| Nome |
# |
|
Investigating the association between social interactions and personality states dynamics, file e3835193-ff79-72ef-e053-3705fe0ad821
|
666
|
|
Counts-of-counts similarity for prediction and search in relational data, file e3835196-061d-72ef-e053-3705fe0ad821
|
264
|
|
Predicting Metal-Binding Sites from Protein Sequence, file e3835192-525f-72ef-e053-3705fe0ad821
|
225
|
|
Three distinct ribosome assemblies modulated by translation are the building blocks of polysomes, file e3835192-d576-72ef-e053-3705fe0ad821
|
204
|
|
Type Extension Trees for feature construction and learning in relational domains, file e3835192-34df-72ef-e053-3705fe0ad821
|
178
|
|
A Big Data and machine learning approach for network monitoring and security, file e3835196-6a97-72ef-e053-3705fe0ad821
|
170
|
|
Interpretability of multivariate brain maps in linear brain decoding: Definition, and heuristic quantification in multivariate analysis of MEG time-locked effects, file e3835193-ff61-72ef-e053-3705fe0ad821
|
159
|
|
Widespread uncoupling between transcriptome and translatome variations after a stimulus in mammalian cells., file e3835192-418b-72ef-e053-3705fe0ad821
|
142
|
|
Improved multi-level protein–protein interaction prediction with semantic-based regularization, file e3835192-34e1-72ef-e053-3705fe0ad821
|
136
|
|
Pyconstruct: Constraint Programming Meets Structured Prediction, file e3835195-2aeb-72ef-e053-3705fe0ad821
|
135
|
|
Constructive Preference Elicitation, file e3835194-2d0c-72ef-e053-3705fe0ad821
|
127
|
|
Constructive Layout Synthesis via Coactive Learning, file e3835193-fc9f-72ef-e053-3705fe0ad821
|
121
|
|
Classtering: Joint Classification and Clustering with Mixture of Factor Analysers, file e3835193-f957-72ef-e053-3705fe0ad821
|
117
|
|
null, file e3835195-20f8-72ef-e053-3705fe0ad821
|
104
|
|
Predicting zinc binding at the proteome level, file e3835191-ca16-72ef-e053-3705fe0ad821
|
96
|
|
The pywmi Framework and Toolbox for Probabilistic Inference using Weighted Model Integration, file e3835196-0cf3-72ef-e053-3705fe0ad821
|
95
|
|
Hybrid Probabilistic Inference with Logical and Algebraic Constraints: a Survey, file e3835199-519a-72ef-e053-3705fe0ad821
|
78
|
|
Learning SMT(LRA) Constraints using SMT Solvers, file e3835196-0472-72ef-e053-3705fe0ad821
|
67
|
|
Dealing with Mislabeling via Interactive Machine Learning, file e3835199-1ba6-72ef-e053-3705fe0ad821
|
67
|
|
Explainable Interactive Machine Learning @ UNITN, file 677da57e-d045-4867-88b0-30f7e2ed4aaf
|
61
|
|
An efficient procedure for mining egocentric temporal motifs, file e3835199-6b8c-72ef-e053-3705fe0ad821
|
59
|
|
Learning Modulo Theories for constructive preference elicitation, file e3835199-236d-72ef-e053-3705fe0ad821
|
56
|
|
A neuro-symbolic approach to structured event recognition, file e3835199-4a35-72ef-e053-3705fe0ad821
|
49
|
|
Putting human behavior predictability in context, file 2adb7803-9421-4f38-bfea-e74772b64f08
|
41
|
|
null, file e3835199-4596-72ef-e053-3705fe0ad821
|
41
|
|
Learning Aggregation Functions, file e3835199-519c-72ef-e053-3705fe0ad821
|
40
|
|
Give more data, awareness and control to individual citizens, and they will help COVID-19 containment, file e3835199-2f09-72ef-e053-3705fe0ad821
|
38
|
|
null, file e3835198-c17b-72ef-e053-3705fe0ad821
|
37
|
|
Give more data, awareness and control to individual citizens, and they will help COVID-19 containment, file e3835196-b954-72ef-e053-3705fe0ad821
|
33
|
|
Neuro-Symbolic Constraint Programming for Structured Prediction, file 5bc4604b-0e27-4d60-992d-33ab6719da67
|
31
|
|
Learning Constraints from Examples, file e3835195-96c0-72ef-e053-3705fe0ad821
|
30
|
|
Structured Feedback for Preference Elicitation in Complex Domains, file e3835193-f842-72ef-e053-3705fe0ad821
|
29
|
|
Inducing Sparse Programs for Learning Modulo Theories, file e3835193-ffc0-72ef-e053-3705fe0ad821
|
29
|
|
