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...
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...
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...
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...
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...
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...
In this talk, I will first introduce our recent work on the Deep
Equilibrium Model (DEQ). Instead of stacking nonlinear layers, as
is common in deep learning, this approach finds the equilibrium
point of the repeated iteration of a single non...
Despite the success of deep learning, much of its success has
existed in settings where the goal is to learn one, single-purpose
function from data. However, in many contexts, we hope to optimize
neural networks for multiple, distinct tasks (i.e...
Physical processes in the world often have a modular structure,
with complexity emerging through combinations of simpler
subsystems. Machine learning seeks to uncover and use regularities
in the physical world. Although these regularities manifest...
Standard machine learning produces models that are highly accurate
on average but that degrade dramatically when the test distribution
deviates from the training distribution. While one can train robust
models, this often comes at the expense of...
There are several broad insights we can draw from computational
models of human cognition in order to build more human-like forms
of machine learning. (1) The brain has a great deal of built-in
structure, yet still tremendous need and potential for...
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...
