3 citations · 4 across the 9 of their papers we have counts for
4 papers · 1 filter
Machine-learnable Sets
Veit Elser, Manish Krishan Lal
In this study we present a formal definition of large discrete sets having, informally, three properties: their elements are easily recognized, easily generated, and the latter tas…
Learning with Boolean threshold functions
Veit Elser, Manish Krishan Lal
We develop a method for training neural networks on Boolean data in which the values at all nodes are strictly , and the resulting models are typically equivalent to network…
Cross-fluctuation phase transitions reveal sampling dynamics in diffusion models
Sai Niranjan Ramachandran, Manish Krishan Lal, Suvrit Sra
We analyse how the sampling dynamics of distributions evolve in score-based diffusion models using cross-fluctuations, a centered-moment statistic from statistical physics. Specifi…
A projection-based framework for gradient-free and parallel learning
Andreas Bergmeister, Manish Krishan Lal, Stefanie Jegelka +1
We present a feasibility-seeking approach to neural network training. This mathematical optimization framework is distinct from conventional gradient-based loss minimization and us…