
Learning quantum dynamics with latent neural ODEs
The core objective of machineassisted scientific discovery is to learn ...
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Seeing Glass: Joint Point Cloud and Depth Completion for Transparent Objects
The basis of many object manipulation algorithms is RGBD input. Yet, co...
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Design of quantum optical experiments with logic artificial intelligence
Logic artificial intelligence (AI) is a subfield of AI where variables c...
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Predicting 3D shapes, masks, and properties of materials, liquids, and objects inside transparent containers, using the TransProteus CGI dataset
We present TransProteus, a dataset, and methods for predicting the 3D st...
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Learning Interpretable Representations of Entanglement in Quantum Optics Experiments using Deep Generative Models
Quantum physics experiments produce interesting phenomena such as interf...
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JANUS: Parallel Tempered Genetic Algorithm Guided by Deep Neural Networks for Inverse Molecular Design
Inverse molecular design, i.e., designing molecules with specific target...
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Computer vision for liquid samples in hospitals and medical labs using hierarchical image segmentation and relations prediction
This work explores the use of computer vision for image segmentation and...
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Golem: An algorithm for robust experiment and process optimization
Numerous challenges in science and engineering can be framed as optimiza...
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Gemini: Dynamic Bias Correction for Autonomous Experimentation and Molecular Simulation
Bayesian optimization has emerged as a powerful strategy to accelerate s...
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Assigning Confidence to Molecular Property Prediction
Introduction: Computational modeling has rapidly advanced over the last ...
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Noisy intermediatescale quantum (NISQ) algorithms
A universal faulttolerant quantum computer that can solve efficiently p...
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Curiosity in exploring chemical space: Intrinsic rewards for deep molecular reinforcement learning
Computeraided design of molecules has the potential to disrupt the fiel...
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Deep Molecular Dreaming: Inverse machine learning for denovo molecular design and interpretability with surjective representations
Computerbased denovo design of functional molecules is one of the most...
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Natural Evolutionary Strategies for Variational Quantum Computation
Natural evolutionary strategies (NES) are a family of gradientfree blac...
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Bayesian Variational Optimization for Combinatorial Spaces
This paper focuses on Bayesian Optimization in combinatorial spaces. In ...
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Scientific intuition inspired by machine learning generated hypotheses
Machine learning with application to questions in the physical sciences ...
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Olympus: a benchmarking framework for noisy optimization and experiment planning
Research challenges encountered across science, engineering, and economi...
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Experimental demonstration of a quantum generative adversarial network for continuous distributions
The potential advantage of machine learning in quantum computers is a to...
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Gryffin: An algorithm for Bayesian optimization for categorical variables informed by physical intuition with applications to chemistry
Designing functional molecules and advanced materials requires complex i...
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Neural Message Passing on High Order Paths
Graph neural network have achieved impressive results in predicting mole...
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Graph Deconvolutional Generation
Graph generation is an extremely important task, as graphs are found thr...
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Machine Learning for Scent: Learning Generalizable Perceptual Representations of Small Molecules
Predicting the relationship between a molecule's structure and its odor ...
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Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Space
Challenges in natural sciences can often be phrased as optimization prob...
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An Artificial Spiking Quantum Neuron
Artificial spiking neural networks have found applications in areas wher...
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SELFIES: a robust representation of semantically constrained graphs with an example application in chemistry
Graphs are ideal representations of complex, relational information. The...
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Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models
Deep generative models such as generative adversarial networks, variatio...
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PHOENICS: A universal deep Bayesian optimizer
In this work we introduce PHOENICS, a probabilistic global optimization ...
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Quantum Neuron: an elementary building block for machine learning on quantum computers
Even the most sophisticated artificial neural networks are built by aggr...
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qTorch: The Quantum Tensor Contraction Handler
Classical simulation of quantum computation is necessary for studying th...
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Machine Learning for Quantum Dynamics: Deep Learning of Excitation Energy Transfer Properties
Understanding the relationship between the structure of lightharvesting...
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Parallel and Distributed Thompson Sampling for Largescale Accelerated Exploration of Chemical Space
Chemical space is so large that brute force searches for new interesting...
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ObjectiveReinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models
In unsupervised data generation tasks, besides the generation of a sampl...
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Neural networks for the prediction organic chemistry reactions
Reaction prediction remains one of the major challenges for organic chem...
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SpaceFilling Curves as a Novel Crystal Structure Representation for Machine Learning Models
A fundamental problem in applying machine learning techniques for chemic...
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Convolutional Networks on Graphs for Learning Molecular Fingerprints
We introduce a convolutional neural network that operates directly on gr...
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Alán AspuruGuzik
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