Search Results for author: Timothy Atkinson

Found 6 papers, 3 papers with code

Bayesian Flow Networks

1 code implementation14 Aug 2023 Alex Graves, Rupesh Kumar Srivastava, Timothy Atkinson, Faustino Gomez

Notably, the network inputs for discrete data lie on the probability simplex, and are therefore natively differentiable, paving the way for gradient-based sample guidance and few-step generation in discrete domains such as language modelling.

Bayesian Inference Data Compression +2

Tiny Classifier Circuits: Evolving Accelerators for Tabular Data

no code implementations28 Feb 2023 Konstantinos Iordanou, Timothy Atkinson, Emre Ozer, Jedrzej Kufel, John Biggs, Gavin Brown, Mikel Lujan

This paper proposes a methodology for automatically generating predictor circuits for classification of tabular data with comparable prediction performance to conventional ML techniques while using substantially fewer hardware resources and power.


EvoTorch: Scalable Evolutionary Computation in Python

1 code implementation24 Feb 2023 Nihat Engin Toklu, Timothy Atkinson, Vojtěch Micka, Paweł Liskowski, Rupesh Kumar Srivastava

Evolutionary computation is an important component within various fields such as artificial intelligence research, reinforcement learning, robotics, industrial automation and/or optimization, engineering design, etc.

reinforcement-learning Reinforcement Learning (RL)

Automatic design of novel potential 3CL$^{\text{pro}}$ and PL$^{\text{pro}}$ inhibitors

no code implementations28 Jan 2021 Timothy Atkinson, Saeed Saremi, Faustino Gomez, Jonathan Masci

With the goal of designing novel inhibitors for SARS-CoV-1 and SARS-CoV-2, we propose the general molecule optimization framework, Molecular Neural Assay Search (MONAS), consisting of three components: a property predictor which identifies molecules with specific desirable properties, an energy model which approximates the statistical similarity of a given molecule to known training molecules, and a molecule search method.

Evolving Graphs with Semantic Neutral Drift

no code implementations24 Oct 2018 Timothy Atkinson, Detlef Plump, Susan Stepney

We introduce the concept of Semantic Neutral Drift (SND) for genetic programming (GP), where we exploit equivalence laws to design semantics preserving mutations guaranteed to preserve individuals' fitness scores.

The Text-Based Adventure AI Competition

1 code implementation3 Aug 2018 Timothy Atkinson, Hendrik Baier, Tara Copplestone, Sam Devlin, Jerry Swan

In 2016, 2017, and 2018 at the IEEE Conference on Computational Intelligence in Games, the authors of this paper ran a competition for agents that can play classic text-based adventure games.

Board Games Natural Language Understanding

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