Search Results for author: Site Li

Found 15 papers, 1 papers with code

Can Large Language Models Understand Context?

no code implementations1 Feb 2024 YIlun Zhu, Joel Ruben Antony Moniz, Shruti Bhargava, Jiarui Lu, Dhivya Piraviperumal, Site Li, Yuan Zhang, Hong Yu, Bo-Hsiang Tseng

Understanding context is key to understanding human language, an ability which Large Language Models (LLMs) have been increasingly seen to demonstrate to an impressive extent.

In-Context Learning Quantization

Ontology Revision based on Pre-trained Language Models

no code implementations27 Oct 2023 Qiu Ji, Guilin Qi, Yuxin Ye, Jiaye Li, Site Li, Jianjie Ren, Songtao Lu

We conduct experiments over 19 ontology pairs and compare our algorithms and scoring functions with existing ones.

An Embedding-based Approach to Inconsistency-tolerant Reasoning with Inconsistent Ontologies

1 code implementation4 Apr 2023 Keyu Wang, Site Li, Jiaye Li, Guilin Qi, Qiu Ji

A natural way to reason with an inconsistent ontology is to utilize the maximal consistent subsets of the ontology.

Management Relation

Adversarial Unsupervised Domain Adaptation with Conditional and Label Shift: Infer, Align and Iterate

no code implementations ICCV 2021 Xiaofeng Liu, Zhenhua Guo, Site Li, Fangxu Xing, Jane You, C. -C. Jay Kuo, Georges El Fakhri, Jonghye Woo

In this work, we propose an adversarial unsupervised domain adaptation (UDA) approach with the inherent conditional and label shifts, in which we aim to align the distributions w. r. t.

Unsupervised Domain Adaptation

MBB: Model-Based Baseline for Global Guidance of Model-Free Reinforcement Learning via Lower-Dimensional Solutions

no code implementations4 Nov 2020 Xubo Lyu, Site Li, Seth Siriya, Ye Pu, Mo Chen

On the other end, "classical methods" such as optimal control generate solutions without collecting data, but assume that an accurate model of the system and environment is known and are mostly limited to problems with low-dimensional (lo-dim) state spaces.

Importance-Aware Semantic Segmentation in Self-Driving with Discrete Wasserstein Training

no code implementations21 Oct 2020 Xiaofeng Liu, Yuzhuo Han, Song Bai, Yi Ge, Tianxing Wang, Xu Han, Site Li, Jane You, Ju Lu

However, the cross entropy loss can not take the different importance of each class in an self-driving system into account.

Segmentation Self-Driving Cars +1

Deep Verifier Networks: Verification of Deep Discriminative Models with Deep Generative Models

no code implementations18 Nov 2019 Tong Che, Xiaofeng Liu, Site Li, Yubin Ge, Ruixiang Zhang, Caiming Xiong, Yoshua Bengio

We test the verifier network on out-of-distribution detection and adversarial example detection problems, as well as anomaly detection problems in structured prediction tasks such as image caption generation.

Anomaly Detection Autonomous Driving +3

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