5 papers · 1 filter
RubiConv -- Efficient Boundary-Respecting Convolutions
Linda Friso, Annie Marsden, Xinyi Chen +4
Convolutional architectures have emerged as powerful alternatives to Transformers for sequence modeling. The primary advantage is that they offer improved theoretical sequence leng…
FutureFill: Fast Generation from Convolutional Sequence Models
Naman Agarwal, Xinyi Chen, Evan Dogariu +6
We address the challenge of efficient auto-regressive generation in sequence prediction models by introducing FutureFill, a general-purpose fast generation method for any sequence…
Provable Length Generalization in Sequence Prediction via Spectral Filtering
Annie Marsden, Evan Dogariu, Naman Agarwal +3
We consider the problem of length generalization in sequence prediction. We define a new metric of performance in this setting -- the Asymmetric-Regret -- which measures regret aga…
Adaptive Online Learning of Quantum States
Xinyi Chen, Elad Hazan, Tongyang Li +3
The problem of efficient quantum state learning, also called shadow tomography, aims to comprehend an unknown -dimensional quantum state through POVMs. Yet, these states are rar…
Spectral State Space Models
Naman Agarwal, Daniel Suo, Xinyi Chen +1
This paper studies sequence modeling for prediction tasks with long range dependencies. We propose a new formulation for state space models (SSMs) based on learning linear dynamica…