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Found 12 papers, 8 papers with code
PiShield: A NeSy Framework for Learning with Requirements
Given the widespread application of deep learning, there is a growing need for frameworks allowing for the integration of the requirements across various domains.
Exploiting T-norms for Deep Learning in Autonomous Driving
Deep learning has been at the core of the autonomous driving field development, due to the neural networks' success in finding patterns in raw data and turning them into accurate predictions.
How Realistic Is Your Synthetic Data? Constraining Deep Generative Models for Tabular Data
Further, we show how our CL does not necessarily need to be integrated at training time, as it can be also used as a guardrail at inference time, still producing some improvements in the overall performance of the models.
Machine Learning with Requirements: a Manifesto
In the recent years, machine learning has made great advancements that have been at the root of many breakthroughs in different application domains.
ROAD-R: The Autonomous Driving Dataset with Logical Requirements
Neural networks have proven to be very powerful at computer vision tasks.
Deep Learning with Logical Constraints
In recent years, there has been an increasing interest in exploiting logically specified background knowledge in order to obtain neural models (i) with a better performance, (ii) able to learn from less data, and/or (iii) guaranteed to be compliant with the background knowledge itself, e. g., for safety-critical applications.
Imposing Hard Logical Constraints on Multi-label Classification Neural Networks
In this paper, we thus propose to enhance deep learning models by incorporating background knowledge as hard logical constraints.
Multi-Label Classification Neural Networks with Hard Logical Constraints
Multi-label classification (MC) is a standard machine learning problem in which a data point can be associated with a set of classes.
Coherent Hierarchical Multi-Label Classification Networks
Hierarchical multi-label classification (HMC) is a challenging classification task extending standard multi-label classification problems by imposing a hierarchy constraint on the classes.
The Struggles of Feature-Based Explanations: Shapley Values vs. Minimal Sufficient Subsets
For neural models to garner widespread public trust and ensure fairness, we must have human-intelligible explanations for their predictions.
Knowledge Graph Extraction from Videos
Nearly all existing techniques for automated video annotation (or captioning) describe videos using natural language sentences.
Can I Trust the Explainer? Verifying Post-hoc Explanatory Methods
We aim for this framework to provide a publicly available, off-the-shelf evaluation when the feature-selection perspective on explanations is needed.
