| Nome |
# |
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Human-Driven FOL Explanations of Deep Learning, file e0feeaa9-3751-44d2-e053-6605fe0a8db0
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330
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Regularizing deep networks with prior knowledge: A constraint-based approach, file e0feeaaa-60a7-44d2-e053-6605fe0a8db0
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215
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null, file e0feeaaa-249a-44d2-e053-6605fe0a8db0
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108
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The principle of least cognitive action, file e0feeaa9-300e-44d2-e053-6605fe0a8db0
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76
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null, file e0feeaaa-1f34-44d2-e053-6605fe0a8db0
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67
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Gravitational models explain shifts on human visual attention, file e0feeaa9-9548-44d2-e053-6605fe0a8db0
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59
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Domain Knowledge Alleviates Adversarial Attacks in Multi-Label Classifiers, file 17e33002-da70-4e2b-b12a-c85a172f5c83
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45
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Entropy-Based Logic Explanations of Neural Networks, file 8680d8c5-304a-424c-a1ac-787c07deeb97
|
39
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The polymorphism L412F in TLR3 inhibits autophagy and is a marker of severe COVID-19 in males., file e8887f01-8ecd-408c-a6e4-5b1d8e6d5c42
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38
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Relational neural machines, file e0feeaa9-2e7c-44d2-e053-6605fe0a8db0
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37
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Foundations of support constraint machines, file e0feeaab-4d71-44d2-e053-6605fe0a8db0
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35
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Being Friends Instead of Adversaries: Deep Networks Learn from Data Simplified by Other Networks, file e6e30fbd-6a08-4c4f-bcd3-d7488f4bdf20
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31
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Carriers of ADAMTS13 Rare Variants Are at High Risk of Life-Threatening COVID-19, file 96f4df86-86ac-462c-8a12-3ca958289b45
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28
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Inference in relational neural machines, file e0feeaa9-7ec8-44d2-e053-6605fe0a8db0
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27
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Logic Explained Networks, file d72b2308-0e3c-4c1e-b67a-a69477a25a80
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25
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T-norms driven loss functions for machine learning, file c5346fa6-11da-4972-bb7b-8109c5fd7073
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23
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A lagrangian approach to information propagation in graph neural networks, file e0feeaa9-6028-44d2-e053-6605fe0a8db0
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23
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Guest Editorial: Non-Euclidean Machine Learning, file dbf195d1-8b50-4416-915e-c4ac318e6b16
|
22
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null, file e0feeaab-b657-44d2-e053-6605fe0a8db0
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21
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Local propagation of visual stimuli in focus of attention, file 01947428-ddb3-4625-9261-d5fbc36e4634
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20
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SARS-CoV-2 susceptibility and COVID-19 disease severity are associated with genetic variants affecting gene expression in a variety of tissues, file b27bf0e9-010f-47e6-a116-48649af66e36
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20
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Learning visual features under motion invariance, file a82cd392-490b-420c-9a78-9eb825fbcfeb
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17
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Similarity Learning of Graph-Based Image Representations, file e0feeaa4-e255-44d2-e053-6605fe0a8db0
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17
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Can machines learn to see without visual databases?, file e0feeaab-b7f1-44d2-e053-6605fe0a8db0
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17
