most citedOnline Learning in Contextual Bandits using Gated Linear Networks

8 citations · 23 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2022★ 7 cited

Exploring Vision Transformers as Diffusion Learners

He Cao, Jianan Wang, Tianhe Ren +4

Score-based diffusion models have captured widespread attention and funded fast progress of recent vision generative tasks. In this paper, we focus on diffusion model backbone whic…

cs.CV2022★ 4 cited

ManiTrans: Entity-Level Text-Guided Image Manipulation via Token-wise Semantic Alignment and Generation

Jianan Wang, Guansong Lu, Hang Xu +3

Existing text-guided image manipulation methods aim to modify the appearance of the image or to edit a few objects in a virtual or simple scenario, which is far from practical appl…

cs.CV2020

Data-efficient Alignment of Multimodal Sequences by Aligning Gradient Updates and Internal Feature Distributions

Jianan Wang, Boyang Li, Xiangyu Fan +2

The task of video and text sequence alignment is a prerequisite step toward joint understanding of movie videos and screenplays. However, supervised methods face the obstacle of li…

cs.LG2020★ 4 cited

A Combinatorial Perspective on Transfer Learning

Jianan Wang, Eren Sezener, David Budden +2

Human intelligence is characterized not only by the capacity to learn complex skills, but the ability to rapidly adapt and acquire new skills within an ever-changing environment. I…

cs.LG2020★ 8 cited

Online Learning in Contextual Bandits using Gated Linear Networks

Eren Sezener, Marcus Hutter, David Budden +2

We introduce a new and completely online contextual bandit algorithm called Gated Linear Contextual Bandits (GLCB). This algorithm is based on Gated Linear Networks (GLNs), a recen…