27 citations · 93 across the 10 of their papers we have counts for
10 papers
Neural Functional Transformers
Allan Zhou, Kaien Yang, Yiding Jiang +5
The recent success of neural networks as implicit representation of data has driven growing interest in neural functionals: models that can process other neural networks as input b…
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…
Noisy Feature Mixup
Soon Hoe Lim, N. Benjamin Erichson, Francisco Utrera +2
We introduce Noisy Feature Mixup (NFM), an inexpensive yet effective method for data augmentation that combines the best of interpolation based training and noise injection schemes…
Prioritized training on points that are learnable, worth learning, and not yet learned (workshop version)
Sören Mindermann, Muhammed Razzak, Winnie Xu +7
We introduce Goldilocks Selection, a technique for faster model training which selects a sequence of training points that are "just right". We propose an information-theoretic acqu…
Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations
Winnie Xu, Ricky T. Q. Chen, Xuechen Li +1
We perform scalable approximate inference in continuous-depth Bayesian neural networks. In this model class, uncertainty about separate weights in each layer gives hidden units tha…