Cv Classification
Best practices for image classification tasks. Use when working on CIFAR, ImageNet, or other classification benchmarks.
Nlp Alignment
Best practices for LLM alignment techniques including RLHF, DPO, and instruction tuning. Use when working on alignment or safety.
Meta Analysis
Statistical methods for combining results across multiple studies. Use when aggregating cross-study or cross-experiment results.
Nlp Pretraining
Best practices for language model pretraining and fine-tuning. Use when generating or reviewing NLP training code.
Rl Policy Optimization
Best practices for reinforcement learning policy optimization. Use when working on RL agents, PPO, SAC, or reward design.
Pytorch Training
Best practices for building robust PyTorch training loops. Use when generating or reviewing ML training code.
Systematic Review
Structured methodology for comprehensive literature review following PRISMA guidelines. Use during literature search and screening stages.
Distributed Training
Multi-GPU and distributed training patterns with PyTorch DDP. Use when scaling training across GPUs.
Mixed Precision
Use FP16/BF16 mixed precision to accelerate training and reduce memory. Use when optimizing GPU performance.
Experimental Design
Best practices for designing reproducible ML experiments. Use when planning ablations, baselines, or controlled experiments.
Cv Detection
Best practices for object detection tasks. Use when working on COCO, VOC, or detection architectures like YOLO and DETR.
Data Loading
Optimize data loading pipeline to prevent GPU starvation. Use when setting up DataLoader or data preprocessing.