4 papers
A General Framework for Inference-time Scaling and Steering of Diffusion Models
Raghav Singhal, Zachary Horvitz, Ryan Teehan +4
Diffusion models produce impressive results in modalities ranging from images and video to protein design and text. However, generating samples with user-specified properties remai…
Are LLMs Prescient? A Continuous Evaluation using Daily News as the Oracle
Hui Dai, Ryan Teehan, Mengye Ren
Many existing evaluation benchmarks for Large Language Models (LLMs) quickly become outdated due to the emergence of new models and training data. These benchmarks also fall short…
PooDLe: Pooled and dense self-supervised learning from naturalistic videos
Alex N. Wang, Christopher Hoang, Yuwen Xiong +2
Self-supervised learning has driven significant progress in learning from single-subject, iconic images. However, there are still unanswered questions about the use of minimally-cu…
Reawakening knowledge: Anticipatory recovery from catastrophic interference via structured training
Yanlai Yang, Matt Jones, Michael C. Mozer +1
We explore the training dynamics of neural networks in a structured non-IID setting where documents are presented cyclically in a fixed, repeated sequence. Typically, networks suff…