A machine learning technique that combines labeled and unlabeled data to improve learning efficiency.
AI using a few labeled X-ray images and many unlabeled ones to improve diagnosis.
An optimization algorithm where AI updates model parameters using small random samples.
AI training neural networks with faster convergence using SGD.
A technique where AI learns patterns from data without explicit labels.
AI pretraining language models like GPT using self-supervised learning.
A method where AI represents data using fewer non-zero elements for efficiency.
AI reducing storage size by encoding images with sparse representations.
AI models that predict structured outputs like sequences or graphs.
AI predicting full sentences instead of single words in translation tasks.
A branch of AI that uses logic and rules for reasoning.
AI diagnosing diseases based on logical rules.
A type of AI model that mimics brain neuron firing patterns.
AI running on neuromorphic chips for ultra-low power processing.
AI networks that compare and match similar inputs.
AI matching handwritten signatures for authentication.
A mathematical function that converts scores into probabilities.
AI predicting object categories in images using softmax output.
AI models that learn efficient, compressed representations.
AI detecting fraudulent transactions using sparse autoencoders.
An AI clustering technique that uses graph-based approaches to group data.
AI segmenting different regions in satellite images using spectral clustering.
Mathematical models where AI accounts for randomness over time.
AI forecasting stock trends using stochastic models.
Neural networks that organize and visualize high-dimensional data.
AI clustering customer purchasing behaviors using SOM.
An optimization technique where AI selects the best subset from a dataset.
AI selecting key features in medical diagnosis using submodular optimization.
A reinforcement learning algorithm that balances exploration and exploitation.
AI controlling robotic arms using SAC for efficient movement.
A method where AI learns continuously from real-time data streams.
AI detecting credit card fraud instantly from live transactions.
AI models that approximate complex systems for faster predictions.
AI replacing expensive weather simulations with faster surrogate models.
A principle where AI reduces both error and complexity in model training.
AI improving generalization in SVMs using structural risk minimization.
A probabilistic approach where AI learns using minimal parameters.
AI predicting economic trends using sparse Bayesian models.
A technique where AI trains in simulations before real-world deployment.
AI training a self-driving car in a virtual environment before real-world testing.
A computer vision technique where AI labels each pixel in an image with a class.
AI identifying pedestrians, roads, and vehicles in self-driving cars.
A variant of PCA where AI selects important features with sparsity constraints.
AI identifying key genetic markers in DNA analysis using Sparse PCA.
A computing approach where AI handles uncertainty and imprecision.
AI using fuzzy logic for weather prediction models.
AI identifying key influencers in Twitter networks.
A technique where AI learns efficient, compact representations of data.
AI reducing image storage size using sparse representations.
An AI method that analyzes patterns based on grammar rules.
AI parsing sentences to identify correct grammatical structure.
A measure of how many training samples AI needs to generalize well.
AI determining the number of labeled examples required for accurate classification.
A measure in AI that quantifies the uncertainty in data distributions.
AI analyzing entropy in cryptographic security systems.
A concept where AI stores multiple features in shared parameters.
AI using superposition to train smaller yet more powerful networks.
A technique where AI combines multiple sensor inputs for better decision-making.
AI combining lidar, radar, and cameras in self-driving cars.
An AI method that uses simpler models to approximate complex functions.
AI optimizing deep learning parameters using surrogate models.
A technique in AI for filtering out small values to enforce sparsity.
AI improving image clarity by removing noise with soft thresholding.
A type of machine learning where AI predicts structured outputs like sequences or trees.
AI generating sentence syntax trees from raw text input.
A neural network that learns compressed representations with sparse activations.
AI detecting fraudulent transactions using sparse autoencoders.
A type of AI model that mimics biological neuron spiking behavior.
AI-powered robotics using SNN for low-power processing.
A mathematical framework where AI models randomness in time-series data.
AI predicting currency exchange rates using stochastic models.
A deep learning model where AI maps input sequences to output sequences.
AI translating English to French using sequence-to-sequence learning.
A field where AI represents knowledge using rules and logic.
AI diagnosing diseases using symbolic reasoning.
A metaheuristic AI optimization technique inspired by cooling metal.
AI optimizing airline flight schedules using simulated annealing.
A Bayesian deep learning method where AI adds noise to gradient descent for uncertainty estimation.
AI estimating model uncertainty in financial forecasting.
Used in predictive maintenance and customer retention analysis.
AI-powered assistants like Siri and Alexa.
ML is a subset of AI that enables machines to learn patterns from data and make predictions or decisions without explicit programming.
Spam detection in emails using classification models.
DL is a subset of ML that uses artificial neural networks to process complex data and perform high-level computations.
Image recognition in self-driving cars.
Gen AI refers to AI models that generate new content, including text, images, and code, using trained knowledge bases.
AI models like ChatGPT and Stable Diffusion that generate text and images.
Social Network Analysis
AI methods for analyzing social relationships using graph theory.