Smart contract optimization is the process of improving the performance and efficiency of smart contracts, which are self-executing programs on blockchain platforms. This involves making the code use less computing power, storage, and transaction fees, while still achieving the same results. Well-optimised smart contracts are faster, more secure, and cost less to run for users…
Category: Model Optimisation Techniques
Layer 2 Transaction Optimization
Layer 2 transaction optimisation refers to methods and technologies that improve the speed and reduce the cost of transactions on blockchain networks by processing them off the main blockchain, or Layer 1. These solutions use separate protocols or networks to handle transactions, then periodically record summaries or proofs back to the main chain. This approach…
Token Incentive Optimization
Token incentive optimisation is the process of designing and adjusting rewards in digital token systems to encourage desirable behaviours among users. It involves analysing how people respond to different incentives and making changes to maximise engagement, participation, or other goals. This approach helps ensure that the token system remains effective, sustainable, and aligned with the…
Federated Learning Optimization
Federated learning optimisation is the process of improving how machine learning models are trained across multiple devices or servers without sharing raw data between them. Each participant trains a model on their own data and only shares the learned updates, which are then combined to create a better global model. Optimisation in this context involves…
Differential Privacy Optimization
Differential privacy optimisation is a process of adjusting data analysis methods so they protect individuals’ privacy while still providing useful results. It involves adding carefully controlled random noise to data or outputs to prevent someone from identifying specific people from the data. The goal is to balance privacy and accuracy, so the information remains helpful…
Inference Optimization Techniques
Inference optimisation techniques are methods used to make machine learning models run faster and use less computer power when making predictions. These techniques focus on improving the speed and efficiency of models after they have already been trained. Common strategies include reducing the size of the model, simplifying its calculations, or using special hardware to…
Neural Sparsity Optimization
Neural sparsity optimisation is a technique used to make artificial neural networks more efficient by reducing the number of active connections or neurons. This process involves identifying and removing parts of the network that are not essential for accurate predictions, helping to decrease the amount of memory and computing power needed. By making neural networks…
Model Efficiency Metrics
Model efficiency metrics are measurements used to evaluate how effectively a machine learning model uses resources like time, memory, and computational power while making predictions. These metrics help developers understand the trade-off between a model’s accuracy and its resource consumption. By tracking model efficiency, teams can choose solutions that are both fast and practical for…
Multi-Objective Learning
Multi-objective learning is a machine learning approach where a model is trained to achieve several goals at the same time, rather than just one. Instead of optimising for a single outcome, such as accuracy, the model balances multiple objectives, which may sometimes conflict with each other. This approach is useful when real-world tasks require considering…
Model Quantization Strategies
Model quantisation strategies are techniques used to reduce the size and computational requirements of machine learning models. They work by representing numbers with fewer bits, for example using 8-bit integers instead of 32-bit floating point values. This makes models run faster and use less memory, often with only a small drop in accuracy.