Sparse Model
A sparse model activates only a subset of its parameters for each input, reducing computational cost while maintaining capacity. Mixture of Experts and pruned networks are common sparse model architectures.
Understanding Sparse Model
A sparse model is a machine learning model where a significant portion of parameters are zero or near-zero, resulting in reduced memory footprint, faster inference, and lower energy consumption compared to dense alternatives. Sparsity can be achieved through pruning (removing unimportant weights after training), sparse training (maintaining sparsity throughout training), or architectures that inherently use conditional computation like mixture-of-experts models. Research has shown that large neural networks contain sparse subnetworks that can match the performance of the full model, a finding known as the lottery ticket hypothesis. Sparse models are critical for deploying AI on edge devices and mobile AI chips with limited resources. The technique relates to model serving optimization and is complementary to other efficiency methods like quantization and depthwise separable convolutions. As scaling laws push models ever larger, sparsity offers a path to maintaining capability while reducing computational demands.
Category
Deep Learning
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Activation Function
An activation function is a mathematical function applied to a neuron's output to introduce non-linearity into a neural network. Common activation functions include ReLU, sigmoid, and tanh, each with different properties for gradient flow.
Adam Optimizer
Adam (Adaptive Moment Estimation) is an optimization algorithm that combines the benefits of AdaGrad and RMSProp. It adapts learning rates for each parameter using estimates of first and second moments of gradients.
Adapter Layers
Adapter layers are small trainable modules inserted into a pre-trained model to enable parameter-efficient fine-tuning. They allow task adaptation while keeping the original model weights frozen.
Attention Mechanism
An attention mechanism allows neural networks to focus on the most relevant parts of the input when producing each element of the output. Attention is the foundational innovation behind the Transformer architecture and modern large language models.
Autoencoder
An autoencoder is a neural network trained to compress input data into a compact representation and then reconstruct it. Autoencoders are used for dimensionality reduction, denoising, and learning latent representations.
Backpropagation
Backpropagation is the algorithm used to train neural networks by computing gradients of the loss function with respect to each weight. It propagates error signals backward through the network to update weights and minimize prediction errors.
Batch Normalization
Batch normalization is a technique that normalizes layer inputs across mini-batches during training to stabilize and accelerate neural network training. It reduces internal covariate shift and allows higher learning rates.
Batch Size
Batch size is the number of training examples used in one iteration of gradient descent. Larger batches provide more stable gradient estimates but require more memory, while smaller batches add beneficial noise.