End-to-End Learning for Stochastic Optimization: A Bayesian Perspective

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End-to-End Learning for Stochastic Optimization: A Bayesian Perspective

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A Stochastic Approach to Contact-rich Manipulation

Dimensionless Group-Driven Ensemble Neural Networks for Robust Design Optimisation in Engineering

First and Zeroth-Order Implementations of the Regularized Newton Method with Lazy Approximated Hessians

Integer Linear Programming for Unsupervised Training Set Selection in Molecular Machine Learning

A shortcut model for batch distillation columns using pertinent effective factors

Directly optimizing for synthesizability in generative molecular design using retrosynthesis models

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Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination

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Deep Learning Theory Through the Lens of Diagonal Linear Networks

Incorporating Projective Geometry into Deep Learning

Performing and Detecting Backdoor Attacks on Face Recognition Algorithms

Few-shot Learning for Efficient and Effective Machine Learning Model Adaptation

Coupling a recurrent neural network to SPAD TCSPC systems for real-time fluorescence lifetime imaging

Understanding generalization and robustness in modern deep learning

Task-driven neural network models predict neural dynamics of proprioception: Neural network model weights

Machine Learning for Modeling Stock Returns

On the Generalization of Stochastic Gradient Descent with Momentum