Hernández-Lobato, José Miguel

Minerva: enabling low-power, highly-accurate deep neural network accelerators

Reagen, B., Whatmough, P., Adolf, R., Rama, S., Lee, H., Lee, S. K., Hernández-Lobato, J. M., Wei, G.-Y., & Brooks, D. (2016) · ACM SIGARCH Computer Architecture News

Low-power high-performance VLSI designAdvanced Neural Network Applications

Cited by 446

Predictive Entropy Search for Efficient Global Optimization of Black-box Functions

Hernández-Lobato, J. M., Hoffman, M. W., & Ghahramani, Z. (2014) · arXiv

Advanced Bandit Algorithms ResearchAdvanced Multi-Objective Optimization Algorithms

Cited by 407

Inference in Deep Gaussian Processes using Stochastic Gradient Hamiltonian Monte Carlo

Havasi, M., Hernández-Lobato, J. M., & Murillo-Fuentes, J. J. (2018) · arXiv

Gaussian Processes and Bayesian InferenceMarkov Chains and Monte Carlo Methods

Cited by 69

Getting a CLUE: A Method for Explaining Uncertainty Estimates

Antorán, J., Bhatt, U., Adel, T., Weller, A., & Hernández-Lobato, J. M. (2021) · Apollo - University of Cambridge Repository

Explainable Artificial Intelligence (XAI)Adversarial Robustness in Machine Learning

Cited by 49

Bayesian Variational Autoencoders for Unsupervised Out-of-Distribution Detection

Daxberger, E., & Hernández-Lobato, J. M. (2019) · arXiv

Adversarial Robustness in Machine LearningAnomaly Detection Techniques and Applications

Cited by 45

Successor Uncertainties: Exploration and Uncertainty in Temporal Difference Learning

Janz, D., Hron, J., Mazur, P., Hofmann, K., Hernández-Lobato, J. M., & Tschiatschek, S. (2018) · arXiv

Reinforcement Learning in RoboticsAdvanced Bandit Algorithms Research

Cited by 23