Large Language Model with 2.7B parameters. Architecture: Transformer decoder with rotary position embeddings. Training: 825GB text corpus.

Bidirectional Encoder Representations from Transformers. 24 layers, 1024 hidden units. Pre-trained on BookCorpus + Wikipedia.

Extended context transformer with recurrence mechanism. Segment-level recurrence with relative positional encoding.

Real-time object detection. CSPDarknet53 backbone with PANet neck. Trained on COCO dataset with 80 object classes.

Text-to-image diffusion model. Latent Diffusion Model (LDM) with CLIP text encoder and U-Net denoising network.

Contrastive Language-Image Pre-training. Vision Transformer + Text Transformer trained on 400M image-text pairs.

Deep Q-Network with experience replay and target network. CNN + fully connected layers for Atari game environments.

Proximal Policy Optimization for continuous control. Actor-Critic architecture with clipped surrogate objective.

Generative Adversarial Network with Progressive Growing. WGAN-GP loss for stable training on CelebA-HQ dataset.

β-Variational Autoencoder for disentangled representation learning. KL divergence with β=4 regularization.

Quantum-inspired optimization using Variational Quantum Eigensolver (VQE) with parameterized quantum circuits.

Evolutionary computation with tournament selection, crossover, and mutation operators for complex optimization problems.