Sequential Attention: Greedy Feature Selection That Actually Scales To Modern Neural Networks скачать в хорошем качестве

Sequential Attention: Greedy Feature Selection That Actually Scales To Modern Neural Networks

Read the full article here: https://binaryverseai.com/sequential-... Feature selection sounds simple until you try to do it in a modern neural network. The search space explodes, interactions hide the real signal, and “just rank features once” turns into a redundancy magnet. In this video, I break down Sequential Attention, a scalable greedy subset selection method that re-scores candidates after every pick. You’ll see why classic greedy forward selection becomes infeasible, how a differentiable mask turns selection into something you can train inside a single model run, and how the method naturally handles redundancy and synergy. We’ll also connect the dots to Orthogonal Matching Pursuit (OMP) in the linear setting, then close with practical integration tips, logging, and sanity checks so you don’t fool yourself. If you care about speed, accuracy, and interpretability, this is one of the cleanest ways to think about greedy selection at deep-learning scale. Chapters: 00:00 Introduction: The Data Mountain 00:46 The Latency Budget & Scavenger Hunt 01:51 Static Ranking vs. Dynamic Drafting 03:08 What 'Attention' Means in Context 03:55 Why Feature Selection is NP-Hard 04:55 The Old Way: Greedy Forward Selection 05:48 The Innovation: Differentiable Mask 06:48 The Selection Loop Algorithm 08:05 Redundancy & Synergy 09:25 Theoretical Grounding: The OMP Connection 10:20 Integration & Logging Best Practices 11:25 Sanity Checks: Don't Fool Yourself 12:15 Real Tradeoffs & Failure Modes 13:28 Beyond Feature Selection: Structured Pruning 14:15 Summary: The Big Three 14:48 Conclusion: Be a Detective, Not a Tourist 👍 If this helped, like, subscribe, and share it with someone battling a 10,000-feature input pipeline.