Results for "stochastic network"

Optimization under uncertainty.

A parameterized function composed of interconnected units organized in layers with nonlinear activations.

A gradient method using random minibatches for efficient training on large datasets.

Neural networks can approximate any continuous function under certain conditions.

Early architecture using learned gates for skip connections.

Probabilistic energy-based neural network with hidden variables.

Nonlinear functions enabling networks to approximate complex mappings; ReLU variants dominate modern DL.

Allows gradients to bypass layers, enabling very deep networks.

Stochastic generation strategies that trade determinism for diversity; key knobs include temperature and nucleus sampling.

Randomly zeroing activations during training to reduce co-adaptation and overfitting.

Methods to set starting weights to preserve signal/gradient scales across layers.

Raw model outputs before converting to probabilities; manipulated during decoding and calibration.

Networks with recurrent connections for sequences; largely supplanted by Transformers for many tasks.

A narrow hidden layer forcing compact representations.

Tradeoffs between many layers vs many neurons per layer.

Neural networks that operate on graph-structured data by propagating information along edges.

Chooses which experts process each token.

GNN framework where nodes iteratively exchange and aggregate messages from neighbors.

Two-network setup where generator fools a discriminator.

Loss of old knowledge when learning new tasks.

A branch of ML using multi-layer neural networks to learn hierarchical representations, often excelling in vision, speech, and language.

Iterative method that updates parameters in the direction of negative gradient to minimize loss.

Learning where data arrives sequentially and the model updates continuously, often under changing distributions.

Uses an exponential moving average of gradients to speed convergence and reduce oscillation.

Popular optimizer combining momentum and per-parameter adaptive step sizes via first/second moment estimates.

Number of samples per gradient update; impacts compute efficiency, generalization, and stability.

One complete traversal of the training dataset during training.

Training across many devices/silos without centralizing raw data; aggregates updates, not data.

Samples from the k highest-probability tokens to limit unlikely outputs.

Optimization with multiple local minima/saddle points; typical in neural networks.