Research Posters

Research Posters

Below are the research posters that were preseneted at the Annual CDT meeting in November 2018.

  • Ada Alevzaki – Localisation and policy synthesis for underwater swarming autonomous vehicles with probabilistic guarantees about safe exploration and reachability requirements – PDF
  • Yuki Asano – Deep Image Prior for Image Segmentation – PDF
  • Mark Finean – Motion Planning To Smoothly Intercept Moving Objects – PDF
  • Siddhant Gangapurwala – Generative Adversarial Imitation Learning for Quadrupedal Locomotion using Unstructured Expert Demonstrations – PDF
  • Chia-Man Hung – Robot Learning for Autonomous Assembly – PDF
  • Florian Jaeckle & Alasdair Paren – Binary Neural Networks – PDF
  • Henry Kenlay – Geometric deep learning for business classification – PDF
  • Hala Lamdouar – Unsupervised Alignment Network – PDF
  • Benjamin Moseley – Bayesian Optimisation for Variational Quantum Eigensolvers – PDF
  • Robert McCraith – Unsupervised Learning of Vehicle Motion using Image Sequences – PDF
  • Tim Rudner – VIREL: A Variational Inference Framework for Reinforcement Learning – PDF
  • Lewis Smith – Online Learning for Faster Verification of Neural Nets: Speeding up integer programs with online convex optimisation – PDF

Below are the research posters that were preseneted at the Annual CDT meeting in October 2017.

  • Triantafyllos Afouras – Deep Learning for Lip Reading
  • Oliver Bent – Deploying Novel Exploration Techniques (NETs) for Malaria Policy Interventions – PDF
  • Fabian Fuchs – Addressing Drift and Overfitting in Deep Visual Odometry – PDF
  • Adam Golinski – Towards Inference Amortization for BUGS Models: BUGS to Anglican Compilation – PDF
  • Bradley Gram-Hansen – Hamiltonian Monte Carlo for Non-Differentiable Points in Probabilistic Programmng Languages
  • Xu Ji – DistinctiveNet: Self-supervised Objectness Losses for Detection – PDF
  • Shuyu Lin – Gaussian Process Based Spatial Inference of Environmental Properties with Noisy Location Data – PDF
  • Andrea Patane – Closed-loop Quantitative Verification of Rate-adaptive Pacemakers – PDF
  • Sasha Salter – Learning from Limited Demonstrations in High Dimensional Feature Spaces – PDF
  • Ed Wagstaff – Inverse Reinforcement Learning for Robotic Arm Control – PDF