Search Results for author: Tianqi Zhang

Found 7 papers, 3 papers with code

The Earth is Flat because...: Investigating LLMs' Belief towards Misinformation via Persuasive Conversation

no code implementations14 Dec 2023 Rongwu Xu, Brian S. Lin, Shujian Yang, Tianqi Zhang, Weiyan Shi, Tianwei Zhang, Zhixuan Fang, Wei Xu, Han Qiu

Therefore, in this study, we delve into LLMs' susceptibility to persuasive conversations, particularly on factual questions that they can answer correctly.

Misinformation

SpecHD: Hyperdimensional Computing Framework for FPGA-based Mass Spectrometry Clustering

no code implementations20 Nov 2023 Sumukh Pinge, Weihong Xu, Jaeyoung Kang, Tianqi Zhang, Neima Moshiri, Wout Bittremieux, Tajana Rosing

This approach markedly improves clustering speed and efficiency, serving as a catalyst for real-time, high-throughput data analysis in future healthcare applications.

Clustering

Spectral Efficiency Analysis of Uplink-Downlink Decoupled Access in C-V2X Networks

1 code implementation5 Dec 2022 Luofang Jiao, Kai Yu, Yunting Xu, Tianqi Zhang, Haibo Zhou, Xuemin, Shen

The uplink (UL)/downlink (DL) decoupled access has been emerging as a novel access architecture to improve the performance gains in cellular networks.

Spectral Efficiency Analysis of Uplink-Downlink Decoupled Access in C-V2X Networks

VL-CheckList: Evaluating Pre-trained Vision-Language Models with Objects, Attributes and Relations

1 code implementation1 Jul 2022 Tiancheng Zhao, Tianqi Zhang, Mingwei Zhu, Haozhan Shen, Kyusong Lee, Xiaopeng Lu, Jianwei Yin

Inspired by the CheckList for testing natural language processing, we exploit VL-CheckList, a novel framework to understand the capabilities of VLP models.

Combinatorial Learning of Graph Edit Distance via Dynamic Embedding

no code implementations CVPR 2021 Runzhong Wang, Tianqi Zhang, Tianshu Yu, Junchi Yan, Xiaokang Yang

This paper presents a hybrid approach by combing the interpretability of traditional search-based techniques for producing the edit path, as well as the efficiency and adaptivity of deep embedding models to achieve a cost-effective GED solver.

Sub-graph Contrast for Scalable Self-Supervised Graph Representation Learning

3 code implementations22 Sep 2020 Yizhu Jiao, Yun Xiong, Jiawei Zhang, Yao Zhang, Tianqi Zhang, Yangyong Zhu

Instead of learning on the complete input graph data, with a novel data augmentation strategy, \textsc{Subg-Con} learns node representations through a contrastive loss defined based on subgraphs sampled from the original graph instead.

Data Augmentation Graph Representation Learning +2

Option Pricing Under a Discrete-Time Markov Switching Stochastic Volatility with Co-Jump Model

no code implementations26 Jun 2020 Michael C. Fu, Bingqing Li, Rongwen Wu, Tianqi Zhang

We consider option pricing using a discrete-time Markov switching stochastic volatility with co-jump model, which can model volatility clustering and varying mean-reversion speeds of volatility.

Clustering

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