activity
20152025
most citedLearning to Reweight Examples for Robust Deep Learning

841 citations · 2.6k across the 48 of their papers we have counts for

collaborators
Showing 2024Show all

7 papers · 1 filter

cs.CL2024

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Many existing evaluation benchmarks for Large Language Models (LLMs) quickly become outdated due to the emergence of new models and training data.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…

cs.CV2024

ProCreate, Don't Reproduce! Propulsive Energy Diffusion for Creative Generation

Jack Lu, Ryan Teehan, Mengye Ren

In this paper, we propose ProCreate, a simple and easy-to-implement method to improve sample diversity and creativity of diffusion-based image generative models and to prevent trai…

cs.CV2024

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…

cs.LG2024

Integrating Present and Past in Unsupervised Continual Learning

Yipeng Zhang, Laurent Charlin, Richard Zemel +1

We formulate a unifying framework for unsupervised continual learning (UCL), which disentangles learning objectives that are specific to the present and the past data, encompassing…

cs.CL2024★ 1 cited

CoLLEGe: Concept Embedding Generation for Large Language Models

Ryan Teehan, Brenden Lake, Mengye Ren

Current language models are unable to quickly learn new concepts on the fly, often requiring a more involved finetuning process to learn robustly. Prompting in-context is not robus…

cs.LG2024

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…

cs.CV2024★ 1 cited

Self-supervised learning of video representations from a child's perspective

A. Emin Orhan, Wentao Wang, Alex N. Wang +2

Children learn powerful internal models of the world around them from a few years of egocentric visual experience. Can such internal models be learned from a child's visual experie…