12 citations · 32 across the 10 of their papers we have counts for
11 papers
ViTok-v2: Scaling Native Resolution Auto-Encoders to 5 Billion Parameters
Philippe Hansen-Estruch, Jiahui Chen, Vivek Ramanujan +9
Vision Transformer (ViT) autoencoders have emerged as compelling tokenizers for images, offering improved reconstruction over convolutional tokenizers. However, existing ViT tokeni…
Unified Text-Image Generation with Weakness-Targeted Post-Training
Jiahui Chen, Philippe Hansen-Estruch, Xiaochuang Han +7
Unified multimodal generation architectures that jointly produce text and images have recently emerged as a promising direction for text-to-image (T2I) synthesis. However, many exi…
Learnings from Scaling Visual Tokenizers for Reconstruction and Generation
Philippe Hansen-Estruch, David Yan, Ching-Yao Chung +7
Visual tokenization via auto-encoding empowers state-of-the-art image and video generative models by compressing pixels into a latent space. Although scaling Transformer-based gene…
Apollo: An Exploration of Video Understanding in Large Multimodal Models
Orr Zohar, Xiaohan Wang, Yann Dubois +9
Despite the rapid integration of video perception capabilities into Large Multimodal Models (LMMs), the underlying mechanisms driving their video understanding remain poorly unders…
D5RL: Diverse Datasets for Data-Driven Deep Reinforcement Learning
Rafael Rafailov, Kyle Hatch, Anikait Singh +9
Offline reinforcement learning algorithms hold the promise of enabling data-driven RL methods that do not require costly or dangerous real-world exploration and benefit from large…
Video Occupancy Models
Manan Tomar, Philippe Hansen-Estruch, Philip Bachman +4
We introduce a new family of video prediction models designed to support downstream control tasks. We call these models Video Occupancy models (VOCs). VOCs operate in a compact lat…
Unified Auto-Encoding with Masked Diffusion
Philippe Hansen-Estruch, Sriram Vishwanath, Amy Zhang +1
At the core of both successful generative and self-supervised representation learning models there is a reconstruction objective that incorporates some form of image corruption. Di…
Goal Representations for Instruction Following: A Semi-Supervised Language Interface to Control
Vivek Myers, Andre He, Kuan Fang +7
Our goal is for robots to follow natural language instructions like "put the towel next to the microwave." But getting large amounts of labeled data, i.e. data that contains demons…
BridgeData V2: A Dataset for Robot Learning at Scale
Homer Walke, Kevin Black, Abraham Lee +11
We introduce BridgeData V2, a large and diverse dataset of robotic manipulation behaviors designed to facilitate research on scalable robot learning. BridgeData V2 contains 60,096…
IDQL: Implicit Q-Learning as an Actor-Critic Method with Diffusion Policies
Philippe Hansen-Estruch, Ilya Kostrikov, Michael Janner +2
Effective offline RL methods require properly handling out-of-distribution actions. Implicit Q-learning (IQL) addresses this by training a Q-function using only dataset actions thr…
Bisimulation Makes Analogies in Goal-Conditioned Reinforcement Learning
Philippe Hansen-Estruch, Amy Zhang, Ashvin Nair +2
Building generalizable goal-conditioned agents from rich observations is a key to reinforcement learning (RL) solving real world problems. Traditionally in goal-conditioned RL, an…