. However, these methods only use the detection boxes of the current frame to obtain trajectory... | ['Hongsheng Li', 'Simon See', 'Ka Chun Cheung', 'Qiang Wang', 'Benjin Zhu', 'Chao Zhang', 'Shaoshuai Shi', 'Xuesong Chen'] | 2023-06-09 | null | null | null | null | ['object-tracking', 'multi-object-tracking', '3d-object-tracking', '3d-multi-object-tracking'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [-3.95091206e-01 -2.71843761e-01 -2.12709799e-01 -1.31377637e-01
-5.14989138e-01 -3.28925103e-01 4.80520368e-01 1.37153685e-01
-3.45520973e-01 3.41407180e-01 -2.58943766e-01 7.08285943e-02
8.39956999e-02 -6.79294705e-01 -8.14011931e-01 -7.91532457e-01
-2.96307802e-01 6.13846481e-01 1.19357538e+00 6.08507954... | [6.63966703414917, -2.21549129486084] |
0ffc687a-f468-4bdc-a900-945021e9a475 | starcraft-micromanagement-with-reinforcement | 1804.00810 | null | http://arxiv.org/abs/1804.00810v1 | http://arxiv.org/pdf/1804.00810v1.pdf | StarCraft Micromanagement with Reinforcement Learning and Curriculum Transfer Learning | Real-time strategy games have been an important field of game artificial
intelligence in recent years. This paper presents a reinforcement learning and
curriculum transfer learning method to control multiple units in StarCraft
micromanagement. We define an efficient state representation, which breaks down
the complexit... | ['Yuanheng Zhu', 'Kun Shao', 'Dongbin Zhao'] | 2018-04-03 | null | null | null | null | ['real-time-strategy-games'] | ['playing-games'] | [-1.75785825e-01 2.42572464e-02 -1.36323586e-01 1.74453735e-01
-4.15589333e-01 -5.71921170e-01 3.44475329e-01 -2.09323376e-01
-9.39456105e-01 1.11992621e+00 -2.07211196e-01 -2.61470765e-01
-2.28986382e-01 -1.00211251e+00 -6.22317195e-01 -8.66615951e-01
-3.59701872e-01 5.29199481e-01 7.54276514e-01 -1.07276070... | [3.6259562969207764, 1.6431585550308228] |
b3dca677-345c-4494-8424-27a3eb805e13 | rcsearcher-reaction-center-identification-in | 2301.12071 | null | https://arxiv.org/abs/2301.12071v1 | https://arxiv.org/pdf/2301.12071v1.pdf | RCsearcher: Reaction Center Identification in Retrosynthesis via Deep Q-Learning | The reaction center consists of atoms in the product whose local properties are not identical to the corresponding atoms in the reactants. Prior studies on reaction center identification are mainly on semi-templated retrosynthesis methods. Moreover, they are limited to single reaction center identification. However, ma... | ['Fei Ma', 'Zhenfu Liu', 'Binjie Hong', 'Zuo Zeng', 'Zixun Lan'] | 2023-01-28 | null | null | null | null | ['retrosynthesis'] | ['medical'] | [ 4.36738253e-01 2.75581509e-01 -8.26250136e-01 2.09596425e-01
-2.71885693e-01 -9.25316334e-01 6.79452181e-01 2.12575167e-01
-2.65309196e-02 7.28416145e-01 2.21244916e-01 -7.49137461e-01
-2.89022103e-02 -9.42677081e-01 -7.53433645e-01 -1.14232838e+00
3.91816050e-02 4.59682673e-01 1.35442942e-01 -3.93262029... | [4.496026515960693, 6.1087965965271] |
c6bfb9df-4517-44ca-b81c-a3715a49c923 | brain-anatomy-prior-modeling-to-forecast | 2306.11837 | null | https://arxiv.org/abs/2306.11837v2 | https://arxiv.org/pdf/2306.11837v2.pdf | Brain Anatomy Prior Modeling to Forecast Clinical Progression of Cognitive Impairment with Structural MRI | Brain structural MRI has been widely used to assess the future progression of cognitive impairment (CI). Previous learning-based studies usually suffer from the issue of small-sized labeled training data, while there exist a huge amount of structural MRIs in large-scale public databases. Intuitively, brain anatomical s... | ['Mingxia Liu', 'Guy G. Potter', 'Shijun Qiu', 'David C. Steffens', 'Li Wang', 'Lihong Wang', 'Jinjian Wu', 'Lintao Zhang'] | 2023-06-20 | null | null | null | null | ['trajectory-prediction', 'image-reconstruction', 'mri-reconstruction', 'anatomy'] | ['computer-vision', 'computer-vision', 'computer-vision', 'miscellaneous'] | [ 4.16244507e-01 1.92740753e-01 -1.30100548e-01 -6.81854367e-01
-1.06390870e+00 -1.46337256e-01 3.49023938e-01 -1.98476896e-01
-5.61700940e-01 7.62757599e-01 5.97227633e-01 -5.57733119e-01
-1.05093040e-01 -6.75601542e-01 -8.15210402e-01 -4.37365413e-01
-4.79664207e-01 8.73232961e-01 3.76354784e-01 3.28699499... | [14.364457130432129, -1.919914722442627] |
9f6d4ae9-5941-4310-a5b6-dc8518854c51 | wikisqe-a-large-scale-dataset-for-sentence | 2305.05928 | null | https://arxiv.org/abs/2305.05928v1 | https://arxiv.org/pdf/2305.05928v1.pdf | WikiSQE: A Large-Scale Dataset for Sentence Quality Estimation in Wikipedia | Wikipedia can be edited by anyone and thus contains various quality sentences. Therefore, Wikipedia includes some poor-quality edits, which are often marked up by other editors. While editors' reviews enhance the credibility of Wikipedia, it is hard to check all edited text. Assisting in this process is very important,... | ['Mamoru Komachi', 'Satoshi Sekine', 'Kenichiro Ando'] | 2023-05-10 | null | null | null | null | ['automated-essay-scoring'] | ['natural-language-processing'] | [-4.79985058e-01 3.42087179e-01 -2.26514101e-01 -2.45815158e-01
-1.04769516e+00 -4.85483915e-01 3.13494802e-01 7.78219819e-01
-5.88390350e-01 1.14955378e+00 4.44693297e-01 -1.04145892e-01
-2.02074185e-01 -8.39797914e-01 -6.34998918e-01 9.00347456e-02
2.74702638e-01 4.32118654e-01 2.54191369e-01 -4.85045463... | [12.018891334533691, 9.402193069458008] |
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