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09/05/2023
Extended Symmetry Preserving Attention Networks for LHC Analysis
Reconstructing unstable heavy particles requires sophisticated technique...
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07/21/2023
Selective Perception: Optimizing State Descriptions with Reinforcement Learning for Language Model Actors
Large language models (LLMs) are being applied as actors for sequential ...
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06/14/2023
ClimSim: An open large-scale dataset for training high-resolution physics emulators in hybrid multi-scale climate simulators
Modern climate projections lack adequate spatial and temporal resolution...
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05/17/2023
End-To-End Latent Variational Diffusion Models for Inverse Problems in High Energy Physics
High-energy collisions at the Large Hadron Collider (LHC) provide valuab...
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03/30/2023
Language Models can Solve Computer Tasks
Agents capable of carrying out general tasks on a computer can improve e...
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03/10/2023
Interpretable Joint Event-Particle Reconstruction for Neutrino Physics at NOvA with Sparse CNNs and Transformers
The complex events observed at the NOvA long-baseline neutrino oscillati...
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07/19/2022
Feasible Adversarial Robust Reinforcement Learning for Underspecified Environments
Robust reinforcement learning (RL) considers the problem of learning pol...
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07/13/2022
Self-Play PSRO: Toward Optimal Populations in Two-Player Zero-Sum Games
In competitive two-agent environments, deep reinforcement learning (RL) ...
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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...
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02/15/2022
The Quarks of Attention
Attention plays a fundamental role in both natural and artificial intell...
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01/19/2022
Anytime PSRO for Two-Player Zero-Sum Games
Policy space response oracles (PSRO) is a multi-agent reinforcement lear...
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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...
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07/13/2021
Tourbillon: a Physically Plausible Neural Architecture
In a physical neural system, backpropagation is faced with a number of o...
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06/07/2021
Improving Social Welfare While Preserving Autonomy via a Pareto Mediator
Machine learning algorithms often make decisions on behalf of agents wit...
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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...
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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...
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03/11/2021
XDO: A Double Oracle Algorithm for Extensive-Form Games
Policy Space Response Oracles (PSRO) is a deep reinforcement learning al...
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02/19/2021
A theory of capacity and sparse neural encoding
Motivated by biological considerations, we study sparse neural maps from...
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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...
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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...
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12/11/2020
Deep-Learning-Based Kinematic Reconstruction for DUNE
In the framework of three-active-neutrino mixing, the charge parity phas...
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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...
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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...
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07/29/2020
Quantity vs. Quality: On Hyperparameter Optimization for Deep Reinforcement Learning
Reinforcement learning algorithms can show strong variation in performan...
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06/16/2020
SPLASH: Learnable Activation Functions for Improving Accuracy and Adversarial Robustness
We introduce SPLASH units, a class of learnable activation functions sho...
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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...
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05/08/2020
Sherpa: Robust Hyperparameter Optimization for Machine Learning
Sherpa is a hyperparameter optimization library for machine learning mod...
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04/17/2020
Continuous Representation of Molecules Using Graph Variational Autoencoder
In order to continuously represent molecules, we propose a generative mo...
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04/14/2020
A Fortran-Keras Deep Learning Bridge for Scientific Computing
Implementing artificial neural networks is commonly achieved via high-le...
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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 ...
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09/23/2019
Learning in the Machine: To Share or Not to Share?
Weight-sharing is one of the pillars behind Convolutional Neural Network...
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06/09/2019
Curiosity-Driven Multi-Criteria Hindsight Experience Replay
Dealing with sparse rewards is a longstanding challenge in reinforcement...
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05/24/2019
Modeling Dynamic Functional Connectivity with Latent Factor Gaussian Processes
Dynamic functional connectivity, as measured by the time-varying covaria...
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01/02/2019
The capacity of feedforward neural networks
A long standing open problem in the theory of neural networks is the dev...
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12/12/2018
Towards Automating Precision Studies of Clone Detectors
Current research in clone detection suffers from poor ecosystems for eva...
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11/16/2018
Efficient Neutrino Oscillation Parameter Inference with Gaussian Process
Neutrino oscillation study involves inferences from tiny samples of data...
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06/15/2018
Oreo: Detection of Clones in the Twilight Zone
Source code clones are categorized into four types of increasing difficu...
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05/18/2018
Solving the Rubik's Cube Without Human Knowledge
A generally intelligent agent must be able to teach itself how to solve ...
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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 ...
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11/14/2017
Neural Network Gradient Hamiltonian Monte Carlo
Hamiltonian Monte Carlo is a widely used algorithm for sampling from pos...
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03/10/2017
Decorrelated Jet Substructure Tagging using Adversarial Neural Networks
We describe a strategy for constructing a neural network jet substructur...
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12/08/2016
Learning in the Machine: Random Backpropagation and the Learning Channel
Random backpropagation (RBP) is a variant of the backpropagation algorit...
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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...
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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...
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12/21/2014
Learning Activation Functions to Improve Deep Neural Networks
Artificial neural networks typically have a fixed, non-linear activation...
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08/20/2011
