Search Results for author: Haozhu Wang

Found 8 papers, 4 papers with code

Latent Skill Discovery for Chain-of-Thought Reasoning

no code implementations7 Dec 2023 Zifan Xu, Haozhu Wang, Dmitriy Bespalov, Peter Stone, Yanjun Qi

Simultaneously, RSD learns a reasoning policy to determine the required reasoning skill for a given question.

Math

A Review of Reinforcement Learning for Natural Language Processing, and Applications in Healthcare

no code implementations23 Oct 2023 Ying Liu, Haozhu Wang, Huixue Zhou, Mingchen Li, Yu Hou, Sicheng Zhou, Fang Wang, Rama Hoetzlein, Rui Zhang

It has gained significant attention in the field of Natural Language Processing (NLP) due to its ability to learn optimal strategies for tasks such as dialogue systems, machine translation, and question-answering.

Decision Making Machine Translation +5

Graph Neural Prompting with Large Language Models

1 code implementation27 Sep 2023 Yijun Tian, Huan Song, Zichen Wang, Haozhu Wang, Ziqing Hu, Fang Wang, Nitesh V. Chawla, Panpan Xu

While existing work has explored utilizing knowledge graphs (KGs) to enhance language modeling via joint training and customized model architectures, applying this to LLMs is problematic owing to their large number of parameters and high computational cost.

Knowledge Graphs Language Modelling +2

OL-Transformer: A Fast and Universal Surrogate Simulator for Optical Multilayer Thin Film Structures

no code implementations19 May 2023 Taigao Ma, Haozhu Wang, L. Jay Guo

Deep learning-based methods have recently been established as fast and accurate surrogate simulators for optical multilayer thin film structures.

OptoGPT: A Foundation Model for Inverse Design in Optical Multilayer Thin Film Structures

no code implementations20 Apr 2023 Taigao Ma, Haozhu Wang, L. Jay Guo

Foundation models are large machine learning models that can tackle various downstream tasks once trained on diverse and large-scale data, leading research trends in natural language processing, computer vision, and reinforcement learning.

Computational Efficiency

LEARNING TO SHARE: SIMULTANEOUS PARAMETER TYING AND SPARSIFICATION IN DEEP LEARNING

1 code implementation ICLR 2018 Dejiao Zhang, Haozhu Wang, Mario Figueiredo, Laura Balzano

This has motivated a large body of work to reduce the complexity of the neural network by using sparsity-inducing regularizers.

Learning Credible Models

1 code implementation8 Nov 2017 Jiaxuan Wang, Jeeheh Oh, Haozhu Wang, Jenna Wiens

In this work, we formally define credibility in the linear setting and focus on techniques for learning models that are both accurate and credible.

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