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Found 8 papers, 0 papers with code
The loss surface of deep linear networks viewed through the algebraic geometry lens
By using the viewpoint of modern computational algebraic geometry, we explore properties of the optimization landscapes of the deep linear neural network models.
Machine learning the real discriminant locus
This article views locating the real discriminant locus as a supervised classification problem in machine learning where the goal is to determine classification boundaries over the parameter space, with the classes being the number of real solutions.
Consequences of the packing problem
We study several consequences of the packing problem, a conjecture from combinatorial optimization, using algebraic invariants of square-free monomial ideals.
Combinatorial Optimization
Commutative Algebra
Combinatorics
13C70, 13F55, 05E40, 05C65, 05C15
Convex bodies and asymptotic invariants for powers of monomial ideals
Continuing a well established tradition of associating convex bodies to monomial ideals, we initiate a program to construct asymptotic Newton polyhedra from decompositions of monomial ideals.
Commutative Algebra Combinatorics 13F55, 13F20, 52B20, 14M25
Adaptive Verifiable Coded Computing: Towards Fast, Secure and Private Distributed Machine Learning
Much of the overhead in prior schemes comes from the fact that they tightly couple coding for all three problems into a single framework.
Impact of resource distributions on the competition of species in stream environment
Our earlier work in \cite{nguyen2022population} shows that concentrating the resources on the upstream end tends to maximize the total biomass in a metapopulation model for a stream species.
Ethos: Rectifying Language Models in Orthogonal Parameter Space
Evaluations show Ethos is more effective in removing undesired knowledge and maintaining the overall model performance compared to current task arithmetic methods.
Edge Private Graph Neural Networks with Singular Value Perturbation
Eclipse adds noise to the low-rank singular values instead of the entire graph, thereby preserving the graph privacy while still maintaining enough of the graph structure to maintain model utility.
