b'Pierre Baldi'

research

∙ 07/19/2022

Feasible Adversarial Robust Reinforcement Learning for Underspecified Environments

Robust reinforcement learning (RL) considers the problem of learning pol...

JB Lanier, et al. ∙

research

∙ 07/13/2022

Self-Play PSRO: Toward Optimal Populations in Two-Player Zero-Sum Games

In competitive two-agent environments, deep reinforcement learning (RL) ...

Stephen McAleer, et al. ∙

research

∙ 06/06/2022

Deep Learning Models of the Discrete Component of the Galactic Interstellar Gamma-Ray Emission

A significant point-like component from the small scale (or discrete) st...

Alexander Shmakov, et al. ∙

research

∙ 02/15/2022

The Quarks of Attention

Attention plays a fundamental role in both natural and artificial intell...

Pierre Baldi, et al. ∙

research

∙ 01/19/2022

Anytime PSRO for Two-Player Zero-Sum Games

Policy space response oracles (PSRO) is a multi-agent reinforcement lear...

Stephen McAleer, et al. ∙

research

∙ 01/02/2022

Rxn Hypergraph: a Hypergraph Attention Model for Chemical Reaction Representation

It is fundamental for science and technology to be able to predict chemi...

Mohammadamin Tavakoli, et al. ∙

research

∙ 07/13/2021

Tourbillon: a Physically Plausible Neural Architecture

In a physical neural system, backpropagation is faced with a number of o...

Mohammadamin Tavakoli, et al. ∙

research

∙ 06/07/2021

Improving Social Welfare While Preserving Autonomy via a Pareto Mediator

Machine learning algorithms often make decisions on behalf of agents wit...

Stephen McAleer, et al. ∙

research

∙ 06/07/2021

SPANet: Generalized Permutationless Set Assignment for Particle Physics using Symmetry Preserving Attention

The creation of unstable heavy particles at the Large Hadron Collider is...

Alexander Shmakov, et al. ∙

research

∙ 03/24/2021

Quantum Mechanics and Machine Learning Synergies: Graph Attention Neural Networks to Predict Chemical Reactivity

There is a lack of scalable quantitative measures of reactivity for func...

Mohammadamin Tavakoli, et al. ∙

research

∙ 03/11/2021

XDO: A Double Oracle Algorithm for Extensive-Form Games

Policy Space Response Oracles (PSRO) is a deep reinforcement learning al...

Stephen McAleer, et al. ∙

research

∙ 02/19/2021

A theory of capacity and sparse neural encoding

Motivated by biological considerations, we study sparse neural maps from...

Pierre Baldi, et al. ∙

research

∙ 02/08/2021

A* Search Without Expansions: Learning Heuristic Functions with Deep Q-Networks

A* search is an informed search algorithm that uses a heuristic function...

Forest Agostinelli, et al. ∙

research

∙ 01/30/2021

Detecting Pulmonary Coccidioidomycosis (Valley fever) with Deep Convolutional Neural Networks

Coccidioidomycosis is the most common systemic mycosis in dogs in the so...

Jordan Ott, et al. ∙

research

∙ 12/11/2020

Deep-Learning-Based Kinematic Reconstruction for DUNE

In the framework of three-active-neutrino mixing, the charge parity phas...

Junze Liu, et al. ∙

research

∙ 11/10/2020

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

Two-photon excitation fluorescence (2PEF) allows imaging of tissue up to...

Stephen McAleer, et al. ∙

research

∙ 10/19/2020

Permutationless Many-Jet Event Reconstruction with Symmetry Preserving Attention Networks

Top quarks are the most massive particle in the Standard Model and are p...

Michael James Fenton, et al. ∙

research

∙ 07/29/2020

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

Reinforcement learning algorithms can show strong variation in performan...

Lars Hertel, et al. ∙

research

∙ 06/16/2020

SPLASH: Learnable Activation Functions for Improving Accuracy and Adversarial Robustness

We introduce SPLASH units, a class of learnable activation functions sho...

