284 citations · 535 across the 21 of their papers we have counts for
5 papers · 1 filter
LLMs are not (consistently) Bayesian: Quantifying internal (in)consistencies of LLMs' probabilistic beliefs
Chacha Chen, Matthew Jörke, Adam Goliński +4
Modern AI systems are being deployed in complex domains such as medicine, science, and law, where it is important that they not only produce correct answers, but also represent and…
Beyond Sensor Data: Foundation Models of Behavioral Data from Wearables Improve Health Predictions
Eray Erturk, Fahad Kamran, Salar Abbaspourazad +6
Wearable devices record physiological and behavioral signals that can improve health predictions. While foundation models are increasingly used for such predictions, they have been…
Adaptively Truncating Backpropagation Through Time to Control Gradient Bias
Christopher Aicher, Nicholas J. Foti, Emily B. Fox
Truncated backpropagation through time (TBPTT) is a popular method for learning in recurrent neural networks (RNNs) that saves computation and memory at the cost of bias by truncat…
Disentangled VAE Representations for Multi-Aspect and Missing Data
Samuel K. Ainsworth, Nicholas J. Foti, Emily B. Fox
Many problems in machine learning and related application areas are fundamentally variants of conditional modeling and sampling across multi-aspect data, either multi-view, multi-m…
Interpretable VAEs for nonlinear group factor analysis
Samuel Ainsworth, Nicholas Foti, Adrian KC Lee +1
Deep generative models have recently yielded encouraging results in producing subjectively realistic samples of complex data. Far less attention has been paid to making these gener…