625 citations · 2.7k across the 69 of their papers we have counts for
7 papers · 1 filter
Auto-Differentiating Linear Algebra
Matthias Seeger, Asmus Hetzel, Zhenwen Dai +2
Development systems for deep learning (DL), such as Theano, Torch, TensorFlow, or MXNet, are easy-to-use tools for creating complex neural network models. Since gradient computatio…
Gaussian Process Latent Force Models for Learning and Stochastic Control of Physical Systems
Simo Särkkä, Mauricio A. Álvarez, Neil D. Lawrence
This article is concerned with learning and stochastic control in physical systems which contain unknown input signals. These unknown signals are modeled as Gaussian processes (GP)…
Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes
Zhenwen Dai, Mauricio A. Álvarez, Neil D. Lawrence
Often in machine learning, data are collected as a combination of multiple conditions, e.g., the voice recordings of multiple persons, each labeled with an ID. How could we build a…
Living Together: Mind and Machine Intelligence
Neil D. Lawrence
In this paper we consider the nature of the machine intelligences we have created in the context of our human intelligence. We suggest that the fundamental difference between human…
Data Readiness Levels
Neil D. Lawrence
Application of models to data is fraught. Data-generating collaborators often only have a very basic understanding of the complications of collating, processing and curating data.…
Preferential Bayesian Optimization
Javier Gonzalez, Zhenwen Dai, Andreas Damianou +1
Bayesian optimization (BO) has emerged during the last few years as an effective approach to optimizing black-box functions where direct queries of the objective are expensive. In…