TF.Learn: TensorFlow's High-level Module for Distributed Machine Learning

13 Dec 20161 code implementation

TF. Learn is a high-level Python module for distributed machine learning inside TensorFlow.

TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems

14 Mar 20162 code implementations

TensorFlow is an interface for expressing machine learning algorithms, and an implementation for executing such algorithms.

DIMENSIONALITY REDUCTION

TensorFlow Eager: A Multi-Stage, Python-Embedded DSL for Machine Learning

27 Feb 20191 code implementation

TensorFlow Eager is a multi-stage, Python-embedded domain-specific language for hardware-accelerated machine learning, suitable for both interactive research and production.

TensorFlow Estimators: Managing Simplicity vs. Flexibility in High-Level Machine Learning Frameworks

8 Aug 20171 code implementation

Our focus is on simplifying cutting edge machine learning for practitioners in order to bring such technologies into production.

Adversarial Machine Learning at Scale

4 Nov 20168 code implementations

Adversarial examples are malicious inputs designed to fool machine learning models.

Scikit-learn: Machine Learning in Python

2 Jan 20123 code implementations

Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems.

DIMENSIONALITY REDUCTION MODEL SELECTION

MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems

3 Dec 20152 code implementations

This paper describes both the API design and the system implementation of MXNet, and explains how embedding of both symbolic expression and tensor operation is handled in a unified fashion.

DIMENSIONALITY REDUCTION

Neural Additive Models: Interpretable Machine Learning with Neural Nets

29 Apr 20202 code implementations

NAMs learn a linear combination of neural networks that each attend to a single input feature.

DECISION MAKING INTERPRETABLE MACHINE LEARNING

Benchmarking Automatic Machine Learning Frameworks

17 Aug 20184 code implementations

AutoML serves as the bridge between varying levels of expertise when designing machine learning systems and expedites the data science process.

AUTOMATED FEATURE ENGINEERING HYPERPARAMETER OPTIMIZATION

Optimization with Non-Differentiable Constraints with Applications to Fairness, Recall, Churn, and Other Goals

11 Sep 20181 code implementation

This new formulation leads to an algorithm that produces a stochastic classifier by playing a two-player non-zero-sum game solving for what we call a semi-coarse correlated equilibrium, which in turn corresponds to an approximately optimal and feasible solution to the constrained optimization problem.

FAIRNESS