13 citations · 27 across the 3 of their papers we have counts for
6 papers
An empirical study of pretrained representations for few-shot classification
Tiago Ramalho, Thierry Sousbie, Stefano Peluchetti
Recent algorithms with state-of-the-art few-shot classification results start their procedure by computing data features output by a large pretrained model. In this paper we system…
Density estimation in representation space to predict model uncertainty
Tiago Ramalho, Miguel Miranda
Deep learning models frequently make incorrect predictions with high confidence when presented with test examples that are not well represented in their training dataset. We propos…
Adaptive Posterior Learning: few-shot learning with a surprise-based memory module
Tiago Ramalho, Marta Garnelo
The ability to generalize quickly from few observations is crucial for intelligent systems. In this paper we introduce APL, an algorithm that approximates probability distributions…
Encoding Spatial Relations from Natural Language
Tiago Ramalho, Tomáš Kočiský, Frederic Besse +5
Natural language processing has made significant inroads into learning the semantics of words through distributional approaches, however representations learnt via these methods fa…
Conditional Neural Processes
Marta Garnelo, Dan Rosenbaum, Chris J. Maddison +6
Deep neural networks excel at function approximation, yet they are typically trained from scratch for each new function. On the other hand, Bayesian methods, such as Gaussian Proce…
Simulation of stochastic network dynamics via entropic matching
Tiago Ramalho, Marco Selig, Ulrich Gerland +1
The simulation of complex stochastic network dynamics arising, for instance, from models of coupled biomolecular processes remains computationally challenging. Often, the necessity…