| Nome |
# |
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Investigating the association between social interactions and personality states dynamics, file e3835193-ff79-72ef-e053-3705fe0ad821
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666
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Federated Multi-Task Attention for Cross-Individual Human Activity Recognition, file 3c6e051a-daf7-4b33-992a-6369f862201e
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233
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Improved multi-level protein–protein interaction prediction with semantic-based regularization, file e3835192-34e1-72ef-e053-3705fe0ad821
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136
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Constructive Preference Elicitation, file e3835194-2d0c-72ef-e053-3705fe0ad821
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127
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Constructive Layout Synthesis via Coactive Learning, file e3835193-fc9f-72ef-e053-3705fe0ad821
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121
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null, file e3835195-20f8-72ef-e053-3705fe0ad821
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104
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Learning MAX-SAT from Contextual Examples for Combinatorial Optimisation, file e3835199-47bd-72ef-e053-3705fe0ad821
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84
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Learning SMT(LRA) Constraints using SMT Solvers, file e3835196-0472-72ef-e053-3705fe0ad821
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66
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Explainable Interactive Machine Learning @ UNITN, file 677da57e-d045-4867-88b0-30f7e2ed4aaf
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61
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Learning Modulo Theories for constructive preference elicitation, file e3835199-236d-72ef-e053-3705fe0ad821
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55
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Putting human behavior predictability in context, file 2adb7803-9421-4f38-bfea-e74772b64f08
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41
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Learning MAX-SAT from Contextual Examples for Combinatorial Optimisation, file e3835197-ad2a-72ef-e053-3705fe0ad821
|
38
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Semantic Probabilistic Layers for Neuro-Symbolic Learning, file 095505fe-6293-476f-8d1d-02925ea1d08f
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35
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Making deep neural networks right for the right scientific reasons by interacting with their explanations, file e3835197-c53a-72ef-e053-3705fe0ad821
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33
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Learning Constraints from Examples, file e3835195-96c0-72ef-e053-3705fe0ad821
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30
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Inducing Sparse Programs for Learning Modulo Theories, file e3835193-ffc0-72ef-e053-3705fe0ad821
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29
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Neuro-Symbolic Constraint Programming for Structured Prediction, file 5bc4604b-0e27-4d60-992d-33ab6719da67
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28
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Structured Feedback for Preference Elicitation in Complex Domains, file e3835193-f842-72ef-e053-3705fe0ad821
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28
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GlanceNets: Interpretabile, Leak-proof Concept-based Models, file 15c024e9-60a3-441e-b885-cb9dbd302c6e
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26
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Decomposition Strategies for Constructive Preference Elicitation, file e3835194-51cf-72ef-e053-3705fe0ad821
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25
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Learning in the Wild with Incremental Skeptical Gaussian Processes, file e3835196-9205-72ef-e053-3705fe0ad821
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21
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Predictive spreadsheet autocompletion with constraints, file e3835197-c97e-72ef-e053-3705fe0ad821
|
20
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Concept-level debugging of part-prototype networks, file fd52cf1f-e197-4725-9adc-180f1e66f373
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20
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Learning MAX-SAT from contextual examples for combinatorial optimisation, file 72ab3dc5-ad41-4a64-98d0-b482d51986b1
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18
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Structured learning modulo theories, file e3835198-01e9-72ef-e053-3705fe0ad821
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18
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Efficient Generation of Structured Objects with Constrained Adversarial Networks, file e3835197-b516-72ef-e053-3705fe0ad821
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16
