Pierre Baldi

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University of California, Irvine

Chancellor's Professor in the Department of Computer Science, School of Information and Computer Sciences.Director, Institute for Genomics and Bioinformatics. Associate Director, Center for Machine Learning and Data Mining, Joint appointments in the Departments of Mathematics, Statistics, Biological Chemistry, and Biomedical Engineering, Member of the California Institute for, Telecommunications and Information Technology, Member of the Institute for Mathematical Behavioral Sciences.

Deep-Learning-Based Kinematic Reconstruction for DUNE

In the framework of three-active-neutrino mixing, the charge parity phas...
12/11/2020 ∙ by Junze Liu, et al. ∙ 2 ∙ share

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Deep machine learning-assisted multiphoton microscopy to reduce light exposure and expedite imaging

Two-photon excitation fluorescence (2PEF) allows imaging of tissue up to...
11/10/2020 ∙ by Stephen McAleer, et al. ∙ 23 ∙ share

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Permutationless Many-Jet Event Reconstruction with Symmetry Preserving Attention Networks

Top quarks are the most massive particle in the Standard Model and are p...
10/19/2020 ∙ by Michael James Fenton, et al. ∙ 4 ∙ share

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Quantity vs. Quality: On Hyperparameter Optimization for Deep Reinforcement Learning

Reinforcement learning algorithms can show strong variation in performan...
07/29/2020 ∙ by Lars Hertel, et al. ∙ 16 ∙ share

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SPLASH: Learnable Activation Functions for Improving Accuracy and Adversarial Robustness

We introduce SPLASH units, a class of learnable activation functions sho...
06/16/2020 ∙ by Mohammadamin Tavakoli, et al. ∙ 17 ∙ share

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Pipeline PSRO: A Scalable Approach for Finding Approximate Nash Equilibria in Large Games

Finding approximate Nash equilibria in zero-sum imperfect-information ga...
06/15/2020 ∙ by Stephen McAleer, et al. ∙ 0 ∙ share

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Sherpa: Robust Hyperparameter Optimization for Machine Learning

Sherpa is a hyperparameter optimization library for machine learning mod...
05/08/2020 ∙ by Lars Hertel, et al. ∙ 111 ∙ share

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Continuous Representation of Molecules Using Graph Variational Autoencoder

In order to continuously represent molecules, we propose a generative mo...
04/17/2020 ∙ by Mohammadamin Tavakoli, et al. ∙ 1 ∙ share

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A Fortran-Keras Deep Learning Bridge for Scientific Computing

Implementing artificial neural networks is commonly achieved via high-le...
04/14/2020 ∙ by Jordan Ott, et al. ∙ 0 ∙ share

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ColosseumRL: A Framework for Multiagent Reinforcement Learning in N-Player Games

Much of recent success in multiagent reinforcement learning has been in ...
12/10/2019 ∙ by Alexander Shmakov, et al. ∙ 0 ∙ share

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Learning in the Machine: To Share or Not to Share?

Weight-sharing is one of the pillars behind Convolutional Neural Network...
09/23/2019 ∙ by Jordan Ott, et al. ∙ 10 ∙ share

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Curiosity-Driven Multi-Criteria Hindsight Experience Replay

Dealing with sparse rewards is a longstanding challenge in reinforcement...
06/09/2019 ∙ by John B. Lanier, et al. ∙ 3 ∙ share

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Modeling Dynamic Functional Connectivity with Latent Factor Gaussian Processes

Dynamic functional connectivity, as measured by the time-varying covaria...
05/24/2019 ∙ by Lingge Li, et al. ∙ 0 ∙ share

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The capacity of feedforward neural networks

A long standing open problem in the theory of neural networks is the dev...
01/02/2019 ∙ by Pierre Baldi, et al. ∙ 0 ∙ share

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Towards Automating Precision Studies of Clone Detectors

Current research in clone detection suffers from poor ecosystems for eva...
12/12/2018 ∙ by Vaibhav Saini, et al. ∙ 0 ∙ share

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Efficient Neutrino Oscillation Parameter Inference with Gaussian Process

Neutrino oscillation study involves inferences from tiny samples of data...
11/16/2018 ∙ by Lingge Li, et al. ∙ 0 ∙ share

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Oreo: Detection of Clones in the Twilight Zone

