142 citations · 512 across the 30 of their papers we have counts for
4 papers · 1 filter
When Worse is Better: Navigating the compression-generation tradeoff in visual tokenization
Vivek Ramanujan, Kushal Tirumala, Armen Aghajanyan +2
Current image generation methods are based on a two-stage training approach. In stage 1, an auto-encoder is trained to compress an image into a latent space; in stage 2, a generati…
MoMa: Efficient Early-Fusion Pre-training with Mixture of Modality-Aware Experts
Xi Victoria Lin, Akshat Shrivastava, Liang Luo +5
We introduce MoMa, a novel modality-aware mixture-of-experts (MoE) architecture designed for pre-training mixed-modal, early-fusion language models. MoMa processes images and text…
Small Molecule Optimization with Large Language Models
Philipp Guevorguian, Menua Bedrosian, Tigran Fahradyan +3
Molecular optimization, the process of designing molecules with desirable properties, represents a critical challenge in drug discovery. The recent advancements in large language m…
Text Quality-Based Pruning for Efficient Training of Language Models
Vasu Sharma, Karthik Padthe, Newsha Ardalani +8
In recent times training Language Models (LMs) have relied on computationally heavy training over massive datasets which makes this training process extremely laborious. In this pa…