119 citations · 255 across the 5 of their papers we have counts for
5 papers · 1 filter
Effectively Modeling Time Series with Simple Discrete State Spaces
Michael Zhang, Khaled K. Saab, Michael Poli +3
Time series modeling is a well-established problem, which often requires that methods (1) expressively represent complicated dependencies, (2) forecast long horizons, and (3) effic…
Hungry Hungry Hippos: Towards Language Modeling with State Space Models
Daniel Y. Fu, Tri Dao, Khaled K. Saab +3
State space models (SSMs) have demonstrated state-of-the-art sequence modeling performance in some modalities, but underperform attention in language modeling. Moreover, despite sc…
Domino: Discovering Systematic Errors with Cross-Modal Embeddings
Sabri Eyuboglu, Maya Varma, Khaled Saab +5
Machine learning models that achieve high overall accuracy often make systematic errors on important subsets (or slices) of data. Identifying underperforming slices is particularly…
Combining Recurrent, Convolutional, and Continuous-time Models with Linear State-Space Layers
Albert Gu, Isys Johnson, Karan Goel +4
Recurrent neural networks (RNNs), temporal convolutions, and neural differential equations (NDEs) are popular families of deep learning models for time-series data, each with uniqu…
Double Descent Optimization Pattern and Aliasing: Caveats of Noisy Labels
Florian Dubost, Erin Hong, Max Pike +5
Optimization plays a key role in the training of deep neural networks. Deciding when to stop training can have a substantial impact on the performance of the network during inferen…