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MPhil in Machine Learning and Machine Intelligence

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MPhil in Machine Learning and Machine Intelligence

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  • Course Highlights
  • Frequently Asked Questions
  • Contact Us

About the Programme

  • About the Programme overview
  • Course Structure
  • Research Project
  • Assessment
  • Meet Former Students
  • Professional Development
  • Academic Staff
  • Preliminary Reading

How to Apply

  • How to Apply overview
  • Making an Application
  • Academic Background
  • Funding
  • Home
  • About the Programme

    About the Programme

    About the Programme overview
    • Course Structure
    • Research Project
    • Assessment
    • Meet Former Students
    • Professional Development
    • Academic Staff
    • Preliminary Reading
  • How to Apply

    How to Apply

    How to Apply overview
    • Making an Application
    • Academic Background
    • Funding
  • Course Highlights
  • Frequently Asked Questions
  • Contact Us
    • Home
    • About the Programme
    • How to Apply
    • Course Highlights
    • Frequently Asked Questions
    • Contact Us

2017 - 2018 Course Highlights

MPhil MLMI Class of 2017-18

Dissertations

Fairness in Machine Learning with Causal Reasoning

Spectral Methods in Gaussian Process Approximations * Poster

Fashion Products Identification Using Bayesian Latent Variable Models

Well-Calibrated Bayesian Neural Networks * Poster

Relation Classification Based on Deep Learning Approach * Poster

Augmenting Natural Language Generation with External Memory Modules in Spoken Dialogue Systems * Poster

Combining Sum Product Networks and Variational Autoencoders * Poster

Curiosity-Driven Reinforcement Learning for Dialogue Management * Poster   Paper at ICASSP 2018 - 2019

Generative Adversarial Networks for Speech Recognition Data Augmentation * Poster

Manifold Hamiltonian Dynamics for Variational Auto-Encoders * Poster

Neural Network Compression * Poster

Automatic Chemical Design with Molecular Graph Variational Autoencoders

3D Human Motion Synthesis with Recurrent Gaussian Processes * Poster

Deeper Understanding of Autophagy and pseudo-Autophagy through Latent-Space Analysis

Contextual Reasoning in Scene Understanding (Poster)

Defending a Speech Recogniser against Adversarial Examples (Poster)

Compressing Neural Networks

Overcoming Catastrophic Forgetting in Neural Machine Translation

 

Posters from the Advanced Machine Learning Module

Dropout as A Variational Approximation to Bayesian Neural Networks

Sequential Neural Models with Stochastic Layers

Auto-Encoding Variational Bayes 

MPhil in Machine Learning and Machine Intelligence

Contact Information

Department of Engineering Trumpington Street Cambridge CB2 1PZ United Kingdom
mlmi-mphil-enquiries@eng.cam.ac.uk

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