13 citations · 22 across the 6 of their papers we have counts for
8 papers
The RAIL Principles for Neurosymbolic AI: Reasoning, Assurances, Interfacing and Learning
Agnese Chiatti, Michael Cochez, Cristina Cornelio +14
Neurosymbolic AI systems that integrate machine learning and symbolic reasoning are rapidly gaining attention. They complement the data-intensive statistical approaches of neural n…
IID Relaxation by Logical Expressivity: A Research Agenda for Fitting Logics to Neurosymbolic Requirements
Maarten C. Stol, Alessandra Mileo
Neurosymbolic background knowledge and the expressivity required of its logic can break Machine Learning assumptions about data Independence and Identical Distribution. In this pos…
Prompt Generation Networks for Input-Space Adaptation of Frozen Vision Transformers
Jochem Loedeman, Maarten C. Stol, Tengda Han +1
With the introduction of the transformer architecture in computer vision, increasing model scale has been demonstrated as a clear path to achieving performance and robustness gains…
Self-supervised Video Representation Learning with Cross-Stream Prototypical Contrasting
Martine Toering, Ioannis Gatopoulos, Maarten Stol +1
Instance-level contrastive learning techniques, which rely on data augmentation and a contrastive loss function, have found great success in the domain of visual representation lea…
Mixing Consistent Deep Clustering
Daniel Lutscher, Ali el Hassouni, Maarten Stol +1
Finding well-defined clusters in data represents a fundamental challenge for many data-driven applications, and largely depends on good data representation. Drawing on literature r…
FlipOut: Uncovering Redundant Weights via Sign Flipping
Andrei Apostol, Maarten Stol, Patrick Forré
Modern neural networks, although achieving state-of-the-art results on many tasks, tend to have a large number of parameters, which increases training time and resource usage. This…
Super-resolution Variational Auto-Encoders
Ioannis Gatopoulos, Maarten Stol, Jakub M. Tomczak
The framework of variational autoencoders (VAEs) provides a principled method for jointly learning latent-variable models and corresponding inference models. However, the main draw…
Pruning via Iterative Ranking of Sensitivity Statistics
Stijn Verdenius, Maarten Stol, Patrick Forré
With the introduction of SNIP [arXiv:1810.02340v2], it has been demonstrated that modern neural networks can effectively be pruned before training. Yet, its sensitivity criterion h…