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Found 4 papers, 0 papers with code
Neural Discovery of Permutation Subgroups
Unlike the traditional $H$-invariant networks wherein $H$ is assumed to be known, we present a method to discover the underlying subgroup, given that it satisfies certain conditions.
A Unified Framework for Discovering Discrete Symmetries
At the core of the framework is a novel architecture composed of linear, matrix-valued and non-linear functions that expresses functions invariant to these subgroups in a principled manner.
GPT-Neo for commonsense reasoning -- a theoretical and practical lens
Recent work has demonstrated substantial gains in pre-training large-language models (LLMs) followed by supervised fine-tuning on the downstream task.
A survey of deep learning optimizers -- first and second order methods
Deep Learning optimization involves minimizing a high-dimensional loss function in the weight space which is often perceived as difficult due to its inherent difficulties such as saddle points, local minima, ill-conditioning of the Hessian and limited compute resources.