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Visual Features and Their Own Optical Flow, file 3b729878-2132-46cb-9d0f-ff27082d08a4
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16
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Lagrangian Propagation Graph Neural Networks, file e0feeaa9-1c1e-44d2-e053-6605fe0a8db0
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16
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Gain- and Loss-of-Function CFTR Alleles Are Associated with COVID-19 Clinical Outcomes, file ebc57d5c-8d48-406c-b361-06dd583c7b6b
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12
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Foundations of support constraint machines, file e0feeaa5-09ae-44d2-e053-6605fe0a8db0
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11
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Whole-genome sequencing reveals host factors underlying critical COVID-19, file b47ac884-9c8d-4cf9-84ab-2c6dfbaa52e5
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10
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Graph Neural Networks for Graph Drawing, file 04cf29e7-3c38-4cdd-b8ae-eb4f73f88063
|
9
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Shorter androgen receptor polyQ alleles protect against life-threatening COVID-19 disease in European males, file 434b66aa-b383-4316-9027-8d28bd50a93f
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9
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Graph neural networks for object localization, file af43e16c-bf9e-4443-9d94-4b991a239162
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9
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Neural networks for relational learning: an experimental comparisonn, file e0feeaa4-f551-44d2-e053-6605fe0a8db0
|
8
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Detecting near-replicas on the Web by content and hyperlink analysis, file e0feeaa4-dac2-44d2-e053-6605fe0a8db0
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7
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SARS-CoV-2 susceptibility and COVID-19 disease severity are associated with genetic variants affecting gene expression in a variety of tissues, file 4b6371f4-5458-443b-941a-cf972c6ace63
|
6
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A Constraint-Based Approach to Learning and Reasoning, file 985dcf6f-d5a1-4145-aaee-378ebd956314
|
6
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Logic Explained Networks, file d431fefe-6408-4a29-8ec2-33fb3610689c
|
6
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Detecting near replicas on the Web by content and hyperlink analysis, file e0feeaa4-cd2e-44d2-e053-6605fe0a8db0
|
6
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Learning with mixed hard/soft pointwise constraints, file e0feeaa5-c942-44d2-e053-6605fe0a8db0
|
6
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Learning visual features under motion invariance, file e0feeaa8-c792-44d2-e053-6605fe0a8db0
|
6
|
|
Continual Learning through Hamilton Equations, file a230dc87-1165-47ba-bfc4-55e195398357
|
5
|
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Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity, file d7cf9e79-1409-43ab-b33c-176302109711
|
5
|
|
Similarity learning for graph-based image representations, file e0feeaa4-e11a-44d2-e053-6605fe0a8db0
|
5
|
|
Gravitational laws of focus of attention, file e0feeaa9-2d7d-44d2-e053-6605fe0a8db0
|
5
|
|
Developing constrained neural units over time, file e0feeaa9-54b1-44d2-e053-6605fe0a8db0
|
5
|
|
Logic Explained Networks, file 025c5092-04a4-4be0-af1f-cbbd8cd73724
|
4
|
|
Employing a systematic approach to biobanking and analyzing clinical and genetic data for advancing COVID-19 research, file 2280e542-6881-4bb1-813e-9235d2285d2d
|
4
|
|
Learning Logic Explanations by Neural Networks, file 5ed83bb2-1e25-4ee4-a52a-b7935fa371f4
|
4
|
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Foveated Neural Computation, file d1d1e647-9e2f-4af0-91e7-8b3d22bf7594
|
4
|
|
Multitask Kernel-based Learning with First-Order Logic Constraints, file e0feeaa4-cdae-44d2-e053-6605fe0a8db0
|
4
|
|
Web-Browser Access Through Voice Input and Page Interest Prediction, file e0feeaa4-f8f0-44d2-e053-6605fe0a8db0
|
4
|
|
Semantic-based regularization for learning and inference, file e0feeaa9-5cc5-44d2-e053-6605fe0a8db0
|
4
|
|
Friendly Training: Neural Networks Can Adapt Data to Make Learning Easier, file e0feeaab-c523-44d2-e053-6605fe0a8db0
|
4
|
|
Italian Crossword Generator: Enhancing Education through Interactive Word Puzzles, file 6733b6b9-62a5-4860-bdda-36be9fb67d99
|
3
|
|
En Plein Air Visual Agents, file e0feeaa5-8456-44d2-e053-6605fe0a8db0
|
3
|
|
Learning with Box Kernels, file e0feeaa5-bd82-44d2-e053-6605fe0a8db0
|
3
|
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Learning as Constraint Reactions, file e0feeaa5-bd88-44d2-e053-6605fe0a8db0
|
3
|
|
