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Deep-Learning-Based Kinematic Reconstruction for DUNE
In the framework of three-active-neutrino mixing, the charge parity phas...
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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...
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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...
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Quantity vs. Quality: On Hyperparameter Optimization for Deep Reinforcement Learning
Reinforcement learning algorithms can show strong variation in performan...
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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...
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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...
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Sherpa: Robust Hyperparameter Optimization for Machine Learning
Sherpa is a hyperparameter optimization library for machine learning mod...
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Continuous Representation of Molecules Using Graph Variational Autoencoder
In order to continuously represent molecules, we propose a generative mo...
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A Fortran-Keras Deep Learning Bridge for Scientific Computing
Implementing artificial neural networks is commonly achieved via high-le...
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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 ...
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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...
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Curiosity-Driven Multi-Criteria Hindsight Experience Replay
Dealing with sparse rewards is a longstanding challenge in reinforcement...
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Modeling Dynamic Functional Connectivity with Latent Factor Gaussian Processes
Dynamic functional connectivity, as measured by the time-varying covaria...
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The capacity of feedforward neural networks
A long standing open problem in the theory of neural networks is the dev...
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Towards Automating Precision Studies of Clone Detectors
Current research in clone detection suffers from poor ecosystems for eva...
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Efficient Neutrino Oscillation Parameter Inference with Gaussian Process
Neutrino oscillation study involves inferences from tiny samples of data...
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Oreo: Detection of Clones in the Twilight Zone
Source code clones are categorized into four types of increasing difficu...
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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 ...
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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 ...
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Neural Network Gradient Hamiltonian Monte Carlo
Hamiltonian Monte Carlo is a widely used algorithm for sampling from pos...
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Decorrelated Jet Substructure Tagging using Adversarial Neural Networks
We describe a strategy for constructing a neural network jet substructur...
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Learning in the Machine: Random Backpropagation and the Learning Channel
Random backpropagation (RBP) is a variant of the backpropagation algorit...
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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...
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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...
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Learning Activation Functions to Improve Deep Neural Networks
Artificial neural networks typically have a fixed, non-linear activation...
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Complex-Valued Autoencoders
Autoencoders are unsupervised machine learning circuits whose learning g...
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