neuralparameter

A neuralparameter is an adjustable element of a neural network that directly determines how inputs are transformed into outputs. In common usage it refers to the model’s learned parameters, most notably weights and biases, which are iteratively updated during training to minimize a loss function. Depending on the architecture, neuralparameters can include additional learned components that influence data flow and representation.

Types of neuralparameters include the per-layer weights that connect neurons, per-neuron biases, and parameters in normalization and attention mechanisms, such as scale and shift terms in normalization layers and projection matrices in attention heads. Some architectures also incorporate specialized neuralparameters, like adapters or factorized factors, that modulate behavior without changing the overall structure.

Training and optimization involve adjusting neuralparameters to reduce error on labeled data. Backpropagation computes gradients of the loss with respect to each neuralparameter, and gradient-based optimizers update their values. Initialization strategies, such as Xavier/Glorot or He/Kaiming, help start training in a stable regime. Regularization techniques, including L1/L2 penalties, dropout, and weight sharing, control capacity and improve generalization.

Neuralparameters are distinct from hyperparameters, which are set before training (for example, learning rate, batch size, and network depth). After training, neuralparameters remain fixed during inference, enabling the network to map new inputs to outputs. In some variants, neuralparameters may be treated probabilistically (as in Bayesian neural networks) or subjected to quantization and pruning for efficiency.