27 citations · 93 across the 10 of their papers we have counts for
6 papers · 1 filter
Deep Latent State Space Models for Time-Series Generation
Linqi Zhou, Michael Poli, Winnie Xu +2
Methods based on ordinary differential equations (ODEs) are widely used to build generative models of time-series. In addition to high computational overhead due to explicitly comp…
Language Model Cascades
David Dohan, Winnie Xu, Aitor Lewkowycz +9
Prompted models have demonstrated impressive few-shot learning abilities. Repeated interactions at test-time with a single model, or the composition of multiple models together, fu…
Prioritized Training on Points that are Learnable, Worth Learning, and Not Yet Learnt
Sören Mindermann, Jan Brauner, Muhammed Razzak +8
Training on web-scale data can take months. But most computation and time is wasted on redundant and noisy points that are already learnt or not learnable. To accelerate training,…
Multi-Game Decision Transformers
Kuang-Huei Lee, Ofir Nachum, Mengjiao Yang +8
A longstanding goal of the field of AI is a method for learning a highly capable, generalist agent from diverse experience. In the subfields of vision and language, this was largel…
Self-Similarity Priors: Neural Collages as Differentiable Fractal Representations
Michael Poli, Winnie Xu, Stefano Massaroli +3
Many patterns in nature exhibit self-similarity: they can be compactly described via self-referential transformations. Said patterns commonly appear in natural and artificial objec…
NoisyMix: Boosting Model Robustness to Common Corruptions
N. Benjamin Erichson, Soon Hoe Lim, Winnie Xu +3
For many real-world applications, obtaining stable and robust statistical performance is more important than simply achieving state-of-the-art predictive test accuracy, and thus ro…