Search Results for author: Jennifer Healey

Found 8 papers, 2 papers with code

Budgeted Online Influence Maximization

no code implementations ICML 2020 Pierre Perrault, Zheng Wen, Michal Valko, Jennifer Healey

We introduce a new budgeted framework for online influence maximization, considering the total cost of an advertising campaign instead of the common cardinality constraint on a chosen influencer set.

valid

Automatic Layout Planning for Visually-Rich Documents with Instruction-Following Models

no code implementations23 Apr 2024 Wanrong Zhu, Jennifer Healey, Ruiyi Zhang, William Yang Wang, Tong Sun

Recent advancements in instruction-following models have made user interactions with models more user-friendly and efficient, broadening their applicability.

Instruction Following

Gaudí: Conversational Interactions with Deep Representations to Generate Image Collections

no code implementations5 Dec 2021 Victor S. Bursztyn, Jennifer Healey, Vishwa Vinay

Based on recent advances in realistic language modeling (GPT-3) and cross-modal representations (CLIP), Gaud\'i was developed to help designers search for inspirational images using natural language.

Language Modelling

Multiscale Manifold Warping

no code implementations19 Sep 2021 Sridhar Mahadevan, Anup Rao, Georgios Theocharous, Jennifer Healey

Many real-world applications require aligning two temporal sequences, including bioinformatics, handwriting recognition, activity recognition, and human-robot coordination.

Activity Recognition Dynamic Time Warping +2

Developing a Conversational Recommendation System for Navigating Limited Options

no code implementations13 Apr 2021 Victor S. Bursztyn, Jennifer Healey, Eunyee Koh, Nedim Lipka, Larry Birnbaum

We have developed a conversational recommendation system designed to help users navigate through a set of limited options to find the best choice.

Navigate

On the Approximation Relationship between Optimizing Ratio of Submodular (RS) and Difference of Submodular (DS) Functions

no code implementations5 Jan 2021 Pierre Perrault, Jennifer Healey, Zheng Wen, Michal Valko

We demonstrate that from an algorithm guaranteeing an approximation factor for the ratio of submodular (RS) optimization problem, we can build another algorithm having a different kind of approximation guarantee -- weaker than the classical one -- for the difference of submodular (DS) optimization problem, and vice versa.

Data Structures and Algorithms

Cannot find the paper you are looking for? You can Submit a new open access paper.