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Proposes a reinforcement learning framework that uses locally linear embeddings to capture environment dynamics and an attention mechanism to adaptively fuse dynamics-specific and reward-specific features, inspired by neural principles, improving learning efficiency.
MM++ is a fully unsupervised, post-hoc framework for out-of-distribution detection that fuses discriminative intermediate layers via top-K gated feature fusion and uses a regularized tied covariance matrix for scale-invariant distance estimation.
This paper introduces DRoRAE, a method that improves visual tokenization by fusing multi-layer features from pretrained vision encoders rather than relying solely on the last layer. It demonstrates significant improvements in reconstruction and generation quality on ImageNet and establishes a scaling law between fusion capacity and performance.