Machine Learning Blog

MPhil-PhD transfer seminar – Dan Philps

News, Seminar.

MPhil-PhD transfer presentation

Dan Philps

Thursday, 21 September 2017, 2pm

Room: A109 (College building)

Title: Motif Memory Nets

Abstract: Motifs are recurring patterns in time-series data that have the power to explain or forecast future events/data points. In the context of lifelong learning of a time-continuous dataset, “motif awareness” would improve modelling accuracy when compared to “motif naïve” generalizations. This study introduces a “motif aware” methodology called Motif Memory Nets (MMN), an ensemble approach based on a multi-column architecture of deep feedforward neural networks, employing a memory framework using Dynamic Time Warping. MMN identify, remember and recall “models” of repeating motifs in time continuous, multivariate time-series data, achieving an improvement in accuracy and, in some cases, parsimony over generalised approaches tested on the same problems. The contributions of this work are several: Firstly, this study introduces novel memory augmentation using a simple memory crystallization methodology that does not require traditional “gating”. Secondly, for memory retrieval, it is found that it is not necessary to fully learn the complex and potentially sparse distribution of the way a “memory associates with the problem space”, instead using a conditional framework drawn from the idea of “motif discovery” (or “similarity”) results in a much reduced memory retrieval and balancing task. Thirdly, MMN’s deep architecture draws on several threads in the literature applying “memory augmentation” but uses a synchronous, parallel architecture which can support distilling, while allowing MMNs to be theoretically deployable across processes and machines. Fourthly, MMNs are successfully applied to a complex, real-world dataset in the domain of Finance, a novel application for memory augmented approaches.

Keywords: Deep neural network, memory model, motif, dynamic time warping, multi-column, ensemble, time-series, lifelong learning.

Short bio: Dan Philps is a career Fund Manager and quantitative finance researcher and is working towards achieving a PhD in Machine Learning, applied (currently) to the domain of Finance, with supervisors Prof Artur Garcez and Dr Tillman Weyde. Commercially, Mr. Philps is Head of Rothko Investment Strategies – the quantitative equity investment group – and chairs the Rothko Investment and Research Committee. Prior to this, he was a Senior Fund Manager in Mondrian Investment Partners’ Global Fixed Income and Currencies team and before joining Mondrian, in 1998, Mr. Philps was a Consultant Quantitative Analyst/Programmer in the equity and derivatives businesses of Dresdner-KB, Bankers Trust and Barclays Capital, specializing in trading and risk models. Mr. Philps has a BSc (Hons) from King’s College London, is a CFA Charterholder, a member of the CFA Institute and a member of the CFA Society of the UK.

All welcome!


ML Seminar: Thu, 1 June, 2pm, ELG10, Jakob Foerster (University of Oxford)

News, Seminar.

Machine Learning Seminar

Thursday, 1 June, 2pm, ELG10

Jakob Foerster (University of Oxford)

Title: Counterfactual Multi-Agent Policy Gradients

Abstract: Cooperative multi-agent systems can be naturally used to model many real world problems, such as network packet routing or the coordination of autonomous vehicles. There is a great need for new reinforcement learning methods that can efficiently learn decentralised policies for such systems. To this end, we propose a new multi-agent actor-critic method called counterfactual multi-agent (COMA) policy gradients. COMA uses a centralised critic to estimate the Q-function and decentralised actors to optimise the agents’ policies. In addition, to address the challenges of multi-agent credit assignment, it uses a counterfactual baseline that marginalises out a single agent’s action, while keeping the other agents’ actions fixed. COMA also uses a critic representation that allows the counterfactual base- line to be computed efficiently in a single forward pass. We evaluate COMA in the testbed of StarCraft unit micromanagement, using a decentralised variant with significant partial observability. COMA significantly improves average performance over other multi-agent actor-critic methods in this setting, and the best performing agents are competitive with state-of-the-art centralised controllers that get access to the full state.

ML Seminar: 15 May 2017, 1pm, AG10

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ML seminar

Kaio Motawara

When: 15 May 2017, 1pm

Where: AG10


Abstract: Appropriate feature engineering, the process of combining and transforming raw features, can contribute significantly to improving the performance of a supervised learning task. Because of the combinatorial explosion of possible engineered features, very often successful feature engineering in a corporate environment requires domain knowledge. However, with the increasing complexity of data and numbers of sources of data, applying human insight to the feature engineering process becomes difficult.

In this presentation, I introduce a novel, fast, and hopefully elegant algorithm where the complexity of the process of feature engineering is only connected to the number of features relevant to modelling the target variable, instead of all the features in a dataset. When the number of relevant features is much smaller than the total number of features, this amounts to increasing the efficiency to almost constant time. This method draws in insights from previous work linking machine learning and information theory. I then present the results of tests of the algorithm, which show that the engineering method I have developed is indeed effective in creating a feature that improves the performance of both classification and regression algorithms when the engineered feature is included along with the rest of the features. In order to reach statistically valid conclusions, it is necessary to test the algorithm on large numbers of appropriate datasets. Therefore I also introduce a method by which to generate synthetic datasets with desirable characteristics on which to test machine learning algorithms.

At this stage, the work is a proof of concept of the method created. Future work would include creating more generalised methods and coding these into Python packages for use by the data science community.

Greg Slabaugh receives the Research Student Supervision Award 2017


Hosted and organised by the Students’ Union, the Learning Enhancement Awards allow City students to recognise staff for their support (

Member of the Machine Learning Research Centre, Greg Slabaugh received the Research Student Supervision award 2017, an award that covers all Schools at City, University of London.


Machine Learning seminar by Prof Duncan Gillies, Imperial College

News, Seminar.

