arXiv research

3d-SMRnet speeds up MPI system matrix recovery to 1 minute with high quality.

Python framework for distributed Keras training on multiple GPUs/CPU.

SparCML improves machine learning scalability by handling sparse data efficiently.

AITuning uses reinforcement learning to optimize communication libraries.

Paper proposes a GPU-based system for training massive deep learning models in ads systems.

Non-negative matrix factorization (NMF) is the problem of determining two non-negative low rank factors $W$ and $H$, for the given input matrix $A$, such that $A \approx W H$. NMF is a useful tool for many applications in different domains such as topic modeling in text mining, background separation in video analysis, …

This paper considers a cross-layer adaptive modulation system that is modeled as a Markov decision process (MDP). We study how to utilize the monotonicity of the optimal transmission policy to relieve the computational complexity of dynamic programming (DP). In this system, a scheduler controls the bit rate of the m-qu…

Asynchronous SVI scales up Bayesian computation for massive data.

The paper optimizes dynamic scheduling for ring architectures in deep learning training.

Gradient coding improves distributed learning by tolerating failures.

ParaMonte::Python streamlines Bayesian data analysis with fast Monte Carlo and MCMC routines.

Proposes a linear model for facial action recognition without requiring large datasets.

Financial institutions have massive computations to carry out overnight which are very demanding in terms of the consumed CPU. The challenge is to price many different products on a cluster-like architecture. We have used the Premia software to valuate the financial derivatives. In this work, we explain how Premia can …

New method speeds up nuclear-norm constrained learning over multiple machines.

ParaMonte simplifies Monte Carlo simulations for various scientific fields.

Combines soft greediness with Modified Policy Iteration for more efficient deep reinforcement learning.

Linear algebra approach for parallel deep learning models.

Etalumis bridges scientific simulators and probabilistic programming.

Enhanced SMC$^2$ uses gradients from CRN-PF in Langevin proposals for improved state and parameter estimation.

Paper analyzes convergence of entropy-regularized reinforcement learning methods.

Study compares three performance metrics of Bangladeshi banks.

Enhanced 3D shape analysis using information geometry.

HybridSGD improves SGD performance by balancing computation and communication.

JAMPI improves matrix multiplication in Spark, boosting performance by up to 24%.

We present the first public release of our generic neural network training algorithm, called SkyNet. This efficient and robust machine learning tool is able to train large and deep feed-forward neural networks, including autoencoders, for use in a wide range of supervised and unsupervised learning applications, such as…

New DCD and BDCD methods for K-SVM and K-RR reduce communication costs.

Many powerful machine learning models are based on the composition of multiple processing layers, such as deep nets, which gives rise to nonconvex objective functions. A general, recent approach to optimise such "nested" functions is the method of auxiliary coordinates (MAC). MAC introduces an auxiliary coordinate for …

Extension formulae on almost complex manifolds studied with applications.

The paper proves extension theorems for holomorphic sections from divisors.

Generalizes Nielsen equivalence theorem to hyperbolic group extensions.

The paper connects group extensions, cochains, and spectral sequences.

Proves HNN extensions of nilpotent groups are left-orderable, constructs non-left-orderable examples.

Paper analyzes mathematical theory behind out-of-sample DR extensions.

Examines differential smoothness in a specific skew PBW extension family.

New insights into identifying mixtures of product distributions using Hadamard extensions.

A spacetime can be embedded in an enveloping space with all its extensions.

We generalize the prequantization central extension of a group of diffeomorphisms preserving a closed 2-form ω(ω-invariant diffeomorphisms) to an abelian extension of a group of diffeomorphisms preserving a closed vector valued 2-form ω, up to a linear isomorphism (ω-equivariant diffeomorphisms). Every abelian extensio…

We give a new variant of $L^2$-extension theorem for the jets of holomorphic sections and discuss the relation between the extension problem of singular Hermitian metrics with semipositive curvature.

Study on flux homomorphism and its extension in symplectic group of a disk.

The purpose of this paper is to show how central extensions of (possibly infinite-dimensional) Lie algebras integrate to central extensions of étale Lie 2-groups. In finite dimensions, central extensions of Lie algebras integrate to central extensions of Lie groups, a fact which is due to the vanishing of π_2 for each …

We construct a Kruskal-Szekeres-type analytic extension of the Emparan-Reall black ring, and investigate its geometry. We prove that the extension is maximal, globally hyperbolic, and unique within a natural class of extensions. The key to those results is the proof that causal geodesics are either complete, or approac…

Analytic linearization and holomorphic extensions for proper groupoids.

We study the properties of Modified Riemann extensions evolving under Ricci flow. We obtain the necessary and sufficient condition for modified Riemann extension under Ricci flow to stay as modified Riemann extension. We also discuss the properties of the curvature tensors under Ricci flow.

The paper examines differential smoothness in skew PBW extensions over polynomial rings.

Quasiregular maps get harmonic extensions in hyperbolic space.

Simple construction of Lie 2-groups from loop group extensions.

Proves Girth Alternative for some HNN extensions, finds counterexamples.

Kan extensions help in data science extrapolation and learning.