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cs.LG2026
Inference and learning in sparse autoencoders as natural gradient flow
Hadi Vafaii, Tejas Rao, David Chanin +6
Sparse autoencoders are widely used to uncover interpretable features in neural networks, yet reliable recovery remains difficult when features overlap or activate infrequently. Th…
stat.ML2026
Metabolic cost of information processing in Poisson variational autoencoders
Hadi Vafaii, Jacob L. Yates
Computation in biological systems is fundamentally energy-constrained, yet standard theories of computation treat energy as freely available. Here, we argue that variational free e…
stat.ML2026
A hitchhiker's guide to Poisson gradient estimation
Michael Ibrahim, Hanqi Zhao, Eli Sennesh +5
Poisson-distributed latent variable models are widely used in computational neuroscience, but differentiating through discrete stochastic samples remains challenging. Two approache…