Image Colorization with Generative Adversarial Networks

14 Mar 20185 code implementations

Over the last decade, the process of automatic image colorization has been of significant interest for several application areas including restoration of aged or degraded images.

COLORIZATION

Layered TPOT: Speeding up Tree-based Pipeline Optimization

18 Jan 20181 code implementation

With the demand for machine learning increasing, so does the demand for tools which make it easier to use.

AUTOMATED FEATURE ENGINEERING HYPERPARAMETER OPTIMIZATION

SQLFlow: A Bridge between SQL and Machine Learning

19 Jan 20201 code implementation

Previous database systems extended their SQL dialect to support ML.

mlpack 3: a fast, flexible machine learning library

Journal of Open Source Software 2018 1 code implementation

In the past several years, the field of machine learning has seen an explosion of interest and excitement, with hundreds or thousands of algorithms developed for different tasks every year.

Developing Bug-Free Machine Learning Systems With Formal Mathematics

ICML 2017 1 code implementation

As a case study, we implement a new system, Certigrad, for optimizing over stochastic computation graphs, and we generate a formal (i. e. machine-checkable) proof that the gradients sampled by the system are unbiased estimates of the true mathematical gradients.

SuSi: Supervised Self-Organizing Maps for Regression and Classification in Python

26 Mar 20192 code implementations

In this paper, we introduce the freely available Supervised Self-organizing maps (SuSi) Python package which performs supervised regression and classification.

pymoo: Multi-objective Optimization in Python

22 Jan 20201 code implementation

To address this issue, we have developed pymoo, a multi-objective optimization framework in Python.

DECISION MAKING

Neural Architecture Search with Bayesian Optimisation and Optimal Transport

NeurIPS 2018 1 code implementation

A common use case for BO in machine learning is model selection, where it is not possible to analytically model the generalisation performance of a statistical model, and we resort to noisy and expensive training and validation procedures to choose the best model.

BAYESIAN OPTIMISATION MODEL SELECTION NEURAL ARCHITECTURE SEARCH

LightLDA: Big Topic Models on Modest Compute Clusters

4 Dec 20141 code implementation

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.

TOPIC MODELS

Learning to Prove Theorems via Interacting with Proof Assistants

21 May 20191 code implementation

Proof assistants offer a formalism that resembles human mathematical reasoning, representing theorems in higher-order logic and proofs as high-level tactics.

AUTOMATED THEOREM PROVING MATHEMATICAL PROOFS