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6 papers · 2 filters
ACCAMS: Additive Co-Clustering to Approximate Matrices Succinctly
Alex Beutel, Amr Ahmed, Alexander J. Smola
Matrix completion and approximation are popular tools to capture a user's preferences for recommendation and to approximate missing data. Instead of using low-rank factorization we…
A Convex Formulation for Spectral Shrunk Clustering
Xiaojun Chang, Feiping Nie, Zhigang Ma +2
Spectral clustering is a fundamental technique in the field of data mining and information processing. Most existing spectral clustering algorithms integrate dimensionality reducti…
Conditional Random Field Autoencoders for Unsupervised Structured Prediction
Waleed Ammar, Chris Dyer, Noah A. Smith
We introduce a framework for unsupervised learning of structured predictors with overlapping, global features. Each input's latent representation is predicted conditional on the ob…
Predictive Encoding of Contextual Relationships for Perceptual Inference, Interpolation and Prediction
Mingmin Zhao, Chengxu Zhuang, Yizhou Wang +1
We propose a new neurally-inspired model that can learn to encode the global relationship context of visual events across time and space and to use the contextual information to mo…
High-Performance Distributed ML at Scale through Parameter Server Consistency Models
Wei Dai, Abhimanu Kumar, Jinliang Wei +3
As Machine Learning (ML) applications increase in data size and model complexity, practitioners turn to distributed clusters to satisfy the increased computational and memory deman…
GraphLab: A New Framework For Parallel Machine Learning
Yucheng Low, Joseph E. Gonzalez, Aapo Kyrola +3
Designing and implementing efficient, provably correct parallel machine learning (ML) algorithms is challenging. Existing high-level parallel abstractions like MapReduce are insuff…