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Found 18 papers, 5 papers with code
Large-Scale Automatic Audiobook Creation
In this work, we present a system that can automatically generate high-quality audiobooks from online e-books.
A Tensor Compiler for Unified Machine Learning Prediction Serving
Machine Learning (ML) adoption in the enterprise requires simpler and more efficient software infrastructure---the bespoke solutions typical in large web companies are simply untenable.
MLOS: An Infrastructure for Automated Software Performance Engineering
In this paper we present: MLOS, an ML-powered infrastructure and methodology to democratize and automate Software Performance Engineering.
Vamsa: Automated Provenance Tracking in Data Science Scripts
There has recently been a lot of ongoing research in the areas of fairness, bias and explainability of machine learning (ML) models due to the self-evident or regulatory requirements of various ML applications.
Data Science through the looking glass and what we found there
The recent success of machine learning (ML) has led to an explosive growth both in terms of new systems and algorithms built in industry and academia, and new applications built by an ever-growing community of data science (DS) practitioners.
FLAML: A Fast and Lightweight AutoML Library
We study the problem of using low computational cost to automate the choices of learners and hyperparameters for an ad-hoc training dataset and error metric, by conducting trials of different configurations on the given training data.
Extending Relational Query Processing with ML Inference
The broadening adoption of machine learning in the enterprise is increasing the pressure for strict governance and cost-effective performance, in particular for the common and consequential steps of model storage and inference.
PDP: A General Neural Framework for Learning SAT Solvers
There have been recent efforts for incorporating Graph Neural Network models for learning fully neural solvers for constraint satisfaction problems (CSP) and particularly Boolean satisfiability (SAT).
Cloudy with high chance of DBMS: A 10-year prediction for Enterprise-Grade ML
Consequently, rigorous data management has emerged as a key requirement in enterprise settings.
Making Classical Machine Learning Pipelines Differentiable: A Neural Translation Approach
To this end, we propose a framework that translates a pre-trained ML pipeline into a neural network and fine-tunes the ML models within the pipeline jointly using backpropagation.
Machine Learning at Microsoft with ML .NET
Machine Learning is transitioning from an art and science into a technology available to every developer.
Learning To Solve Circuit-SAT: An Unsupervised Differentiable Approach
Recent efforts to combine Representation Learning with Formal Methods, commonly known as the Neuro-Symbolic Methods, have given rise to a new trend of applying rich neural architectures to solve classical combinatorial optimization problems.
MLSys: The New Frontier of Machine Learning Systems
Machine learning (ML) techniques are enjoying rapidly increasing adoption.
PDP: A General Neural Framework for Learning Constraint Satisfaction Solvers
In this paper, we propose a generic neural framework for learning CSP solvers that can be described in terms of probabilistic inference and yet learn search strategies beyond greedy search.
PRETZEL: Opening the Black Box of Machine Learning Prediction Serving Systems
Machine Learning models are often composed of pipelines of transformations.
Batch-Expansion Training: An Efficient Optimization Framework
We propose Batch-Expansion Training (BET), a framework for running a batch optimizer on a gradually expanding dataset.
Towards Geo-Distributed Machine Learning
Current solutions to learning from geo-distributed data sources revolve around the idea of first centralizing the data in one data center, and then training locally.
Parallelized Stochastic Gradient Descent
With the increase in available data parallel machine learning has become an increasingly pressing problem.
