Transformer – Page 3

Attention Is All You Need: Transformer Architecture Review

2026년 01월 18일

Attention Is All You Need dismantled: why self-attention replaced RNNs, how positional encoding works, and the 3 tricks that made it scale.

2026년 01월 06일

Autoencoder vs transformer for production line defects: edge deployment benchmarks reveal why reconstruction error fails 40% of the time.

2026년 01월 04일

Transformer self-attention math decoded: positional encoding, multi-head mechanics, and why it beat RNN/LSTM. Full equations + ablations.

2025년 12월 17일

FlashAttention-3 cuts Transformer memory from O(n²) to O(n) on H100 GPUs. See PyTorch benchmarks proving 3-5x speedup on 32k context windows.

2025년 12월 15일

RoPE vs Alibi vs xPos on 128k token contexts: which Transformer position encoding prevents attention dilution? Benchmarks and PyTorch code inside.

2025년 12월 07일

FlashAttention cuts Transformer memory from O(N²) to O(N) while running 2-4× faster. Here's the IO-aware tiling trick explained.

2025년 12월 07일

RetNet achieves 8.4× faster inference than Transformer with 70% less memory. The parallel training + sequential inference proof inside.

2025년 11월 15일

Transformer beat LSTM by 18% RMSE on NASA turbofan RUL prediction. Attention maps reveal why — and when LSTM still wins.