140 citations · 227 across the 5 of their papers we have counts for
5 papers
SAT: Dynamic Spatial Aptitude Training for Multimodal Language Models
Arijit Ray, Jiafei Duan, Ellis Brown +9
Reasoning about motion and space is a fundamental cognitive capability that is required by multiple real-world applications. While many studies highlight that large multimodal lang…
Human Evaluation of Text-to-Image Models on a Multi-Task Benchmark
Vitali Petsiuk, Alexander E. Siemenn, Saisamrit Surbehera +11
We provide a new multi-task benchmark for evaluating text-to-image models. We perform a human evaluation comparing the most common open-source (Stable Diffusion) and commercial (DA…
Improved Techniques for Quantizing Deep Networks with Adaptive Bit-Widths
Ximeng Sun, Rameswar Panda, Chun-Fu Chen +6
Quantizing deep networks with adaptive bit-widths is a promising technique for efficient inference across many devices and resource constraints. In contrast to static methods that…
AdaShare: Learning What To Share For Efficient Deep Multi-Task Learning
Ximeng Sun, Rameswar Panda, Rogerio Feris +1
Multi-task learning is an open and challenging problem in computer vision. The typical way of conducting multi-task learning with deep neural networks is either through handcrafted…
Learning Multi-Level Hierarchies with Hindsight
Andrew Levy, George Konidaris, Robert Platt +1
Hierarchical agents have the potential to solve sequential decision making tasks with greater sample efficiency than their non-hierarchical counterparts because hierarchical agents…