Year hub · 8 claims
AI/ML claims from 2017
Hand-verified research claims with primary sources dated 2017. Each claim has ≥2 primary sources and an HMAC-SHA256 signature.
AdamW optimizer introduced in paper: Decoupled Weight Decay Regularization (Loshchilov & Hutter, 2017).
b6d51eba4fc7f918 · 2 sources · 100% confidence
FAISS introduced in: Johnson, Douze, Jégou 2017 — Facebook AI Similarity Search.
7ee9546a5a7d851e · 2 sources · 100% confidence
Mixture of Experts (MoE) revival popularized in: Shazeer et al. 2017 — outrageously large neural networks via sparse gating.
f068236101568ad7 · 2 sources · 100% confidence
Proximal Policy Optimization (PPO) introduced in paper: Proximal Policy Optimization Algorithms (Schulman et al., 2017).
00f224e1ccc158ef · 2 sources · 100% confidence
PyTorch publicly released on: 2017-01-18 by Facebook AI Research.
76c83fea654bb445 · 2 sources · 100% confidence
Reinforcement Learning from Human Feedback (RLHF) introduced in paper: Deep Reinforcement Learning from Human Preferences (Christiano et al., 2017).
67866330cd60e54d · 3 sources · 100% confidence
Sparsely-Gated Mixture-of-Experts (MoE) introduced in paper: Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer (Shazeer et al., 2017).
2d6d7f61f1db6493 · 1 source · 100% confidence
Transformer architecture introduced in paper: Attention Is All You Need (Vaswani et al., 2017).
ad17e76a8baad7a1 · 3 sources · 100% confidence
Foundational papers · 2024-2025 releases · All claims · All topic hubs