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...