Many challenging problems in modern applications amount to
finding relevant results from an enormous output space of potential
candidates, for example, finding the best matching product from a
large catalog or suggesting related search phrases on a...
Classical algorithms typically provide "one size fits all"
performance, and do not leverage properties or patterns in their
inputs. A recent line of work aims to address this issue by
developing algorithms that use machine learning predictions
to...
Suppose you are monitoring discrete events in real time. Can you
predict what events will happen in the future, and when? Can you
fill in past events that you may have missed? A probability model
that supports such reasoning is the neural Hawkes...
A brief review will be provided first on how deep learning has
disrupted speech recognition and language processing industries
since 2009. Then connections will be drawn between the techniques
(deep learning or otherwise) for modeling speech and...
There are three core orthogonal problems in reinforcement
learning: (1) Crediting actions (2) generalizing across rich
observations (3) Exploring to discover the information necessary
for learning. Good solutions to pairs of these problems are...
Probably Approximately Correct (PAC) learning has attempted to
analyse the generalisation of learning systems within the
statistical learning framework. It has been referred to as a ‘worst
case’ analysis, but the tools have been extended to analyse...
In handling wide range of experiences ranging from data
instances, knowledge, constraints, to rewards, adversaries, and
lifelong interplay in an ever-growing spectrum of tasks,
contemporary ML/AI research has resulted in thousands of
models...
Unsupervised learning, in particular learning general nonlinear
representations, is one of the deepest problems in machine
learning. Estimating latent quantities in a generative model
provides a principled framework, and has been successfully
used...
For autonomous robots to operate in the open, dynamically
changing world, they will need to be able to learn a robust set of
skills from relatively little experience. This talk begins by
introducing Grounded Simulation Learning as a way to bridge...
I will introduce Generalized Energy Based Models (GEBM) for
generative modelling. These models combine two trained components:
a base distribution (generally an implicit model), which can learn
the support of data with low intrinsic dimension in a...