RNAcommender: genome-wide recommendation of RNA-protein interactions, file e3835193-b100-72ef-e053-3705fe0ad821
|
28
|
|
GlanceNets: Interpretabile, Leak-proof Concept-based Models, file 15c024e9-60a3-441e-b885-cb9dbd302c6e
|
26
|
|
Generalized Reasoning with Graph Neural Networks by Relational Bayesian Network Encodings, file b9d79b16-4831-4a95-a338-023ae7d496cd
|
25
|
|
Decomposition Strategies for Constructive Preference Elicitation, file e3835194-51cf-72ef-e053-3705fe0ad821
|
25
|
|
Interpretability in Linear Brain Decoding, file e3835193-f84b-72ef-e053-3705fe0ad821
|
24
|
|
Efficient Generation of Structured Objects with Constrained Adversarial Networks, file e3835197-b516-72ef-e053-3705fe0ad821
|
22
|
|
Learning in the Wild with Incremental Skeptical Gaussian Processes, file e3835196-9205-72ef-e053-3705fe0ad821
|
21
|
|
Concept-level debugging of part-prototype networks, file fd52cf1f-e197-4725-9adc-180f1e66f373
|
21
|
|
Global Explainability of GNNs via Logic Combination of Learned Concepts, file 550621d5-c616-4079-986c-e27ad8b07c3a
|
20
|
|
Fixing Mislabeling by Human Annotators Leveraging Conflict Resolution and Prior Knowledge, file e3835195-8073-72ef-e053-3705fe0ad821
|
20
|
|
RNAcommender: genome-wide recommendation of RNA-protein interactions, file e3835198-7847-72ef-e053-3705fe0ad821
|
19
|
|
SMT-based Weighted Model Integration with Structure Awareness, file 6cf816dd-afe8-4324-abda-c373a3f5dd46
|
18
|
|
Meta-Path Learning for Multi-relational Graph Neural Networks, file c2dc1640-448b-4aa8-b91b-62f3cc6cd3ee
|
18
|
|
Structured learning modulo theories, file e3835198-01e9-72ef-e053-3705fe0ad821
|
18
|
|
Interactive label cleaning with example-based explanations, file e3835199-7742-72ef-e053-3705fe0ad821
|
18
|
|
A review and experimental analysis of active learning over crowdsourced data, file e3835199-f438-72ef-e053-3705fe0ad821
|
18
|
|
A Neuro-Symbolic Approach for Real-World Event Recognition from Weak Supervision, file a6ed0836-9e88-4a3b-b78a-77ba772c45b6
|
17
|
|
Is Parameter Learning via Weighted Model Integration Tractable?, file 6748d327-54d2-4ded-9d71-2bc520440f25
|
16
|
|
Towards Visual Semantics, file e3835199-8382-72ef-e053-3705fe0ad821
|
15
|
|
Co-creating Platformer Levels with
Constrained Adversarial Networks, file b087c890-c151-48c0-8c63-fc83f78d5ddc
|
14
|
|
Interactive label cleaning with example-based explanations, file e3835199-7743-72ef-e053-3705fe0ad821
|
14
|
|
Skeptical Learning-An Algorithm and a Platform for Dealing with Mislabeling in Personal Context Recognition, file fefa8ff5-1209-4eef-99e5-7c559ac8606e
|
14
|
|
Value-Based Hybrid Intelligence, file 0ef2ba94-6bc6-4adb-b577-0a899e9c990c
|
13
|
|
Probabilistic Inference in Hybrid Domains by Weighted Model Integration, file e3835198-01ed-72ef-e053-3705fe0ad821
|
13
|
|
Learning compositional programs with arguments and sampling, file ebd0405f-adfb-4e6e-9bf5-a59b1f50fb4b
|
13
|
|
Probabilistic Inference in Hybrid Domains by Weighted Model Integration, file e3835193-0365-72ef-e053-3705fe0ad821
|
11
|
|
Environmentally-Aware Bundle Recommendation Using the Choquet Integral, file 37de8fdf-10ce-4fb7-801f-619ad00c079e
|
10
|
|
Constructive Learning Modulo Theories, file e3835193-f845-72ef-e053-3705fe0ad821
|
10
|
|
Structured learning modulo theories, file e3835194-4fff-72ef-e053-3705fe0ad821
|
10
|
|
A review and experimental analysis of active learning over crowdsourced data, file e3835199-c8bb-72ef-e053-3705fe0ad821
|
10
|
|
Lifelong Personal Context Recognition, file e3835199-f74a-72ef-e053-3705fe0ad821
|
10
|
|
A Simple Latent Variable Model for Graph Learning and Inference, file 7b9b69cc-bbcb-4aca-83ba-9b38a8256aab
|
8
|
|
Toward a Unified Framework for Debugging Gray-box Models, file 7d7d365c-914b-4f2f-950a-5b96fa483f24
|
8
|
|
Value-Aware Active Learning, file 0c86af38-e109-45b6-90f8-de74ba75c76a
|
7
|
|
Generalising via Meta-Examples for Continual Learning in the Wild, file 5785f924-38ff-4222-9807-a28a8ab63c5a
