Knowledge Base
Explore 5 core concepts in AI/ML research.
A deep learning model architecture relying on self-attention mechanisms.
Definition
The Transformer architecture processes input sequences in parallel using self-attention, allowing it to capture long-range dependencies more effectively than RNNs. It consists of encoder and decoder stacks, each containing multi-head attention and feed-forward layers.
Related Concepts
Key Papers
Examples: GPT-4, Claude
Reinforcement Learning from Human Feedback, used to align LLMs with human preferences.
Definition
RLHF trains a reward model on human preference data, then fine-tunes the language model using PPO to maximize the reward. This alignment technique helps reduce harmful outputs and improve helpfulness.
Related Concepts
Key Papers
Examples: ChatGPT alignment, Claude training
A metric for evaluating the quality of machine translated text.
Definition
BLEU (Bilingual Evaluation Understudy) compares n-gram overlaps between generated and reference translations. Scores range from 0 to 1, with higher scores indicating better translation quality.
Related Concepts
Key Papers
Examples: MT evaluation, Summarization scoring
Generative models that learn to reverse a gradual noising process.
Definition
Diffusion models add Gaussian noise to data over multiple steps, then learn to reverse this process. They achieve state-of-the-art image generation by iteratively denoising random noise into coherent samples.
Related Concepts
Key Papers
Examples: Midjourney, Stable Diffusion XL
Large-scale visual database for object recognition research.
Definition
ImageNet contains over 14 million images annotated with 20,000+ categories. The ILSVRC subset (1000 classes) became the standard benchmark for image classification, driving major advances in CNNs.
Related Concepts
Key Papers
Examples: ResNet-50 on ImageNet, ViT benchmarks