Mixture of experts

Mixture of experts (MoE) divides problem space into homogeneous regions

Mixture of experts (MoE) is a machine learning technique that employs multiple expert networks to partition a problem space into regions where each expert is highly specialized. This specialization allows MoE to achieve better performance by leveraging the strengths of each expert for different parts of the data.

Example

In natural language processing, MoE can be used to classify sentences by assigning different experts to handle specific linguistic features, such as syntax, semantics, or sentiment.

MoE improves model performance by utilizing the expertise of multiple networks, leading to more accurate and efficient predictions.

Related concepts

MoE models have more parameters but similar compute cost

MoE models distribute parameters across k experts, reducing active experts' compute cost

load balancing loss is needed in MoE

Load balancing loss in MoE prevents expert collapse by distributing workload evenly across experts

Graduate Aptitude Test in Engineering

GATE exam assesses engineering and science undergraduate subjects for postgraduate admissions in India

[CLS] pooling does: uses the first token's embedding as the sentence representation

CLS pooling: uses the first token's embedding as the sentence representation

GraphSAGE does: samples and aggregates a fixed-size neighborhood

GraphSAGE samples and aggregates a fixed-size neighborhood

the lottery ticket hypothesis says: sparse subnetworks can match full network performance

Lottery ticket hypothesis posits sparse subnetworks can match full network performance

One email a day: 5 concepts + the 5 stories that matter →

Swipe through 100 ML concepts daily

Open TickerNews