3.3k citations · 6.1k across the 51 of their papers we have counts for
8 papers · 1 filter
Pre-trained Language Models Do Not Help Auto-regressive Text-to-Image Generation
Yuhui Zhang, Brandon McKinzie, Zhe Gan +2
Recent advances in image tokenizers, such as VQ-VAE, have enabled text-to-image generation using auto-regressive methods, similar to language modeling. However, these methods have…
Large Language Models as Generalizable Policies for Embodied Tasks
Andrew Szot, Max Schwarzer, Harsh Agrawal +6
We show that large language models (LLMs) can be adapted to be generalizable policies for embodied visual tasks. Our approach, called Large LAnguage model Reinforcement Learning Po…
Mobile V-MoEs: Scaling Down Vision Transformers via Sparse Mixture-of-Experts
Erik Daxberger, Floris Weers, Bowen Zhang +7
Sparse Mixture-of-Experts models (MoEs) have recently gained popularity due to their ability to decouple model size from inference efficiency by only activating a small subset of t…
Data Filtering Networks
Alex Fang, Albin Madappally Jose, Amit Jain +3
Large training sets have become a cornerstone of machine learning and are the foundation for recent advances in language modeling and multimodal learning. While data curation for p…
Value function estimation using conditional diffusion models for control
Bogdan Mazoure, Walter Talbott, Miguel Angel Bautista +3
A fairly reliable trend in deep reinforcement learning is that the performance scales with the number of parameters, provided a complimentary scaling in amount of training data. As…
Principles and Guidelines for Evaluating Social Robot Navigation Algorithms
Anthony Francis, Claudia Pérez-D'Arpino, Chengshu Li +28
A major challenge to deploying robots widely is navigation in human-populated environments, commonly referred to as social robot navigation. While the field of social navigation ha…
On Robustness in Multimodal Learning
Brandon McKinzie, Joseph Cheng, Vaishaal Shankar +3
Multimodal learning is defined as learning over multiple heterogeneous input modalities such as video, audio, and text. In this work, we are concerned with understanding how models…
STAIR: Learning Sparse Text and Image Representation in Grounded Tokens
Chen Chen, Bowen Zhang, Liangliang Cao +7
Image and text retrieval is one of the foundational tasks in the vision and language domain with multiple real-world applications. State-of-the-art approaches, e.g. CLIP, ALIGN, re…