Contrastive learning of coarse-grained force fields
Coarse-grained models have proven helpful for simulating complex systems over long time
scales to provide molecular insights into various processes. Methodologies for systematic …
scales to provide molecular insights into various processes. Methodologies for systematic …
Provable benefits of annealing for estimating normalizing constants: Importance Sampling, Noise-Contrastive Estimation, and beyond
O Chehab, A Hyvarinen… - Advances in Neural …, 2024 - proceedings.neurips.cc
Recent research has developed several Monte Carlo methods for estimating the
normalization constant (partition function) based on the idea of annealing. This means …
normalization constant (partition function) based on the idea of annealing. This means …
Revisiting energy based models as policies: Ranking noise contrastive estimation and interpolating energy models
A crucial design decision for any robot learning pipeline is the choice of policy
representation: what type of model should be used to generate the next set of robot actions …
representation: what type of model should be used to generate the next set of robot actions …
On the connection between Noise-Contrastive Estimation and Contrastive Divergence
A Olmin, J Lindqvist, L Svensson… - International …, 2024 - proceedings.mlr.press
Noise-contrastive estimation (NCE) is a popular method for estimating unnormalised
probabilistic models, such as energy-based models, which are effective for modelling …
probabilistic models, such as energy-based models, which are effective for modelling …
Pitfalls of gaussians as a noise distribution in NCE
Noise Contrastive Estimation (NCE) is a popular approach for learning probability density
functions parameterized up to a constant of proportionality. The main idea is to design a …
functions parameterized up to a constant of proportionality. The main idea is to design a …
Highly Accurate Real-space Electron Densities with Neural Networks
Variational ab-initio methods in quantum chemistry stand out among other methods in
providing direct access to the wave function. This allows in principle straightforward …
providing direct access to the wave function. This allows in principle straightforward …
Adversarially Contrastive Estimation of Conditional Neural Processes
Conditional Neural Processes~(CNPs) formulate distributions over functions and generate
function observations with exact conditional likelihoods. CNPs, however, have limited …
function observations with exact conditional likelihoods. CNPs, however, have limited …
Optimizing the Noise in Self-Supervised Learning: from Importance Sampling to Noise-Contrastive Estimation
Self-supervised learning is an increasingly popular approach to unsupervised learning,
achieving state-of-the-art results. A prevalent approach consists in contrasting data points …
achieving state-of-the-art results. A prevalent approach consists in contrasting data points …
A Unified View on Learning Unnormalized Distributions via Noise-Contrastive Estimation
This paper studies a family of estimators based on noise-contrastive estimation (NCE) for
learning unnormalized distributions. The main contribution of this work is to provide a unified …
learning unnormalized distributions. The main contribution of this work is to provide a unified …
Learning identifiable representations: independent influences and multiple views
L Gresele - 2023 - tobias-lib.ub.uni-tuebingen.de
Intelligent systems, whether biological or artificial, perceive unstructured information from the
world around them: deep neural networks designed for object recognition receive …
world around them: deep neural networks designed for object recognition receive …