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Found 7 papers, 1 papers with code
The Multilinear Structure of ReLU Networks
By appealing to harmonic analysis we show that all local minima of such network are non-differentiable, except for those minima that occur in a region of parameter space where the loss surface is perfectly flat.
Deep linear neural networks with arbitrary loss: All local minima are global
We consider deep linear networks with arbitrary convex differentiable loss.
A recurrent neural network without chaos
We introduce an exceptionally simple gated recurrent neural network (RNN) that achieves performance comparable to well-known gated architectures, such as LSTMs and GRUs, on the word-level language modeling task.
The Product Cut
We introduce a theoretical and algorithmic framework for multi-way graph partitioning that relies on a multiplicative cut-based objective.
Consistency of Cheeger and Ratio Graph Cuts
We consider point clouds obtained as samples of a ground-truth measure.
An Incremental Reseeding Strategy for Clustering
In this work we propose a simple and easily parallelizable algorithm for multiway graph partitioning.
