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Found 12 papers, 3 papers with code
Learning Sparse Nonparametric DAGs
We develop a framework for learning sparse nonparametric directed acyclic graphs (DAGs) from data.
Efficient Tensor Decomposition with Boolean Factors
Tensor decomposition has been extensively used as a tool for exploratory analysis.
DAGs with NO TEARS: Continuous Optimization for Structure Learning
This is achieved by a novel characterization of acyclicity that is not only smooth but also exact.
State Space LSTM Models with Particle MCMC Inference
Long Short-Term Memory (LSTM) is one of the most powerful sequence models.
LightLDA: Big Topic Models on Modest Compute Clusters
When building large-scale machine learning (ML) programs, such as big topic models or deep neural nets, one usually assumes such tasks can only be attempted with industrial-sized clusters with thousands of nodes, which are out of reach for most practitioners or academic researchers.
On Model Parallelization and Scheduling Strategies for Distributed Machine Learning
Distributed machine learning has typically been approached from a data parallel perspective, where big data are partitioned to multiple workers and an algorithm is executed concurrently over different data subsets under various synchronization schemes to ensure speed-up and/or correctness.
Model-Parallel Inference for Big Topic Models
In real world industrial applications of topic modeling, the ability to capture gigantic conceptual space by learning an ultra-high dimensional topical representation, i. e., the so-called "big model", is becoming the next desideratum after enthusiasms on "big data", especially for fine-grained downstream tasks such as online advertising, where good performances are usually achieved by regression-based predictors built on millions if not billions of input features.
Primitives for Dynamic Big Model Parallelism
When training large machine learning models with many variables or parameters, a single machine is often inadequate since the model may be too large to fit in memory, while training can take a long time even with stochastic updates.
Petuum: A New Platform for Distributed Machine Learning on Big Data
What is a systematic way to efficiently apply a wide spectrum of advanced ML programs to industrial scale problems, using Big Models (up to 100s of billions of parameters) on Big Data (up to terabytes or petabytes)?
Consistent Bounded-Asynchronous Parameter Servers for Distributed ML
Many ML algorithms fall into the category of \emph{iterative convergent algorithms} which start from a randomly chosen initial point and converge to optima by repeating iteratively a set of procedures.
Scalable Inference for Logistic-Normal Topic Models
Logistic-normal topic models can effectively discover correlation structures among latent topics.
Improved Bayesian Logistic Supervised Topic Models with Data Augmentation
Supervised topic models with a logistic likelihood have two issues that potentially limit their practical use: 1) response variables are usually over-weighted by document word counts; and 2) existing variational inference methods make strict mean-field assumptions.
