
DeepLearningBased Kinematic Reconstruction for DUNE
In the framework of threeactiveneutrino mixing, the charge parity phas...
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Deep machine learningassisted multiphoton microscopy to reduce light exposure and expedite imaging
Twophoton excitation fluorescence (2PEF) allows imaging of tissue up to...
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Permutationless ManyJet 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 zerosum imperfectinformation 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 FortranKeras Deep Learning Bridge for Scientific Computing
Implementing artificial neural networks is commonly achieved via highle...
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ColosseumRL: A Framework for Multiagent Reinforcement Learning in NPlayer 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?
Weightsharing is one of the pillars behind Convolutional Neural Network...
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CuriosityDriven MultiCriteria 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 timevarying 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, nonlinear activation...
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ComplexValued Autoencoders
Autoencoders are unsupervised machine learning circuits whose learning g...
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Pierre Baldi
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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.