Search Results for author:
Found 14 papers, 3 papers with code
Kan Extensions in Data Science and Machine Learning
A common problem in data science is "use this function defined over this small set to generate predictions over that larger set."
Generalized Optimization: A First Step Towards Category Theoretic Learning Theory
The Cartesian reverse derivative is a categorical generalization of reverse-mode automatic differentiation.
Category Theory in Machine Learning
Over the past two decades machine learning has permeated almost every realm of technology.
Lessons Learned Addressing Dataset Bias in Model-Based Candidate Generation at Twitter
Traditionally, heuristic methods are used to generate candidates for large scale recommender systems.
Flattening Multiparameter Hierarchical Clustering Functors
We bring together topological data analysis, applied category theory, and machine learning to study multiparameter hierarchical clustering.
Functorial Manifold Learning
We adapt previous research on category theory and topological unsupervised learning to develop a functorial perspective on manifold learning, also known as nonlinear dimensionality reduction.
Functorial Clustering via Simplicial Complexes
We adapt previous research on topological unsupervised learning to characterize hierarchical overlapping clustering algorithms as functors that factor through a category of simplicial complexes.
Tuning Word2vec for Large Scale Recommendation Systems
We show that applying constrained hy-perparameter optimization using only a 10% sample of the data still yields a 91%average improvement in hit rate over the default parameters when applied to thefull datasets.
Categorical Stochastic Processes and Likelihood
In this work we take a Category Theoretic perspective on the relationship between probabilistic modeling and function approximation.
Incremental Monoidal Grammars
In this work we define formal grammars in terms of free monoidal categories, along with a functor from the category of formal grammars to the category of automata.
Learning what and where to attend with humans in the loop
Most recent gains in visual recognition have originated from the inclusion of attention mechanisms in deep convolutional networks (DCNs).
Fighting Redundancy and Model Decay with Embeddings
Every day, hundreds of millions of new Tweets containing over 40 languages of ever-shifting vernacular flow through Twitter.
A Correlation Maximization Approach for Cross Domain Co-Embeddings
Although modern recommendation systems can exploit the structure in users' item feedback, most are powerless in the face of new users who provide no structure for them to exploit.
Learning what and where to attend
Most recent gains in visual recognition have originated from the inclusion of attention mechanisms in deep convolutional networks (DCNs).
