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Exact Graph Learning via Integer Programming

arXiv:2601.20589v2 Announce Type: replace-cross Abstract: Learning the dependence structure among variables in complex systems is a central problem across medical, natural, and social sciences. These structures can be naturally represented by graphs, and the task of inferring such graphs from…

Exact Graph Learning via Integer Programming

arXiv:2601.20589v2 Announce Type: replace-cross Abstract: Learning the dependence structure among variables in complex systems is a central problem across medical, natural, and social sciences. These structures can be naturally represented by graphs, and the task of inferring such graphs from…

Measuring the Representational Alignment of Neural Systems in Superposition

arXiv:2604.00208v1 Announce Type: new Abstract: Comparing the internal representations of neural networks is a central goal in both neuroscience and machine learning. Standard alignment metrics operate on raw neural activations, implicitly assuming that similar representations produce similar activity patterns. However,…

Measuring the Representational Alignment of Neural Systems in Superposition

arXiv:2604.00208v1 Announce Type: new Abstract: Comparing the internal representations of neural networks is a central goal in both neuroscience and machine learning. Standard alignment metrics operate on raw neural activations, implicitly assuming that similar representations produce similar activity patterns. However,…

Rapid mixing in positively weighted restricted Boltzmann machines

arXiv:2604.00963v1 Announce Type: cross Abstract: We show polylogarithmic mixing time bounds for the alternating-scan sampler for positively weighted restricted Boltzmann machines. This is done via analysing the same chain and the Glauber dynamics for ferromagnetic two-spin systems, where we obtain…