|
7
|
|
Continual egocentric object recognition, file e3835197-295a-72ef-e053-3705fe0ad821
|
7
|
|
Interpretability Is in the Mind of the Beholder: A Causal Framework for Human-Interpretable Representation Learning, file 575e1106-b046-46d2-9029-2a6b5af8393b
|
6
|
|
Machine learning for microbiologists, file 59be30bb-ede8-4d05-8b70-6b361b2744ff
|
6
|
|
Automating Layout Synthesis with Constructive Preference Elicitation, file e3835195-20fc-72ef-e053-3705fe0ad821
|
6
|
|
The Science of Rejection: A Research Area for Human Computation, file e3835199-534b-72ef-e053-3705fe0ad821
|
6
|
|
Not All Neuro-Symbolic Concepts Are Created Equal: Analysis and Mitigation of Reasoning Shortcuts, file eb52415e-076a-433e-ba7a-a0ef2b0426a1
|
6
|
|
Few-shot unsupervised continual learning through meta-examples, file bc6e8536-e34a-40b9-ac4f-76faea7a27cd
|
5
|
|
Brain-Computer Evolutionary Multi-Objective Optimization (BC-EMO): a genetic algorithm adapting to the decision maker, file e3835191-d07a-72ef-e053-3705fe0ad821
|
5
|
|
Egocentric Hierarchical Visual Semantics, file ea7bc0e3-90dc-4e11-9a69-15f219d06e6f
|
5
|
|
Egocentric Hierarchical Visual Semantics, file fee2a5da-38b3-495c-9982-da59775d1ad2
|
5
|
|
A Neuro-Symbolic Approach for Non-Intrusive Load Monitoring, file ff264f57-d358-43e9-a29e-a95792c85474
|
5
|
|
Global Explainability of GNNs via Logic Combination of Learned Concepts, file 50bb35bf-b11b-46d4-9df2-f3c6c9e713bb
|
4
|
|
Brain-Computer Evolutionary Multi-Objective Optimization (BC-EMO): A Genetic Algorithm Adapting to the Decision Maker, file 92ad5d2d-9197-48dc-9352-b4c0924129f5
|
4
|
|
Dealing with Mislabeling via Interactive Machine Learning, file e3835196-b952-72ef-e053-3705fe0ad821
|
4
|
|
Active learning of Pareto fronts, file fb6dcd54-e489-4fc7-8b49-943a3a93fc76
|
4
|
|
Sensory and multisensory reasoning: Is Bayesian updating modality-dependent?, file 142dc9d0-71cb-4d4a-a11b-710947c70e6b
|
3
|
|
Automatic Prediction of Functional Residues from Sequence and Structural Information, file 824b2264-eb3d-410b-898b-3db61870cc66
|
3
|
|
Joint Learning and Optimization of Unknown Combinatorial Utility Functions, file 8f9d6ed9-4a87-4f6a-bfb2-aa32e4fb4083
|
3
|
|
Towards a Unified Framework for Probabilistic Verification of AI Systems, file c26abeb5-f39d-403a-abb5-c5434919225d
|
3
|
|
Adaptation of Student Behavioural Routines during COVID-19: A Multimodal
Approach, file cbb40e5c-0560-450a-a293-4dcc865e8020
|
3
|
|
Active Learning of Pareto Fronts, file e3835192-9811-72ef-e053-3705fe0ad821
|
3
|
|
Learning Weighted Model Integration Distributions, file e3835196-b950-72ef-e053-3705fe0ad821
|
3
|
|
Graph Neural Networks for Temporal Graphs: State of the Art, Open Challenges, and Opportunities, file b2fa6c16-ecf3-42bc-958f-6f445ff0793f
|
2
|
|
Learning Modulo Theories, file e3835193-f83e-72ef-e053-3705fe0ad821
|
2
|
|
Guest editors’ introduction to the EcmlPkdd 2016 journal track special issue of Machine Learning, file e3835193-fd54-72ef-e053-3705fe0ad821
|
2
|
|
Coactive critiquing: Elicitation of preferences and features, file e3835194-02ad-72ef-e053-3705fe0ad821
|
2
|
|
Guest editors’ introduction to the EcmlPkdd 2016 journal track special issue of Machine Learning, file e3835194-0402-72ef-e053-3705fe0ad821
|
2
|
|
Counts-of-counts similarity for prediction and search in relational data, file e3835196-7c8e-72ef-e053-3705fe0ad821
|
2
|
|
Learning in the Wild with Incremental Skeptical Gaussian Processes, file 60867f82-eca4-4337-a1ef-cfaade2e4916
|
1
|
|
Neuro-Symbolic Continual Learning: Knowledge, Reasoning Shortcuts and Concept Rehearsal, file a71d3abd-3fa4-4ca5-9ba3-2552bf85cfe2
|
1
|
|
Learning Modulo Theories for constructive preference elicitation, file d6d7675c-8278-4e70-8faa-292d69befd19
|
1
|
|
New results on error correcting output codes of kernel machines, file e3835191-ca0e-72ef-e053-3705fe0ad821
|
1
|
| Totale |
4.401 |