Deep Learning

La-MAML: Look-Ahead Meta-Learning for Continual Learning

In this work we develop a gradient-based meta-learning algorithm for efficient, online continual learning, that is robust and scalable to real-world visual benchmarks.

Probabilistic Object Detection: Strenghts, Weaknesses, and Opportunities

This work surveys recent work in the very nascent field of probabilistic detection and pesents insights and promising avenues for future research in this area.

Unifying Variational Inference and PAC-Bayes for Supervised Learning that Scales

This work proposes to train models with imitation learning in such that they come with a PAC-bayes bound as a performance guarantee for the model. It connects the expressions for the PAC bayes bound and the ELBO of a stochastic predictive policy learnt through likelihood maximisation. The model is then trained by minimising the pac-bayes bound as the objective that also doubles as an error bound.

Autonomous Driving using IRL

Learning from Demonstrations with Inverse Reinforcement in CARLA

Training diverse ensembles for OOD object detection

Exploring Stein Variational Inference for ensemble training

Viewpoint Invariant Junction Recognition using Deep Network Ensembles

This work introduces and formalises the problem of recognising intersections from drastically different viewpoints to enable place recognition for SLAM using a siamese convolutional recurrent architecture trained to classify pairs of short video streams.

Geometric Consistency for Self-Supervised End-to-End Visual Odometry

CTCNet proposed using the compositional property of transformations to self-supervise learning of visual odometry from images.