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Found 5 papers, 4 papers with code
Divergences in Color Perception between Deep Neural Networks and Humans
Deep neural networks (DNNs) are increasingly proposed as models of human vision, bolstered by their impressive performance on image classification and object recognition tasks.
Signal in Noise: Exploring Meaning Encoded in Random Character Sequences with Character-Aware Language Models
Natural language processing models learn word representations based on the distributional hypothesis, which asserts that word context (e. g., co-occurrence) correlates with meaning.
comp-syn: Perceptually Grounded Word Embeddings with Color
Popular approaches to natural language processing create word embeddings based on textual co-occurrence patterns, but often ignore embodied, sensory aspects of language.
Kernel-Based Ensemble Learning in Python
We propose a new supervised learning algorithm, for classification and regression problems where two or more preliminary predictors are available.
Pycobra: A Python Toolbox for Ensemble Learning and Visualisation
We introduce \texttt{pycobra}, a Python library devoted to ensemble learning (regression and classification) and visualisation.
