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Found 6 papers, 0 papers with code
A fast, universal algorithm to learn parametric nonlinear embeddings
This has two advantages: 1) The algorithm is universal in that a specific learning algorithm for any choice of embedding and mapping can be constructed by simply reusing existing algorithms for the embedding and for the mapping.
Alternating optimization of decision trees, with application to learning sparse oblique trees
Learning a decision tree from data is a difficult optimization problem.
Structured Multi-Hashing for Model Compression
Despite the success of deep neural networks (DNNs), state-of-the-art models are too large to deploy on low-resource devices or common server configurations in which multiple models are held in memory.
Optimal Quantization Using Scaled Codebook
We study the problem of quantizing N sorted, scalar datapoints with a fixed codebook containing K entries that are allowed to be rescaled.
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.
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.
