Three frameworks adapt foundation models for generalized category discovery under domain shifts via disentanglement and prompt tuning, showing gains on synthetic and real multi-domain data.
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Symplectic inductive bias combined with chain policies yields sufficient conditions for target reachability in Hamiltonian systems whose sample complexity depends on recurrence and geometry rather than ambient dimension.
Experiments indicate that small-batch SGD promotes flatter loss minima and better generalization in overparameterized networks, and that sparse subnetworks can retain nearly full accuracy.
citing papers explorer
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Generalized Category Discovery under Domain Shifts: From Vision to Vision-Language Models
Three frameworks adapt foundation models for generalized category discovery under domain shifts via disentanglement and prompt tuning, showing gains on synthetic and real multi-domain data.
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Symplectic Inductive Bias for Data-Driven Target Reachability in Hamiltonian Systems
Symplectic inductive bias combined with chain policies yields sufficient conditions for target reachability in Hamiltonian systems whose sample complexity depends on recurrence and geometry rather than ambient dimension.
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Implicit Regularization and Generalization in Overparameterized Neural Networks
Experiments indicate that small-batch SGD promotes flatter loss minima and better generalization in overparameterized networks, and that sparse subnetworks can retain nearly full accuracy.