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Found 8 papers, 6 papers with code
Multiclass Neural Network Minimization via Tropical Newton Polytope Approximation
The field of tropical algebra is closely linked with the domain of neural networks with piecewise linear activations, since their output can be described via tropical polynomials in the max-plus semiring.
OpenThoughts: Data Recipes for Reasoning Models
Yet, there are still many open questions about the best training recipes for reasoning since state-of-the-art models often rely on proprietary datasets with little to no public information available.
DataComp-LM: In search of the next generation of training sets for language models
We introduce DataComp for Language Models (DCLM), a testbed for controlled dataset experiments with the goal of improving language models.
Language models scale reliably with over-training and on downstream tasks
Second, we relate the perplexity of a language model to its downstream task performance by proposing a power law.
DataComp: In search of the next generation of multimodal datasets
Multimodal datasets are a critical component in recent breakthroughs such as Stable Diffusion and GPT-4, yet their design does not receive the same research attention as model architectures or training algorithms.
Inverse Problems Leveraging Pre-trained Contrastive Representations
The problem is to recover the representation of an image R(x), if we are only given a corrupted version A(x), for some known forward operator A.
Tropical Geometrical Zonotope Reduction as Applied to Neural Network Compression.
Based on this result, we propose geometrical neural network compression methods that employ the K-means algorithm.
Tropical Polynomial Division and Neural Networks
In this work, we examine the process of Tropical Polynomial Division, a geometric method which seeks to emulate the division of regular polynomials, when applied to those of the max-plus semiring.