Source code clones are categorized into four types of increasing difficu...
06/15/2018 ∙ by Vaibhav Saini, et al. ∙ 0 ∙ share

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Solving the Rubik's Cube Without Human Knowledge

A generally intelligent agent must be able to teach itself how to solve ...
05/18/2018 ∙ by Stephen McAleer, et al. ∙ 0 ∙ share

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Learning in the Machine: the Symmetries of the Deep Learning Channel

In a physical neural system, learning rules must be local both in space ...
12/22/2017 ∙ by Pierre Baldi, et al. ∙ 0 ∙ share

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Neural Network Gradient Hamiltonian Monte Carlo

Hamiltonian Monte Carlo is a widely used algorithm for sampling from pos...
11/14/2017 ∙ by Lingge Li, et al. ∙ 0 ∙ share

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Decorrelated Jet Substructure Tagging using Adversarial Neural Networks

We describe a strategy for constructing a neural network jet substructur...
03/10/2017 ∙ by Chase Shimmin, et al. ∙ 0 ∙ share

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Learning in the Machine: Random Backpropagation and the Learning Channel

Random backpropagation (RBP) is a variant of the backpropagation algorit...
12/08/2016 ∙ by Pierre Baldi, et al. ∙ 0 ∙ share

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Revealing Fundamental Physics from the Daya Bay Neutrino Experiment using Deep Neural Networks

Experiments in particle physics produce enormous quantities of data that...
01/28/2016 ∙ by Evan Racah, et al. ∙ 0 ∙ share

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A Theory of Local Learning, the Learning Channel, and the Optimality of Backpropagation

In a physical neural system, where storage and processing are intimately...
06/22/2015 ∙ by Pierre Baldi, et al. ∙ 0 ∙ share

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Learning Activation Functions to Improve Deep Neural Networks

Artificial neural networks typically have a fixed, non-linear activation...
12/21/2014 ∙ by Forest Agostinelli, et al. ∙ 0 ∙ share

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Complex-Valued Autoencoders

Autoencoders are unsupervised machine learning circuits whose learning g...
08/20/2011 ∙ by Pierre Baldi, et al. ∙ 0 ∙ share

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Pierre Baldi

Featured Co-authors

Hernando Ombao

Prabhat

Babak Shahbaba

Lars Hertel

Peter Sadowski

Roy Fox

Wahid Bhimji

Evan Racah

Jordan Ott

Matthew Hoffman

Andrew Holbrook

Deep-Learning-Based Kinematic Reconstruction for DUNE

Deep machine learning-assisted multiphoton microscopy to reduce light exposure and expedite imaging

Permutationless Many-Jet Event Reconstruction with Symmetry Preserving Attention Networks

Quantity vs. Quality: On Hyperparameter Optimization for Deep Reinforcement Learning

SPLASH: Learnable Activation Functions for Improving Accuracy and Adversarial Robustness

Pipeline PSRO: A Scalable Approach for Finding Approximate Nash Equilibria in Large Games

Sherpa: Robust Hyperparameter Optimization for Machine Learning

Continuous Representation of Molecules Using Graph Variational Autoencoder

A Fortran-Keras Deep Learning Bridge for Scientific Computing

ColosseumRL: A Framework for Multiagent Reinforcement Learning in N-Player Games

Learning in the Machine: To Share or Not to Share?

Curiosity-Driven Multi-Criteria Hindsight Experience Replay

Modeling Dynamic Functional Connectivity with Latent Factor Gaussian Processes

The capacity of feedforward neural networks

Towards Automating Precision Studies of Clone Detectors

Efficient Neutrino Oscillation Parameter Inference with Gaussian Process

Oreo: Detection of Clones in the Twilight Zone

Solving the Rubik's Cube Without Human Knowledge

Learning in the Machine: the Symmetries of the Deep Learning Channel

Neural Network Gradient Hamiltonian Monte Carlo

Decorrelated Jet Substructure Tagging using Adversarial Neural Networks

Learning in the Machine: Random Backpropagation and the Learning Channel

Revealing Fundamental Physics from the Daya Bay Neutrino Experiment using Deep Neural Networks

A Theory of Local Learning, the Learning Channel, and the Optimality of Backpropagation

Learning Activation Functions to Improve Deep Neural Networks

Complex-Valued Autoencoders