Learning efficiently in semantic based regularization, file e0feeaa5-e2ab-44d2-e053-6605fe0a8db0
|
3
|
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Focus of attention improves information transfer in visual features, file e0feeaaa-149c-44d2-e053-6605fe0a8db0
|
3
|
|
HEmog: A White-Box Model to Unveil the Connection Between Saliency Information and Human Head Motion in Virtual Reality, file e0feeaab-bec0-44d2-e053-6605fe0a8db0
|
3
|
|
ArabIcros: AI-Powered Arabic Crossword Puzzle Generation for Educational Applications, file 4086a607-c3a8-49ee-9c25-773f3bdf0d04
|
2
|
|
Toward Novel Optimizers: A Moreau-Yosida View of Gradient-Based Learning, file 88da1d77-939b-4f40-b1b9-2539ce05b14e
|
2
|
|
Towards next generation CiteSeer: a flexible architecture for digital library deployment, file e0feeaa4-cd37-44d2-e053-6605fe0a8db0
|
2
|
|
Investigations into the application of Graph Neural Networks to large-scale recommender systems, file e0feeaa4-d575-44d2-e053-6605fe0a8db0
|
2
|
|
Semi-supervised Learning with Constraints for Multi-view Object Recognition, file e0feeaa4-dacc-44d2-e053-6605fe0a8db0
|
2
|
|
Does Terminal Attractor Guarantee Global Convergence?, file e0feeaa4-de1d-44d2-e053-6605fe0a8db0
|
2
|
|
Inside PageRank, file e0feeaa4-e7d2-44d2-e053-6605fe0a8db0
|
2
|
|
Graph Neural Networks for Ranking Web Pages, file e0feeaa4-ea81-44d2-e053-6605fe0a8db0
|
2
|
|
Towards Developmental AI: The paradox of ravenous intelligent agents, file e0feeaa4-ee08-44d2-e053-6605fe0a8db0
|
2
|
|
Integrating Logic Knowledge into Graph Regularization: an application to image tagging, file e0feeaa4-ee6e-44d2-e053-6605fe0a8db0
|
2
|
|
Emulazione vocale del mouse per soggetti con disabilità motoria, file e0feeaa4-f8fc-44d2-e053-6605fe0a8db0
|
2
|
|
Inference, Learning, and Laws of Nature, file e0feeaa5-0318-44d2-e053-6605fe0a8db0
|
2
|
|
Constraint Verification With Kernel Machines, file e0feeaa5-bd80-44d2-e053-6605fe0a8db0
|
2
|
|
Support constraint machines, file e0feeaa5-c563-44d2-e053-6605fe0a8db0
|
2
|
|
Kernel methods for minimum entropy encoding, file e0feeaa5-c7c0-44d2-e053-6605fe0a8db0
|
2
|
|
Learning with box kernels, file e0feeaa5-c7c2-44d2-e053-6605fe0a8db0
|
2
|
|
On-line Video Motion Estimation by Invariant Receptive Inputs, file e0feeaa5-c8c5-44d2-e053-6605fe0a8db0
|
2
|
|
Dealing with mixed hard/soft constraints via support constraint machines, file e0feeaa5-c90c-44d2-e053-6605fe0a8db0
|
2
|
|
Jointly Learning to Detect Emotions and Predict Facebook Reactions, file e0feeaa8-3f3a-44d2-e053-6605fe0a8db0
|
2
|
|
Motion invariance in visual environments, file e0feeaa8-4009-44d2-e053-6605fe0a8db0
|
2
|
|
A Constraint-Based Approach to Learning and Explanation, file e0feeaa9-3e6d-44d2-e053-6605fe0a8db0
|
2
|
|
Toward Improving the Evaluation of Visual Attention Models: A Crowdsourcing Approach, file e0feeaa9-88dd-44d2-e053-6605fe0a8db0
|
2
|
|
Generating Facial Expressions Associated with Text, file e0feeaa9-88e1-44d2-e053-6605fe0a8db0
|
2
|
|
Learning to Identify Drilling Defects in Turbine Blades with Single Stage Detectors, file e0feeaaa-1645-44d2-e053-6605fe0a8db0
|
2
|
|
Sailenv: Learning in virtual visual environments made simple, file e0feeaaa-5c53-44d2-e053-6605fe0a8db0
|
2
|
|
A language modeling-like approach to sketching, file e0feeaab-be99-44d2-e053-6605fe0a8db0
|
2
|
|
Messing Up 3D Virtual Environments: Transferable Adversarial 3D Objects, file e0feeaab-de67-44d2-e053-6605fe0a8db0
|
2
|
|
A Lagrangian framework for learning in graph neural networks, file e1906ccb-5d7a-437a-98dc-069008c24d0e
|
2
|
|
PARTIME: Scalable and Parallel Processing Over Time with Deep Neural Networks, file 21acb3d0-b706-467f-8e72-c2cea3b2a998
|
1
|
|
Domain Knowledge Alleviates Adversarial Attacks in Multi-Label Classifiers, file 2f08239a-e365-418f-91b8-fb5d91d38654
|
1
|
|
Learning Logic Explanations by Neural Networks, file 5cdba9e1-77ac-4bde-b71c-2df7f32d5e38
|
1
|
|
Machine Learning: A Constraint-Based Approach, file 6e21f955-55ac-4968-be2e-e25fb7f915ba
|
1
|
|
WoA: An Infrastructural, Web-Based Approach to Digital Agriculture, file b0aaf3d7-1f04-4229-993a-345c23177b3a
|
1
|
|
Continual Learning with Pretrained Backbones by Tuning in the Input Space, file da962713-0084-4972-bb71-38ba169ba1da
|
1
|
|
Unified integration of explicit knowledge and learning by example in recurrent networks, file e0feeaa4-c236-44d2-e053-6605fe0a8db0
|
1
|
|
Optimal Learning in Artificial Neural Networks, file e0feeaa4-d0e9-44d2-e053-6605fe0a8db0
|
1
|
|
A Random-Walk Based Scoring Algorithm applied to Recommender Engines, file e0feeaa4-d0eb-44d2-e053-6605fe0a8db0
|
1
|
|
Recursive neural networks and graphs: dealing with cycles, file e0feeaa4-dac4-44d2-e053-6605fe0a8db0
|
1
|
|
Focus Crawling by Context Graphs, file e0feeaa4-dad9-44d2-e053-6605fe0a8db0
|
1
|
|
Graph matching using random walks, file e0feeaa4-db44-44d2-e053-6605fe0a8db0
|
1
|
| Totale |
1.629 |