Search Results for author: Tianwei Xing

Found 6 papers, 0 papers with code

Learning to Compress Prompt in Natural Language Formats

no code implementations28 Feb 2024 Yu-Neng Chuang, Tianwei Xing, Chia-Yuan Chang, Zirui Liu, Xun Chen, Xia Hu

In this work, we propose a Natural Language Prompt Encapsulation (Nano-Capsulator) framework compressing original prompts into NL formatted Capsule Prompt while maintaining the prompt utility and transferability.

Using DeepProbLog to perform Complex Event Processing on an Audio Stream

no code implementations15 Oct 2021 Marc Roig Vilamala, Tianwei Xing, Harrison Taylor, Luis Garcia, Mani Srivastava, Lance Kaplan, Alun Preece, Angelika Kimmig, Federico Cerutti

We also demonstrate that our approach is capable of training even with a dataset that has a moderate proportion of noisy data.

An Experimentation Platform for Explainable Coalition Situational Understanding

no code implementations27 Oct 2020 Katie Barrett-Powell, Jack Furby, Liam Hiley, Marc Roig Vilamala, Harrison Taylor, Federico Cerutti, Alun Preece, Tianwei Xing, Luis Garcia, Mani Srivastava, Dave Braines

We present an experimentation platform for coalition situational understanding research that highlights capabilities in explainable artificial intelligence/machine learning (AI/ML) and integration of symbolic and subsymbolic AI/ML approaches for event processing.

BIG-bench Machine Learning Explainable artificial intelligence

A Hybrid Neuro-Symbolic Approach for Complex Event Processing

no code implementations7 Sep 2020 Marc Roig Vilamala, Harrison Taylor, Tianwei Xing, Luis Garcia, Mani Srivastava, Lance Kaplan, Alun Preece, Angelika Kimmig, Federico Cerutti

We demonstrate this comparing our approach against a pure neural network approach on a dataset based on Urban Sounds 8K.

8k

Binarized Convolutional Neural Networks with Separable Filters for Efficient Hardware Acceleration

no code implementations15 Jul 2017 Jeng-Hau Lin, Tianwei Xing, Ritchie Zhao, Zhiru Zhang, Mani Srivastava, Zhuowen Tu, Rajesh K. Gupta

State-of-the-art convolutional neural networks are enormously costly in both compute and memory, demanding massively parallel GPUs for execution.

Personalized Course Sequence Recommendations

no code implementations30 Dec 2015 Jie Xu, Tianwei Xing, Mihaela van der Schaar

Given the variability in student learning it is becoming increasingly important to tailor courses as well as course sequences to student needs.

Multi-Armed Bandits

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