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Found 6 papers, 2 papers with code
Memory-efficient Continual Learning with Neural Collapse Contrastive
Building on these insights, we propose Focal Neural Collapse Contrastive (FNC^2), a novel representation learning loss that effectively balances both soft and hard relationships.
Towards Explainable Clustering: A Constrained Declarative based Approach
In our framework an explanation of a cluster is a set of patterns, and we propose a novel interpretable constrained clustering method called ECS for declarative clustering with Explainabilty-driven Cluster Selection that integrates structural or domain expert knowledge expressed by means of constraints.
Incremental Constrained Clustering by Minimal Weighted Modification
Clustering is a well-known task in Data Mining that aims at grouping data instances according to their similarity.
Ranked #1 on
Incremental Constrained Clustering
on Wine
Random Dilated Shapelet Transform: A New Approach for Time Series Shapelets
Shapelet-based algorithms are widely used for time series classification because of their ease of interpretation, but they are currently outperformed by recent state-of-the-art approaches.
Ranked #1 on
Time Series Classification
on ArrowHead
Predictive maintenance on event logs: Application on an ATM fleet
Predictive maintenance is used in industrial applications to increase machine availability and optimize cost related to unplanned maintenance.
From Shallow to Deep Interactions Between Knowledge Representation, Reasoning and Machine Learning (Kay R. Amel group)
Some common concerns are identified and discussed such as the types of used representation, the roles of knowledge and data, the lack or the excess of information, or the need for explanations and causal understanding.
