Statements (50)
| Predicate | Object |
|---|---|
| gptkbp:instance_of |
gptkb:Artificial_Intelligence
|
| gptkbp:benefits |
computational efficiency
adaptive learning rate sensitivity to hyperparameters |
| gptkbp:can_be_combined_with |
momentum and RMSProp
|
| gptkbp:developed_by |
gptkb:D._P_Kingma
|
| https://www.w3.org/2000/01/rdf-schema#label |
Adam Optimizer
|
| gptkbp:input_output |
model weights
optimized parameters |
| gptkbp:is |
widely used
easy to implement used in natural language processing used in reinforcement learning suitable for large datasets a default choice for many applications a first-order optimization algorithm a key component in many deep learning frameworks a method for adaptive learning rates a method for stochastic optimization a method that uses moving averages of gradients a popular choice among practitioners a variant of stochastic gradient descent designed for non-stationary objectives popular in deep learning robust to noisy gradients used in computer vision a method that adjusts learning rates based on past gradients |
| gptkbp:is_implemented_in |
gptkb:Tensor_Flow
gptkb:Py_Torch |
| gptkbp:orbital_period |
beta1
beta2 epsilon learning rate |
| gptkbp:performance |
often used in practice
generally better than SGD works well with sparse gradients |
| gptkbp:related_to |
stochastic gradient descent
adaptive gradient methods |
| gptkbp:requires |
tuning of hyperparameters
initialization of parameters |
| gptkbp:used_for |
training neural networks
|
| gptkbp:used_in |
gptkb:machine_learning
|
| gptkbp:variant |
gptkb:Ada_Max
gptkb:Adam_W Nadam |
| gptkbp:year_established |
gptkb:2014
|
| gptkbp:bfsParent |
gptkb:Feedforward_Neural_Network
gptkb:neural_networks gptkb:Variational_Autoencoders |
| gptkbp:bfsLayer |
4
|