774 citations · 2.7k across the 22 of their papers we have counts for
23 papers
Atomistic graph networks for experimental materials property prediction
Tian Xie, Victor Bapst, Alexander L. Gaunt +5
Machine Learning (ML) has the potential to accelerate discovery of new materials and shed light on useful properties of existing materials. A key difficulty when applying ML in Mat…
Learning to Represent Edits
Pengcheng Yin, Graham Neubig, Miltiadis Allamanis +2
We introduce the problem of learning distributed representations of edits. By combining a "neural editor" with an "edit encoder", our models learn to represent the salient informat…
Deterministic Variational Inference for Robust Bayesian Neural Networks
Anqi Wu, Sebastian Nowozin, Edward Meeds +3
Bayesian neural networks (BNNs) hold great promise as a flexible and principled solution to deal with uncertainty when learning from finite data. Among approaches to realize probab…
Synthetic dissipation and cascade fluxes in a turbulent quantum gas
Nir Navon, Christoph Eigen, Jinyi Zhang +6
Scale-invariant fluxes are the defining property of turbulent cascades, but their direct measurement is a notorious problem. Here we perform such a measurement for a direct energy…
Constrained Graph Variational Autoencoders for Molecule Design
Qi Liu, Miltiadis Allamanis, Marc Brockschmidt +1
Graphs are ubiquitous data structures for representing interactions between entities. With an emphasis on the use of graphs to represent chemical molecules, we explore the task of…
Generative Code Modeling with Graphs
Marc Brockschmidt, Miltiadis Allamanis, Alexander L. Gaunt +1
Generative models for source code are an interesting structured prediction problem, requiring to reason about both hard syntactic and semantic constraints as well as about natural,…
Graph Partition Neural Networks for Semi-Supervised Classification
Renjie Liao, Marc Brockschmidt, Daniel Tarlow +3
We present graph partition neural networks (GPNN), an extension of graph neural networks (GNNs) able to handle extremely large graphs. GPNNs alternate between locally propagating i…
AMPNet: Asynchronous Model-Parallel Training for Dynamic Neural Networks
Alexander L. Gaunt, Matthew A. Johnson, Maik Riechert +4
New types of machine learning hardware in development and entering the market hold the promise of revolutionizing deep learning in a manner as profound as GPUs. However, existing s…
Summary - TerpreT: A Probabilistic Programming Language for Program Induction
Alexander L. Gaunt, Marc Brockschmidt, Rishabh Singh +4
We study machine learning formulations of inductive program synthesis; that is, given input-output examples, synthesize source code that maps inputs to corresponding outputs. Our k…
Differentiable Programs with Neural Libraries
Alexander L. Gaunt, Marc Brockschmidt, Nate Kushman +1
We develop a framework for combining differentiable programming languages with neural networks. Using this framework we create end-to-end trainable systems that learn to write inte…
DeepCoder: Learning to Write Programs
Matej Balog, Alexander L. Gaunt, Marc Brockschmidt +2
We develop a first line of attack for solving programming competition-style problems from input-output examples using deep learning. The approach is to train a neural network to pr…
Differentiable Functional Program Interpreters
John K. Feser, Marc Brockschmidt, Alexander L. Gaunt +1
Programming by Example (PBE) is the task of inducing computer programs from input-output examples. It can be seen as a type of machine learning where the hypothesis space is the se…
Observation of Weak Collapse in a Bose-Einstein Condensate
Christoph Eigen, Alexander L. Gaunt, Aziza Suleymanzade +3
We study the collapse of an attractive atomic Bose-Einstein condensate prepared in the uniform potential of an optical-box trap. We characterise the critical point for collapse and…
Emergence of a Turbulent Cascade in a Quantum Gas
Nir Navon, Alexander L. Gaunt, Robert P. Smith +1
In the modern understanding of turbulence, a central concept is the existence of cascades of excitations from large to small lengthscales, or vice-versa. This concept was introduce…
TerpreT: A Probabilistic Programming Language for Program Induction
Alexander L. Gaunt, Marc Brockschmidt, Rishabh Singh +4
We study machine learning formulations of inductive program synthesis; given input-output examples, we try to synthesize source code that maps inputs to corresponding outputs. Our…
Critical Dynamics of Spontaneous Symmetry Breaking in a Homogeneous Bose gas
Nir Navon, Alexander L. Gaunt, Robert P. Smith +1
We explore the dynamics of spontaneous symmetry breaking in a homogeneous system by thermally quenching an atomic gas with short-range interactions through the Bose-Einstein phase…
Observing Properties of an Interacting Homogeneous Bose--Einstein Condensate: Heisenberg-Limited Momentum Spread, Interaction Energy and Free-Expansion Dynamics
Igor Gotlibovych, Tobias F. Schmidutz, Alexander L. Gaunt +3
We study the properties of an atomic Bose--Einstein condensate produced in an optical-box potential, using high-resolution Bragg spectroscopy. For a range of box sizes, up to $70~μ…
Quantum Joule-Thomson Effect in a Saturated Homogeneous Bose Gas
Tobias F. Schmidutz, Igor Gotlibovych, Alexander L. Gaunt +3
We study the thermodynamics of Bose-Einstein condensation in a weakly interacting quasi-homogeneous atomic gas, prepared in an optical-box trap. We characterise the critical point…
Stability of a unitary Bose gas
Richard J. Fletcher, Alexander L. Gaunt, Nir Navon +2
We study the stability of a thermal K Bose gas across a broad Feshbach resonance, focusing on the unitary regime, where the scattering length exceeds the thermal wavelen…
A superheated Bose-condensed gas
Alexander L. Gaunt, Richard J. Fletcher, Robert P. Smith +1
Our understanding of various states of matter usually relies on the assumption of thermodynamic equilibrium. However, the transitions between different phases of matter can be stro…
A compact single-chamber apparatus for Bose-Einstein condensation of Rb
Igor Gotlibovych, Tobias F. Schmidutz, Stuart Moulder +7
We describe a simple and compact single-chamber apparatus for robust production of Rb Bose-Einstein condensates. The apparatus is built from off-the-shelf components and allow…
Bose-Einstein condensation of atoms in a uniform potential
Alexander L. Gaunt, Tobias F. Schmidutz, Igor Gotlibovych +2
We have observed Bose-Einstein condensation of an atomic gas in the (quasi-)uniform three-dimensional potential of an optical box trap. Condensation is seen in the bimodal momentum…
Robust Digital Holography For Ultracold Atom Trapping
Alexander L Gaunt, Zoran Hadzibabic
We have formulated and experimentally demonstrated an improved algorithm for design of arbitrary two-dimensional holographic traps for ultracold atoms. Our method builds on the bes…