This paper describes how to convert a machine learning problem into a series of map-reduce tasks. We study logistic regression algorithm. In logistic regression algorithm, it is assumed that samples are independent and each sample is assigned a probability. Parameters are obtained by maxmizing the product of all sample…
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11 results for “map-reduce”
TensorWatch enables real-time interactive analysis of deep learning training.
problem Challenges in diagnosing and exploring deep learning models during training.
method Modeling inspection and diagnostic tasks as streams using a map-reduce paradigm.
result Real-time interactive queries on live deep learning training processes.
Let be a flat principal bundle over a closed and oriented manifold of dimension . We construct a map of Lie algebras $Ψ: \H_{2\ast} (L M) \to ø(\Mc)$, where $\H_{2\ast} (LM)$ is the even dimensional part of the equivariant homology of , the free loop space of , and $\Mc$ is the Maurer-C…
A formula for triangle area in Deep Sets form.
problem Finding a polynomial formula for triangle area in Deep Sets form.
method Expressing area as a permutation-invariant function and finding a suitable Deep Sets form.
result Explicit polynomial formula for triangle area in Deep Sets form.
NLARS: Minimum Redundancy Maximum Relevance Feature Selection for Large and High-dimensional Datastat.ML
We propose a feature selection method that finds non-redundant features from a large and high-dimensional data in nonlinear way. Specifically, we propose a nonlinear extension of the non-negative least-angle regression (LARS) called NLARS, where the similarity between input and output is measured through the norm…
The paper studies maps and reducibility for cocycles into CAT(0)-spaces.
problem Existence and reducibility of cocycles into CAT(0)-spaces.
method Analyzes discrete groups acting on CAT(0)-spaces and uses invariant sections and Furstenberg maps.
result Maximal cocycles in PU(1,∞) are finitely reducible.
Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Modelsstat.ML
Gaussian processes (GPs) are a powerful tool for probabilistic inference over functions. They have been applied to both regression and non-linear dimensionality reduction, and offer desirable properties such as uncertainty estimates, robustness to over-fitting, and principled ways for tuning hyper-parameters. However t…
The Dirichlet process (DP) is a fundamental mathematical tool for Bayesian nonparametric modeling, and is widely used in tasks such as density estimation, natural language processing, and time series modeling. Although MCMC inference methods for the DP often provide a gold standard in terms asymptotic accuracy, they ca…
JAMPI improves matrix multiplication in Spark, boosting performance by up to 24%.
problem Efficiently performing matrix multiplication in Spark.
method Combining asynchronous network IO, auto-vectorization, and barrier execution mode.
result Up to 24% performance increase in distributed matrix multiplication.
tf_geometric simplifies graph deep learning in TensorFlow.
problem Efficient graph deep learning in TensorFlow.
method Kernel libraries and infrastructures for GNNs.
result tf_geometric supports various graph tasks and provides efficient GNN models.
Transformers exhibit sparse activation maps, reducing computational load and improving robustness.
problem Sparse activation in Transformer models.
method Extensive experiments on various Transformer architectures and tasks.
result Sparsity in Transformers is a prevalent phenomenon, reducing FLOP count and improving model robustness.