2 citations · 3 across the 11 of their papers we have counts for
7 papers · 1 filter
SCENT: Aligning Mass Spectra with Molecular Structure for Olfactory Perception
Ziqi Zhang, Eunyeong Jin, Miguel Vasco +6
Predicting human olfactory perception from molecular structure has seen remarkable progress, yet these approaches require explicit chemical structure at inference, which is not ava…
Geometry of Uncertainty: Learning Metric Spaces for Multimodal State Estimation in RL
Alfredo Reichlin, Adriano Pacciarelli, Danica Kragic +1
Estimating the state of an environment from high-dimensional, multimodal, and noisy observations is a fundamental challenge in reinforcement learning (RL). Traditional approaches r…
Walking on the Fiber: A Simple Geometric Approximation for Bayesian Neural Networks
Alfredo Reichlin, Miguel Vasco, Danica Kragic
Bayesian Neural Networks provide a principled framework for uncertainty quantification by modeling the posterior distribution of network parameters. However, exact posterior infere…
Humans Coexist, So Must Embodied Artificial Agents
Hannah Kuehn, Joseph La Delfa, Miguel Vasco +2
This paper introduces the concept of coexistence for embodied artificial agents and argues that it is a prerequisite for long-term, in-the-wild interaction with humans. Contemporar…
Can Transformers Smell Like Humans?
Farzaneh Taleb, Miguel Vasco, Antônio H. Ribeiro +2
The human brain encodes stimuli from the environment into representations that form a sensory perception of the world. Despite recent advances in understanding visual and auditory…
Reducing Variance in Meta-Learning via Laplace Approximation for Regression Tasks
Alfredo Reichlin, Gustaf Tegnér, Miguel Vasco +3
Given a finite set of sample points, meta-learning algorithms aim to learn an optimal adaptation strategy for new, unseen tasks. Often, this data can be ambiguous as it might belon…