Search Results for author: Alan Aspuru-Guzik

Found 25 papers, 12 papers with code

Application-Driven Innovation in Machine Learning

no code implementations26 Mar 2024 David Rolnick, Alan Aspuru-Guzik, Sara Beery, Bistra Dilkina, Priya L. Donti, Marzyeh Ghassemi, Hannah Kerner, Claire Monteleoni, Esther Rolf, Milind Tambe, Adam White

As applications of machine learning proliferate, innovative algorithms inspired by specific real-world challenges have become increasingly important.

Learning Zero-Shot Material States Segmentation, by Implanting Natural Image Patterns in Synthetic Data

no code implementations5 Mar 2024 Sagi Eppel, Jolina Li, Manuel Drehwald, Alan Aspuru-Guzik

Visual recognition of materials and their states is essential for understanding the physical world, from identifying wet regions on surfaces or stains on fabrics to detecting infected areas on plants or minerals in rocks.

Material Recognition Segmentation +2

MVTrans: Multi-View Perception of Transparent Objects

no code implementations22 Feb 2023 Yi Ru Wang, Yuchi Zhao, Haoping Xu, Saggi Eppel, Alan Aspuru-Guzik, Florian Shkurti, Animesh Garg

Transparent object perception is a crucial skill for applications such as robot manipulation in household and laboratory settings.

Depth Estimation Object +5

Machine Learning for a Sustainable Energy Future

no code implementations19 Oct 2022 Zhenpeng Yao, Yanwei Lum, Andrew Johnston, Luis Martin Mejia-Mendoza, Xin Zhou, Yonggang Wen, Alan Aspuru-Guzik, Edward H. Sargent, Zhi Wei Seh

Transitioning from fossil fuels to renewable energy sources is a critical global challenge; it demands advances at the levels of materials, devices, and systems for the efficient harvesting, storage, conversion, and management of renewable energy.


Learning Interpretable Representations of Entanglement in Quantum Optics Experiments using Deep Generative Models

1 code implementation6 Sep 2021 Daniel Flam-Shepherd, Tony Wu, Xuemei Gu, Alba Cervera-Lierta, Mario Krenn, Alan Aspuru-Guzik

The complex relationship between the setup structure of a quantum experiment and its entanglement properties is essential to fundamental research in quantum optics but is difficult to intuitively understand.

Computer vision for liquid samples in hospitals and medical labs using hierarchical image segmentation and relations prediction

1 code implementation4 May 2021 Sagi Eppel, Haoping Xu, Alan Aspuru-Guzik

This work explores the use of computer vision for image segmentation and classification of medical fluid samples in transparent containers (for example, tubes, syringes, infusion bags).

Image Segmentation Semantic Segmentation

Deep Molecular Dreaming: Inverse machine learning for de-novo molecular design and interpretability with surjective representations

1 code implementation17 Dec 2020 Cynthia Shen, Mario Krenn, Sagi Eppel, Alan Aspuru-Guzik

We expect that extending PASITHEA to larger datasets, molecules and more complex properties will lead to advances in the design of new functional molecules as well as the interpretation and explanation of machine learning models.

BIG-bench Machine Learning

Scientific intuition inspired by machine learning generated hypotheses

no code implementations27 Oct 2020 Pascal Friederich, Mario Krenn, Isaac Tamblyn, Alan Aspuru-Guzik

Machine learning with application to questions in the physical sciences has become a widely used tool, successfully applied to classification, regression and optimization tasks in many areas.

BIG-bench Machine Learning

Neural Message Passing on High Order Paths

no code implementations24 Feb 2020 Daniel Flam-Shepherd, Tony Wu, Pascal Friederich, Alan Aspuru-Guzik

Graph neural network have achieved impressive results in predicting molecular properties, but they do not directly account for local and hidden structures in the graph such as functional groups and molecular geometry.

Graph Neural Network Molecular Property Prediction +2

Graph Deconvolutional Generation

no code implementations14 Feb 2020 Daniel Flam-Shepherd, Tony Wu, Alan Aspuru-Guzik

Graph generation is an extremely important task, as graphs are found throughout different areas of science and engineering.

Decoder Graph Generation +1

Generator evaluator-selector net for panoptic image segmentation and splitting unfamiliar objects into parts

2 code implementations24 Aug 2019 Sagi Eppel, Alan Aspuru-Guzik

The generator/evaluator approach for this case consists of two independent convolutional neural nets: a generator net that suggests variety segments corresponding to objects, stuff and parts regions in the image, and an evaluator net that chooses the best segments to be merged into the segmentation map.

Image Segmentation Instance Segmentation +2

Variational quantum generators: Generative adversarial quantum machine learning for continuous distributions

no code implementations3 Jan 2019 Jonathan Romero, Alan Aspuru-Guzik

We show that our quantum generator is able to learn target probability distributions using either a classical neural network or a variational quantum circuit as the discriminator.

Quantum Physics

A framework for algorithm deployment on cloud-based quantum computers

2 code implementations24 Oct 2018 Sukin Sim, Yudong Cao, Jonathan Romero, Peter D. Johnson, Alan Aspuru-Guzik

In recent years, the field of quantum computing has significantly developed in both the improvement of hardware as well as the assembly of various software tools and platforms, including cloud access to quantum devices.

Quantum Physics

Quantum computational chemistry

2 code implementations30 Aug 2018 Sam McArdle, Suguru Endo, Alan Aspuru-Guzik, Simon Benjamin, Xiao Yuan

One of the most promising applications of quantum computing is solving classically intractable chemistry problems.

Quantum Physics

Quantum autoencoders for efficient compression of quantum data

4 code implementations8 Dec 2016 Jonathan Romero, Jonathan P. Olson, Alan Aspuru-Guzik

The quantum autoencoder is trained to compress a particular dataset of quantum states, where a classical compression algorithm cannot be employed.

Quantum Physics

Space-Filling Curves as a Novel Crystal Structure Representation for Machine Learning Models

no code implementations19 Aug 2016 Dipti Jasrasaria, Edward O. Pyzer-Knapp, Dmitrij Rappoport, Alan Aspuru-Guzik

While the structure representations based on atom connectivities are prevalent for molecules, two-dimensional descriptors are not suitable for describing molecular crystals.

BIG-bench Machine Learning

Bayesian Network Structure Learning Using Quantum Annealing

no code implementations15 Jul 2014 Bryan O'Gorman, Alejandro Perdomo-Ortiz, Ryan Babbush, Alan Aspuru-Guzik, Vadim Smelyanskiy

The logical structure resulting from the mapping has the appealing property that it is instance-independent for a given number of Bayesian network variables, as well as being independent of the number of data cases.

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