9 articles
Cosine annealing
Cosine annealing is a learning rate schedule for training neural networks that decays the rate along half a cosine curve, introduced by Loshchilov and Hutter in 2016.
Differential privacy SGD
Differentially private stochastic gradient descent (DP-SGD) is a privacy-preserving variant of stochastic gradient descent, introduced by Abadi et al. in 2016 and the standard method for private neural network training.
Gradient clipping
Gradient clipping is a modification to gradient descent that caps the gradient's norm at a chosen threshold each iteration, used to stabilize training and enforce differential privacy.
Group robustness optimization
Group robustness optimization, or group DRO, is a machine learning training method that minimizes worst-case loss over pre-defined groups rather than average loss, improving worst-group accuracy.
Momentum method
The momentum method is an optimization technique in machine learning that adds a fraction of the previous update to each gradient step, dampening oscillations and speeding convergence.
Nadam
Nadam, short for Nesterov-accelerated Adaptive Moment Estimation, is an optimization algorithm for training neural networks that combines Adam with Nesterov momentum, introduced by Timothy Dozat in 2016.
Sharpness-aware minimization
Sharpness-aware minimization (SAM) is a training procedure that perturbs model weights toward higher loss before each descent step, favoring flat minima; introduced by Foret and colleagues in 2020.
Variational neural annealing
Variational neural annealing (VNA) is an optimization method combining annealing with variational training of an autoregressive neural network, introduced in Nature Machine Intelligence in 2021.
Weight decay
Weight decay is a regularization technique for training neural networks in which weights are multiplicatively shrunk toward zero each step, traditionally by adding an L2 penalty to the loss function.