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Found 7 papers, 0 papers with code
Softmax Tree: An Accurate, Fast Classifier When the Number of Classes Is Large
Classification problems having thousands or more classes naturally occur in NLP, for example language models or document classification.
Smaller, more accurate regression forests using tree alternating optimization
We show that using TAO with the bagging approach produces much better forests than random forests, Adaboost or gradient boosting in every dataset we have tried across a wide range of input and output dimensionality and sample size.
GenCO: Generating Diverse Solutions to Design Problems with Combinatorial Nature
Generating diverse objects (e. g., images) using generative models (such as GAN or VAE) has achieved impressive results in the recent years, to help solve many design problems that are traditionally done by humans.
Towards Better Decision Forests: Forest Alternating Optimization
However, unlike for most other models, such as neural networks, optimizing forests or trees is not easy, because they define a non-differentiable function.
Faster Neural Net Inference via Forests of Sparse Oblique Decision Trees
We show that neural nets can be further compressed by replacing layers of it with a special type of decision forest.
Sparse Oblique Decision Trees: A Tool to Understand and Manipulate Neural Net Features
The widespread deployment of deep nets in practical applications has lead to a growing desire to understand how and why such black-box methods perform prediction.
An Experimental Comparison of Old and New Decision Tree Algorithms
This paper presents a detailed comparison of a recently proposed algorithm for optimizing decision trees, tree alternating optimization (TAO), with other popular, established algorithms.
