While the trend in machine learning has tended towards more
complex hypothesis spaces, it is not clear that this extra
complexity is always necessary or helpful for many domains. In
particular, models and their predictions are often made easier
to...
Implicit generative models such as GANs have achieved remarkable
progress at generating convincing fake images, but how well do they
really match the distribution? Log-likelihood has been used
extensively to evaluate generative models whenever it’s...
In this talk, I would like to share some of my reflections on
the progress made in the field of interpretable machine learning.
We will reflect on where we are going as a field, and what are the
things that we need to be aware of to make progress...
Existing generative models are typically based on explicit
representations of probability distributions (e.g., autoregressive
or VAEs) or implicit sampling procedures (e.g., GANs). We propose
an alternative approach based on modeling directly the...
Genomics has revolutionized biology, enabling the interrogation
of whole transcriptomes, genome-wide binding sites for proteins,
and many other molecular processes. However, individual genomic
assays measure elements that interact in vivo as...
Few-shot classification, the task of adapting a classifier to
unseen classes given a small labeled dataset, is an important step
on the path toward human-like machine learning. I will present some
of the key advances in this area, and will then...
Deep learning has led to rapid progress being made in the field
of machine learning and artificial intelligence, leading to
dramatically improved solutions of many challenging problems such
as image understanding, speech recognition, and control...
Meta-learning (or learning to learn) studies how to use machine
learning to design machine learning methods themselves. We consider
an optimization-based formulation of meta-learning that learns to
design an optimization algorithm automatically...
A continuing mystery in understanding the empirical success of
deep neural networks has been in their ability to achieve zero
training error and yet generalize well, even when the training data
is noisy and there are many more parameters than data...
As deep learning systems become more prevalent in real-world
applications it is essential to allow users to exert more control
over the system. Exerting some structure over the learned
representations enables users to manipulate, interpret, and
even...
