10 papers
Predict before you train: Scaling Laws for particle physics foundation models
Jan-Lucas Uslu, Benjamin Nachman, Christopher Re
The largest machine learning models in particle physics are also the most expensive to train, yet the return on scaling a given architecture cannot be estimated before that compute…
HMAR: Efficient Hierarchical Masked Auto-Regressive Image Generation
Hermann Kumbong, Xian Liu, Tsung-Yi Lin +6
Visual Auto-Regressive modeling (VAR) has shown promise in bridging the speed and quality gap between autoregressive image models and diffusion models. VAR reformulates autoregress…
Restructuring Vector Quantization with the Rotation Trick
Christopher Fifty, Ronald G. Junkins, Dennis Duan +5
Vector Quantized Variational AutoEncoders (VQ-VAEs) are designed to compress a continuous input to a discrete latent space and reconstruct it with minimal distortion. They operate…
Towards Learning High-Precision Least Squares Algorithms with Sequence Models
Jerry Liu, Jessica Grogan, Owen Dugan +4
This paper investigates whether sequence models can learn to perform numerical algorithms, e.g. gradient descent, on the fundamental problem of least squares. Our goal is to inheri…
LoLCATs: On Low-Rank Linearizing of Large Language Models
Michael Zhang, Simran Arora, Rahul Chalamala +5
Recent works show we can linearize large language models (LLMs) -- swapping the quadratic attentions of popular Transformer-based LLMs with subquadratic analogs, such as linear att…
Systems and Algorithms for Convolutional Multi-Hybrid Language Models at Scale
Jerome Ku, Eric Nguyen, David W. Romero +13
We introduce convolutional multi-hybrid architectures, with a design grounded on two simple observations. First, operators in hybrid models can be tailored to token manipulation ta…