Mohammadamin Tavakoli, et al. ∙

research

∙ 06/15/2020

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

Finding approximate Nash equilibria in zero-sum imperfect-information ga...

Stephen McAleer, et al. ∙

research

∙ 05/08/2020

Sherpa: Robust Hyperparameter Optimization for Machine Learning

Sherpa is a hyperparameter optimization library for machine learning mod...

Lars Hertel, et al. ∙

research

∙ 04/17/2020

Continuous Representation of Molecules Using Graph Variational Autoencoder

In order to continuously represent molecules, we propose a generative mo...

Mohammadamin Tavakoli, et al. ∙

research

∙ 04/14/2020

A Fortran-Keras Deep Learning Bridge for Scientific Computing

Implementing artificial neural networks is commonly achieved via high-le...

Jordan Ott, et al. ∙

research

∙ 12/10/2019

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

Much of recent success in multiagent reinforcement learning has been in ...

Alexander Shmakov, et al. ∙

research

∙ 09/23/2019

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

Weight-sharing is one of the pillars behind Convolutional Neural Network...

Jordan Ott, et al. ∙

research

∙ 06/09/2019

Curiosity-Driven Multi-Criteria Hindsight Experience Replay

Dealing with sparse rewards is a longstanding challenge in reinforcement...

John B. Lanier, et al. ∙

research

∙ 05/24/2019

Modeling Dynamic Functional Connectivity with Latent Factor Gaussian Processes

Dynamic functional connectivity, as measured by the time-varying covaria...

Lingge Li, et al. ∙

research

∙ 01/02/2019

The capacity of feedforward neural networks

A long standing open problem in the theory of neural networks is the dev...

Pierre Baldi, et al. ∙

research

∙ 12/12/2018

Towards Automating Precision Studies of Clone Detectors

Current research in clone detection suffers from poor ecosystems for eva...

Vaibhav Saini, et al. ∙

research

∙ 11/16/2018

Efficient Neutrino Oscillation Parameter Inference with Gaussian Process

Neutrino oscillation study involves inferences from tiny samples of data...

Lingge Li, et al. ∙

research

∙ 06/15/2018

Oreo: Detection of Clones in the Twilight Zone

Source code clones are categorized into four types of increasing difficu...

Vaibhav Saini, et al. ∙

research

∙ 05/18/2018

Solving the Rubik's Cube Without Human Knowledge

A generally intelligent agent must be able to teach itself how to solve ...

Stephen McAleer, et al. ∙

research

∙ 12/22/2017

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

In a physical neural system, learning rules must be local both in space ...

Pierre Baldi, et al. ∙

research

∙ 11/14/2017

Neural Network Gradient Hamiltonian Monte Carlo

Hamiltonian Monte Carlo is a widely used algorithm for sampling from pos...

Lingge Li, et al. ∙

research

∙ 03/10/2017

Decorrelated Jet Substructure Tagging using Adversarial Neural Networks

We describe a strategy for constructing a neural network jet substructur...

Chase Shimmin, et al. ∙

research

∙ 12/08/2016

Learning in the Machine: Random Backpropagation and the Learning Channel

Random backpropagation (RBP) is a variant of the backpropagation algorit...

Pierre Baldi, et al. ∙

research

∙ 01/28/2016

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

Experiments in particle physics produce enormous quantities of data that...

Evan Racah, et al. ∙

research

∙ 06/22/2015

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

In a physical neural system, where storage and processing are intimately...

Pierre Baldi, et al. ∙

research

∙ 12/21/2014

Learning Activation Functions to Improve Deep Neural Networks

Artificial neural networks typically have a fixed, non-linear activation...

Forest Agostinelli, et al. ∙

research

∙ 08/20/2011

Complex-Valued Autoencoders

Autoencoders are unsupervised machine learning circuits whose learning g...

Pierre Baldi, et al. ∙

Pierre Baldi

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