The Diversity of Neural Networks

Talk: Visual ensemble representations emerge in Deep Neural Networks trained for object recognition

Understanding the Resilience of Neural Network Ensembles against Faulty Training Data

Promoting High Diversity Ensemble Learning with EnsembleBench (IEEE CogMI 2020)

Deep Learning Foundations: Balaji Lakshminarayanan's Talk on Reliability via Pretrained Large Models

ICICS 2022: ODDITY: An Ensemble Framework Leverages Contrastive Representation Learning...

Research Session 2:Data Integration and Machine Learning

Balaji Lakshminarayanan (Google Brain) - Building neural networks that know what they don’t know

UTDCS Grace Series - Dr Ling Liu Prof of CS, Georgia Institute "ENSEMBLE LEARNING FROM DIRTY DATA"

Efficient Learning from Diverse Sources of Information

Learning Local Equivariant Representations for Large-Scale Atomistic Dynamics | Albert Musaelian

Scaling Map-Elites to Deep Neuro-Evolution

On Diversity, Regularization and Complexity in Ensemble Models (slides), by Giovanni Seni 20110124

Keynote: Deep Learning at Scale for Scientific Computing

Sergey Levine - Data-Driven Reinforcement Learning: Deriving Common Sense from Past Experience

Ensemble methods on the Digit dataset, by Thomas Bonald (IPP)

CS 285: Lecture 22, Part 1: Transfer Learning & Meta-Learning

DeepRange: Acoustic Ranging via Deep Learning

Using a Stacking Model Ensemble Approach to Predict Rare Events | SciPy 2019 | Susan Yuhou Xia

MIT 6.S191: Uncertainty in Deep Learning