6 papers · 1 filter
: Transformer-based inference from interaction maps
Eloïse Touron, Pedro L. C. Rodrigues, Julyan Arbel +2
Inference from interaction maps, such as centromere identification from genome-wide chromosome conformation capture techniques -- notably Hi-C -- can be formulated as a generic inv…
Bounding Box Anomaly Scoring for simple and efficient Out-of-Distribution detection
Mohamed Bahi Yahiaoui, Geoffrey Daniel, Loïc Giraldi +2
Out-of-distribution (OOD) detection aims to identify inputs that differ from the training distribution in order to reduce unreliable predictions by deep neural networks. Among post…
Gaussian Pre-Activations in Neural Networks: Myth or Reality?
Pierre Wolinski, Julyan Arbel
The study of feature propagation at initialization in neural networks lies at the root of numerous initialization designs. An assumption very commonly made in the field states that…
Logarithmic Regret for Unconstrained Submodular Maximization Stochastic Bandit
Julien Zhou, Pierre Gaillard, Thibaud Rahier +1
We address the online unconstrained submodular maximization problem (Online USM), in a setting with stochastic bandit feedback. In this framework, a decision-maker receives noisy r…
Towards Efficient and Optimal Covariance-Adaptive Algorithms for Combinatorial Semi-Bandits
Julien Zhou, Pierre Gaillard, Thibaud Rahier +2
We address the problem of stochastic combinatorial semi-bandits, where a player selects among P actions from the power set of a set containing d base items. Adaptivity to the probl…
Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI
Theodore Papamarkou, Maria Skoularidou, Konstantina Palla +22
In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language dat…