Summary
Identity Teacher Forcing (ITF) has been highly effective for stably training recurrent neural networks (RNNs) to reconstruct chaotic dynamical systems. However, this paper highlights that as an intervention-based prediction loss, teacher forcing's optimization geometry may not align with the free-running model's marginal likelihood, indicating a potential mismatch when switching surrogates for chaotic dynamics.
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[Paper] Ruka-v2: Tendon Driven Open-Source Dexterous Hand with Wrist and Abduction for Robot Learning
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