Recent & Upcoming Talks

In this position paper, the authors define interpretability and describe when interpretability is needed (and when it is not). Next, they suggest a taxonomy for rigorous evaluation and expose open questions towards a more rigorous science of interpretable machine learning.

This research aimed to develop a lifestyle intervention system with an emphasis on time series forecasting and also to develop a generic multimodal activity forecasting scheme that can be used with either early fusion or late fusion mechanisms.

This paper provides an overview of basic and advanced approaches of time-series data augmentation.

This paper proposes GPT-3, an autoregressive language model with 175 billion parameters, 10x more than any previous non-sparse language model, and test its performance in the few-shot setting.

This paper shows that masked autoencoders (MAE) are scalable self-supervised learners for computer vision.

A reinforcement learning approach to solve the vehicle routing problem.

This paper addresses the generalization gap on large neural networks with adaptive gradient-based optimizers and proposes a partially adaptive solution to overcome the problem.

We focus on devising algorithms that combine data about physical activity and engagement with the app to predict future physical activity performance.