Search Results for author: Lihua Chen

Found 6 papers, 0 papers with code

LCV2: An Efficient Pretraining-Free Framework for Grounded Visual Question Answering

no code implementations29 Jan 2024 Yuhan Chen, Lumei Su, Lihua Chen, Zhiwei Lin

Experimental implementations were conducted under constrained computational and memory resources, evaluating the proposed method's performance on benchmark datasets including GQA, CLEVR, and VizWiz-VQA-Grounding.

Language Modelling Large Language Model +5

Time Lag Aware Sequential Recommendation

no code implementations9 Aug 2022 Lihua Chen, Ning Yang, Philip S Yu

First, the existing methods often lack the simultaneous consideration of the global stability and local fluctuation of user preference, which might degrade the learning of a user's current preference.

Sequential Recommendation

Polymers for Extreme Conditions Designed Using Syntax-Directed Variational Autoencoders

no code implementations4 Nov 2020 Rohit Batra, Hanjun Dai, Tran Doan Huan, Lihua Chen, Chiho Kim, Will R. Gutekunst, Le Song, Rampi Ramprasad

The design/discovery of new materials is highly non-trivial owing to the near-infinite possibilities of material candidates, and multiple required property/performance objectives.

GPR

Polymer Informatics: Current Status and Critical Next Steps

no code implementations1 Nov 2020 Lihua Chen, Ghanshyam Pilania, Rohit Batra, Tran Doan Huan, Chiho Kim, Christopher Kuenneth, Rampi Ramprasad

Artificial intelligence (AI) based approaches are beginning to impact several domains of human life, science and technology.

Property Prediction

Polymer Informatics with Multi-Task Learning

no code implementations28 Oct 2020 Christopher Künneth, Arunkumar Chitteth Rajan, Huan Tran, Lihua Chen, Chiho Kim, Rampi Ramprasad

Compared to conventional single-task learning models (that are trained on individual property datasets independently), the multi-task approach is accurate, efficient, scalable, and amenable to transfer learning as more data on the same or different properties become available.

Multi-Task Learning

SANTIS: Sampling-Augmented Neural neTwork with Incoherent Structure for MR image reconstruction

no code implementations8 Dec 2018 Fang Liu, Lihua Chen, Richard Kijowski, Li Feng

The undersampled images are generated by a fixed undersampling pattern in the training, and the trained network is then applied to reconstruct new images acquired with the same pattern in the inference.

Image Reconstruction Open-Ended Question Answering

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