Outstanding curation this week. The Nested Learning paper from Google Research is particularly fascinating because it suggests our standard mental model of deep networks as layer stacks may be obscuring deeper structural insights. The framing of gradient descent with momentum as a two-level associative memory module is the kind of conceptual reframe that can unlock new architecture designs. Curious whether the HOPE architectures continual learning improvements hold up when the task distribution shifts more dramatically than typical benchmarks allow.
Outstanding curation this week. The Nested Learning paper from Google Research is particularly fascinating because it suggests our standard mental model of deep networks as layer stacks may be obscuring deeper structural insights. The framing of gradient descent with momentum as a two-level associative memory module is the kind of conceptual reframe that can unlock new architecture designs. Curious whether the HOPE architectures continual learning improvements hold up when the task distribution shifts more dramatically than typical benchmarks allow.
Really appreciate it! I'm excited about the HOPE paper too and see if unlocks new designs going forward.