Search Results for author:
Found 19 papers, 5 papers with code
Physics-Informed Bayesian Optimization of Variational Quantum Circuits
In this paper, we propose a novel and powerful method to harness Bayesian optimization for Variational Quantum Eigensolvers (VQEs) -- a hybrid quantum-classical protocol used to approximate the ground state of a quantum Hamiltonian.
Equivariant Neural Tangent Kernels
Equivariant neural networks have in recent years become an important technique for guiding architecture selection for neural networks with many applications in domains ranging from medical image analysis to quantum chemistry.
Fast and Unified Path Gradient Estimators for Normalizing Flows
Recent work shows that path gradient estimators for normalizing flows have lower variance compared to standard estimators for variational inference, resulting in improved training.
Emergent Equivariance in Deep Ensembles
We show that deep ensembles become equivariant for all inputs and at all training times by simply using data augmentation.
Generalization within in silico screening
In silico screening uses predictive models to select a batch of compounds with favorable properties from a library for experimental validation.
Detecting and Mitigating Mode-Collapse for Flow-based Sampling of Lattice Field Theories
In this work, we first point out that the tunneling problem is also present for normalizing flows but is shifted from the sampling to the training phase of the algorithm.
Gradients should stay on Path: Better Estimators of the Reverse- and Forward KL Divergence for Normalizing Flows
We propose an algorithm to estimate the path-gradient of both the reverse and forward Kullback-Leibler divergence for an arbitrary manifestly invertible normalizing flow.
Path-Gradient Estimators for Continuous Normalizing Flows
Recent work has established a path-gradient estimator for simple variational Gaussian distributions and has argued that the path-gradient is particularly beneficial in the regime in which the variational distribution approaches the exact target distribution.
Diffeomorphic Counterfactuals with Generative Models
Counterfactuals can explain classification decisions of neural networks in a human interpretable way.
Machine Learning of Thermodynamic Observables in the Presence of Mode Collapse
Crucially, these models allow for the direct estimation of the free energy at a given point in parameter space.
Deep learning for surrogate modelling of 2D mantle convection
Using a dataset of 10, 525 two-dimensional simulations of the thermal evolution of the mantle of a Mars-like planet, we show that deep learning techniques can produce reliable parameterized surrogates (i. e. surrogates that predict state variables such as temperature based only on parameters) of the underlying partial differential equations.
Diffeomorphic Explanations with Normalizing Flows
Normalizing flows are diffeomorphisms which are parameterized by neural networks.
Towards Robust Explanations for Deep Neural Networks
Explanation methods shed light on the decision process of black-box classifiers such as deep neural networks.
Fairwashing Explanations with Off-Manifold Detergent
Explanation methods promise to make black-box classifiers more transparent.
Estimation of Thermodynamic Observables in Lattice Field Theories with Deep Generative Models
In this work, we demonstrate that applying deep generative machine learning models for lattice field theory is a promising route for solving problems where Markov Chain Monte Carlo (MCMC) methods are problematic.
Asymptotically unbiased estimation of physical observables with neural samplers
We propose a general framework for the estimation of observables with generative neural samplers focusing on modern deep generative neural networks that provide an exact sampling probability.
Explanations can be manipulated and geometry is to blame
Explanation methods aim to make neural networks more trustworthy and interpretable.
Comment on "Solving Statistical Mechanics Using VANs": Introducing saVANt - VANs Enhanced by Importance and MCMC Sampling
In this comment on "Solving Statistical Mechanics Using Variational Autoregressive Networks" by Wu et al., we propose a subtle yet powerful modification of their approach.
Analysis of Atomistic Representations Using Weighted Skip-Connections
In this work, we extend the SchNet architecture by using weighted skip connections to assemble the final representation.