Machine Learning seminar

Prof Duncan Gillies, Imperial College London

Monday, 20 March 2017, 2pm. College Building, room AG22

Title: Visual Cognition in Face Recognition

(work by C. E. Thomaz, V. Amaral, G. A. Giraldi, D. F. Gillies and D. Rueckert)

Abstract: Recently new methods have been emerging in the field of image recognition. In particular, deep neural networks have reached very high levels of accuracy out performing other learning paradigms on large image data bases However, although deep networks are biologically inspired they tell us very little about how human visual perception works. They can correctly classify a picture, but they cannot give us any interpretation of that picture. Human vision has been found to operate quite differently. When viewing a picture we tend to concentrate our gaze at a small number of key points rather than looking at it globally. Our efforts are directed towards interpreting rather than classifying. An interesting question is “is it possible to characterise and make use of the mechanisms of human visual perception?”

The talk will focus on face recognition, and introduce some recent work carried out as a joint project between Centro Universitario FEI and Imperial College on incorporating human visual cognition with existing face recognition algorithms. It will give a brief overview of the history of face recognition including Sirovich and Kirby’s work on face spaces using PCA and Yarbus’ eye tracking experiment on human perception. It will then go on to describe some experiments where human fixation points have been used to spatially weight the PCA face feature space leading to improved accuracy of recognition.

12th International Workshop on Neural-Symbolic Learning and Reasoning (NeSy’17) at City, University of London, 17-18 July 2017

News, Seminar.

The 12th edition of the Workshop on Neural-Symbolic Learning and Reasoning will take place at City, University of London, on 17 and 18 July 2017.

The workshop studies the combination of well-founded symbolic AI with robust neural computing to help tackle data-driven challenges in many areas of application, from health to finance, transport and global business.

Information about paper submission deadlines, how to register, and the new industry track can be found at


Machine Learning seminar and discussion panel

News, Seminar.

19 Dec 2016, AG05, 2pm-5pm

Logic Tensor Networks, Knowledge Extraction and Applications


Ivan Donadello (FBK, Trento)

Adriana Danilakova (City, London)

Luciano Serafini (FBK, Trento)

Edjard Mota (UFAM, Manaus and City, London)

Simon Odense (City, London)

Dan Philps (Mondrian and City, London)

All are welcome!

Artur Garcez

ps. CoCo@NIPS’16 was a great workshop:

Research Seminar by Eric Humphrey from Spotify, NY

News, Seminar.

Who: Dr Eric Humphrey

When: Monday 5th Dec 2016, 16:00-17:00

Where: AG08 College Building, City, University of London

Title: Open-MIC – The Open Music Instrument Classification Challenge

 Abstract: The Open-MIC initiative is a community-driven experiment to benchmarking content-based MIR algorithms in a transparent and sustainable way. Eric will present the inspiration behind Open-MIC, the approach to building an open music dataset, and the engineering effort behind making the project work. Open-MIC is focused on music, but these topics that are widely applicable to general machine learning and signal processing evaluation.

 About the speaker: Eric Humphrey is a Senior Machine Learning Researcher at Spotify, focusing on algorithms and technology related to audio experience. He obtained a BSEE at Syracuse University in 2007, an MSc in Music Engineering Technology (U of Miami) in 2009 and a PhD in Music Technology at NYU in 2015. Eric is also a multi-instrumentalist, has been a visiting lecturer at the University of Miami, worked as an independent contractor roles for several audio technology companies, spent a summer at Google doing really fun things he can’t really talk about, and currently serves as the secretary to the International Society for Music Information Retrieval.


Data Bites seminar by Dirk Nachbar from Google

News, Seminar.

When: Tue, 6th Dec 2016, 1pm

Where: ELG04, City, University of London

Speaker: Dirk Nachbar (Google, marketing analytics)

Title: Analytical Problems in Marketing Science

Abstract: Like in many fields data is growing exponentially in marketing. I will describe four applications from the real world that use a lot of data and require modelling or data driven approaches: loyalty card data, online attribution problem, real time bidding/buying, and card linked marketing. The talk will introduce challenges and potential solutions in marketing and will describe the different players in the marketing ecosystem.

About the speaker: Dirk studied economics in Essex and then specialised in an MSc in Econometrics in Rotterdam. He worked across different sectors: retail, digital, loyalty, fintech. He worked for companies such as dunnhumby, Nielsen and is now Data Scientist at Google, where he measures effectiveness of advertising. In his spare time, he likes to code in Python, R, or Javascript as well using platforms such as Kaggle, Codewars.

Research excellence in Computer Vision


Muhammad Asad and S.M. Masadur Al-Arif who are pursuing PhD degrees in the Computer Vision Group within the Research Centre for Machine Learning have received Best Paper Awards for research presented in two leading conferences this year.

In June 2016, Muhammad received the Best Paper Award at the 2nd Workshop on Observing and Understanding Hands in Action, part of the 29th IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, USA, for the paper “Learning Marginalization through Regression for Hand Orientation Inference”, co-authored by Dr Gregory Slabaugh.

The paper presented a novel machine learning model that enables a computer to understand and describe the orientation of a human hand by looking at colour images of it.

In September 2016, Arif received the best paper award at the Fourth MICCAI Workshop on Computational Methods and Clinical Applications for Spine Imaging, part of the 19th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Athens, Greece, for the paper “Global Localization and Orientation of the Cervical Spine in X-ray Imaging”, co-authored by M. Gundry, K. Knapp, and G. Slabaugh.

The paper presented a machine learning framework capable of teaching the computer to locate the spinal column in X-ray images. It learns what the spinal column looks like from expert annotated example X-ray images and is able to locate the spine on unseen images.

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