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.

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

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.

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.

In this paper, we introduce the freely available SUpervised Self-organIzing maps (SUSI) Python package which performs supervised regression and classification.

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.

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

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

SOTA for Automated Theorem Proving on CoqGym

Iterative compilation is a widely adopted technique to optimize programs for different constraints such as performance, code size and power consumption in rapidly evolving hardware and software environments.

This paper explores a specific probabilistic programming paradigm, namely message passing in Forney-style factor graphs (FFGs), in the context of automated design of efficient Bayesian signal processing algorithms.