Previous Conferences & Workshops

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-linear...

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...

Through interactions with engineers and computer scientists over the years, including some current visitors at IAS, I have become pretty sold on the idea that machine learning is rich in questions which are interesting to pure mathematicians and...

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...

Organizers:

The workshop was organized by Sanjeev Arora (IAS/Princeton University), Roger Grosse (IAS/Univ Toronto), Sham Kakade (Univ Washington), Bin Yu (IAS/UC Berkeley).

We sought to bring together a diverse group of researchers, focusing...