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Making deep neural networks right for the right scientific reasons by interacting with their explanations, file e3835197-c53b-72ef-e053-3705fe0ad821
|
15
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Interactive label cleaning with example-based explanations, file e3835199-7742-72ef-e053-3705fe0ad821
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15
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Machine Learning for Utility Prediction in Argument-Based Computational Persuasion, file d9eff7c3-5f34-4fb0-85a5-48c4b068b15f
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14
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Interactive label cleaning with example-based explanations, file e3835199-7743-72ef-e053-3705fe0ad821
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11
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Semantic Probabilistic Layers for Neuro-Symbolic Learning, file 3093bb4f-3f09-4089-aaab-ea51a90459ac
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10
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Co-creating Platformer Levels with
Constrained Adversarial Networks, file b087c890-c151-48c0-8c63-fc83f78d5ddc
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10
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Constructive Learning Modulo Theories, file e3835193-f845-72ef-e053-3705fe0ad821
|
10
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Structured learning modulo theories, file e3835194-4fff-72ef-e053-3705fe0ad821
|
10
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Lifelong Personal Context Recognition, file e3835199-f74a-72ef-e053-3705fe0ad821
|
10
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Efficient and Reliable Probabilistic Interactive Learning with Structured Outputs, file c8420bcd-98c9-4843-b34c-12259de63e2b
|
9
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Machine Learning for Utility Prediction in Argument-Based Computational Persuasion, file e3835199-e0be-72ef-e053-3705fe0ad821
|
9
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Toward a Unified Framework for Debugging Gray-box Models, file 7d7d365c-914b-4f2f-950a-5b96fa483f24
|
8
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Statistical Relational Learning for Proteomics: Function, Interactions and Evolution, file fc5aa50e-e954-48db-a351-e926c90be791
|
7
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Interpretability Is in the Mind of the Beholder: A Causal Framework for Human-Interpretable Representation Learning, file 575e1106-b046-46d2-9029-2a6b5af8393b
|
6
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Automating Layout Synthesis with Constructive Preference Elicitation, file e3835195-20fc-72ef-e053-3705fe0ad821
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6
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Not All Neuro-Symbolic Concepts Are Created Equal: Analysis and Mitigation of Reasoning Shortcuts, file eb52415e-076a-433e-ba7a-a0ef2b0426a1
|
5
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Notes on Stochastic Simulation of Chemical Kinetics with Cycle-Leaping, file 4a367e0d-52b0-4042-bbe9-9e68cdbac215
|
4
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To Transfer or Not to Transfer and Why? Meta-Transfer Learning for Explainable and Controllable Cross-Individual Activity Recognition, file 8680b293-237c-4c35-8c11-066a6b655a26
|
4
|
|
Learning MAX-SAT from contextual examples for combinatorial optimisation, file f167e12e-6279-425f-bb64-e1792b22d444
|
4
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Learning Mixed-Integer Linear Programs from Contextual Examples, file decdb47d-da24-4382-8f1f-04eba394d4f3
|
3
|
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Learning Weighted Model Integration Distributions, file e3835196-b950-72ef-e053-3705fe0ad821
|
3
|
|
Challenges in Interactive Machine Learning, file e3835197-c97c-72ef-e053-3705fe0ad821
|
3
|
|
Coactive critiquing: Elicitation of preferences and features, file e3835194-02ad-72ef-e053-3705fe0ad821
|
2
|
|
Multi-Modal Subjective Context Modelling and Recognition, file e3835196-fee6-72ef-e053-3705fe0ad821
|
2
|
|
A Compositional Atlas of Tractable Circuit Operations for Probabilistic Inference, file e3835199-31ff-72ef-e053-3705fe0ad821
|
2
|
|
Predictive spreadsheet autocompletion with constraints, file e3835199-f4be-72ef-e053-3705fe0ad821
|
2
|
|
Learning in the Wild with Incremental Skeptical Gaussian Processes, file 60867f82-eca4-4337-a1ef-cfaade2e4916
|
1
|
|
Explanatory interactive machine learning, file 8b3e93cd-b519-4604-9594-18849a2d6431
|
1
|
|
Learning Modulo Theories for constructive preference elicitation, file d6d7675c-8278-4e70-8faa-292d69befd19
|
1
|
|
Constructive Preference Elicitation for Multiple Users with Setwise Max-margin, file e3835193-fbb0-72ef-e053-3705fe0ad821
|
1
|
|
Constructive Preference Elicitation by Setwise Max-Margin Learning, file e3835193-ffbc-72ef-e053-3705fe0ad821
|
1
|
|
Constructive Preference Elicitation over Hybrid Combinatorial Spaces, file e3835194-51d2-72ef-e053-3705fe0ad821
|
1
|
|
Decomposition Strategies for Constructive Preference Elicitation, file e3835197-65cb-72ef-e053-3705fe0ad821
|
1
|
|
Making deep neural networks right for the right scientific reasons by interacting with their explanations, file e3835199-ee1c-72ef-e053-3705fe0ad821
|
1
|
|
Automating Layout Synthesis with Constructive Preference Elicitation, file e87a28ce-a255-4276-9511-5ead0cd55dae
|
1
|
| Totale |
2.272 |