2 citations · 2 across the 9 of their papers we have counts for
6 papers · 1 filter
Can AI Understand the Language of Origami?
Naaisha Agarwal, Yihan Wu, Xin Guan +10
Building AI systems that can plan, act, and create in the physical world requires more than pattern recognition. Such systems must reason about the generative mechanisms and constr…
Collapse-Proof Non-Contrastive Self-Supervised Learning
Emanuele Sansone, Tim Lebailly, Tinne Tuytelaars
We present a principled and simplified design of the projector and loss function for non-contrastive self-supervised learning based on hyperdimensional computing. We theoretically…
EXPLAIN, AGREE, LEARN: Scaling Learning for Neural Probabilistic Logic
Victor Verreet, Lennert De Smet, Luc De Raedt +1
Neural probabilistic logic systems follow the neuro-symbolic (NeSy) paradigm by combining the perceptive and learning capabilities of neural networks with the robustness of probabi…
(Deep) Generative Geodesics
Beomsu Kim, Michael Puthawala, Jong Chul Ye +1
In this work, we propose to study the global geometrical properties of generative models. We introduce a new Riemannian metric to assess the similarity between any two data points.…
Differentiable Sampling of Categorical Distributions Using the CatLog-Derivative Trick
Lennert De Smet, Emanuele Sansone, Pedro Zuidberg Dos Martires
Categorical random variables can faithfully represent the discrete and uncertain aspects of data as part of a discrete latent variable model. Learning in such models necessitates t…
The Triad of Failure Modes and a Possible Way Out
Emanuele Sansone
We present a novel objective function for cluster-based self-supervised learning (SSL) that is designed to circumvent the triad of failure modes, namely representation collapse, cl…