activity
20092022
most citedBADGER: Learning to (Learn [Learning Algorithms] through Multi-Agent Communication)

17 citations · 48 across the 11 of their papers we have counts for

collaborators

15 papers

cs.LG2022★ 1 cited

A Simple Guard for Learned Optimizers

Isabeau Prémont-Schwarz, Jaroslav Vítků, Jan Feyereisl

If the trend of learned components eventually outperforming their hand-crafted version continues, learned optimizers will eventually outperform hand-crafted optimizers like SGD or…

cs.AI2019★ 17 cited

BADGER: Learning to (Learn [Learning Algorithms] through Multi-Agent Communication)

Marek Rosa, Olga Afanasjeva, Simon Andersson +6

In this work, we propose a novel memory-based multi-agent meta-learning architecture and learning procedure that allows for learning of a shared communication policy that enables t…

cs.AI2019★ 7 cited

ToyArchitecture: Unsupervised Learning of Interpretable Models of the World

Jaroslav Vítků, Petr Dluhoš, Joseph Davidson +10

Research in Artificial Intelligence (AI) has focused mostly on two extremes: either on small improvements in narrow AI domains, or on universal theoretical frameworks which are usu…

cs.AI2017

General AI Challenge - Round One: Gradual Learning

Jan Feyereisl, Matej Nikl, Martin Poliak +2

The General AI Challenge is an initiative to encourage the wider artificial intelligence community to focus on important problems in building intelligent machines with more general…

cs.AI2016★ 12 cited

A Framework for Searching for General Artificial Intelligence

Marek Rosa, Jan Feyereisl, The GoodAI Collective

There is a significant lack of unified approaches to building generally intelligent machines. The majority of current artificial intelligence research operates within a very narrow…

cs.AI2016★ 5 cited

Self-Organising Maps in Computer Security

Jan Feyereisl, Uwe Aickelin

Some argue that biologically inspired algorithms are the future of solving difficult problems in computer science. Others strongly believe that the future lies in the exploration o…

cs.LG2013

Quiet in Class: Classification, Noise and the Dendritic Cell Algorithm

Feng Gu, Jan Feyereisl, Robert Oates +3

Theoretical analyses of the Dendritic Cell Algorithm (DCA) have yielded several criticisms about its underlying structure and operation. As a result, several alterations and fixes…

cs.CE2013

Investigating the Detection of Adverse Drug Events in a UK General Practice Electronic Health-Care Database

Jenna Reps, Jan Feyereisl, Jonathan M. Garibaldi +3

Data-mining techniques have frequently been developed for Spontaneous reporting databases. These techniques aim to find adverse drug events accurately and efficiently. Spontaneous…

cs.NE2013

Wavelet feature extraction and genetic algorithm for biomarker detection in colorectal cancer data

Yihui Liu, Uwe Aickelin, Jan Feyereisl +1

Biomarkers which predict patient's survival can play an important role in medical diagnosis and treatment. How to select the significant biomarkers from hundreds of protein markers…

cs.LG2013

Privileged Information for Data Clustering

Jan Feyereisl, Uwe Aickelin

Many machine learning algorithms assume that all input samples are independently and identically distributed from some common distribution on either the input space X, in the case…

cs.AI2010

ToLeRating UR-STD

Jan Feyereisl, Uwe Aickelin

A new emerging paradigm of Uncertain Risk of Suspicion, Threat and Danger, observed across the field of information security, is described. Based on this paradigm a novel approach…

cs.AI2010★ 6 cited

The DCA:SOMe Comparison A comparative study between two biologically-inspired algorithms

Julie Greensmith, Jan Feyereisl, Uwe Aickelin

The Dendritic Cell Algorithm (DCA) is an immune-inspired algorithm, developed for the purpose of anomaly detection. The algorithm performs multi-sensor data fusion and correlation…

cs.AI2010

STORM - A Novel Information Fusion and Cluster Interpretation Technique

Jan Feyereisl, Uwe Aickelin

Analysis of data without labels is commonly subject to scrutiny by unsupervised machine learning techniques. Such techniques provide more meaningful representations, useful for bet…

cs.AI2010

Detecting Motifs in System Call Sequences

William O. Wilson, Jan Feyereisl, Uwe Aickelin

The search for patterns or motifs in data represents an area of key interest to many researchers. In this paper we present the Motif Tracking Algorithm, a novel immune inspired pat…

cs.AI2009

Artificial Immune Tissue using Self-Orgamizing Networks

Jan Feyereisl, Uwe Aickelin

As introduced by Bentley et al. (2005), artificial immune systems (AIS) are lacking tissue, which is present in one form or another in all living multi-cellular organisms. Some hav…