Search Results for author: Albert Y. S. Lam

Found 14 papers, 5 papers with code

Revisit Few-shot Intent Classification with PLMs: Direct Fine-tuning vs. Continual Pre-training

1 code implementation8 Jun 2023 Haode Zhang, Haowen Liang, LiMing Zhan, Xiao-Ming Wu, Albert Y. S. Lam

We consider the task of few-shot intent detection, which involves training a deep learning model to classify utterances based on their underlying intents using only a small amount of labeled data.

intent-classification Intent Classification +2

New Intent Discovery with Pre-training and Contrastive Learning

1 code implementation ACL 2022 Yuwei Zhang, Haode Zhang, Li-Ming Zhan, Xiao-Ming Wu, Albert Y. S. Lam

Existing approaches typically rely on a large amount of labeled utterances and employ pseudo-labeling methods for representation learning and clustering, which are label-intensive, inefficient, and inaccurate.

Clustering Contrastive Learning +3

Out-of-Scope Intent Detection with Self-Supervision and Discriminative Training

no code implementations ACL 2021 Li-Ming Zhan, Haowen Liang, Bo Liu, Lu Fan, Xiao-Ming Wu, Albert Y. S. Lam

Since the distribution of outlier utterances is arbitrary and unknown in the training stage, existing methods commonly rely on strong assumptions on data distribution such as mixture of Gaussians to make inference, resulting in either complex multi-step training procedures or hand-crafted rules such as confidence threshold selection for outlier detection.

Intent Detection Outlier Detection +1

Reconstructing Capsule Networks for Zero-shot Intent Classification

1 code implementation IJCNLP 2019 Han Liu, Xiaotong Zhang, Lu Fan, Xu Fu, i, Qimai Li, Xiao-Ming Wu, Albert Y. S. Lam

With the burgeoning of conversational AI, existing systems are not capable of handling numerous fast-emerging intents, which motivates zero-shot intent classification.

Classification General Classification +3

Adaptive Chemical Reaction Optimization for Global Numerical Optimization

no code implementations9 Jul 2015 James J. Q. Yu, Albert Y. S. Lam, Victor O. K. Li

A newly proposed chemical-reaction-inspired metaheurisic, Chemical Reaction Optimization (CRO), has been applied to many optimization problems in both discrete and continuous domains.

Real-Coded Chemical Reaction Optimization with Different Perturbation Functions

no code implementations1 Feb 2015 James J. Q. Yu, Albert Y. S. Lam, Victor O. K. Li

The distributions are tested by a set of well-known benchmark functions and simulation results show that problems with different characteristics have different preference on the distribution function.

Optimal V2G Scheduling of Electric Vehicles and Unit Commitment using Chemical Reaction Optimization

no code implementations1 Feb 2015 James J. Q. Yu, Victor O. K. Li, Albert Y. S. Lam

An electric vehicle (EV) may be used as energy storage which allows the bi-directional electricity flow between the vehicle's battery and the electric power grid.

Scheduling

Chemical Reaction Optimization for the Set Covering Problem

no code implementations1 Feb 2015 James J. Q. Yu, Albert Y. S. Lam, Victor O. K. Li

The set covering problem (SCP) is one of the representative combinatorial optimization problems, having many practical applications.

Combinatorial Optimization

Sensor Deployment for Air Pollution Monitoring Using Public Transportation System

no code implementations1 Feb 2015 James J. Q. Yu, Victor O. K. Li, Albert Y. S. Lam

Air pollution monitoring is a very popular research topic and many monitoring systems have been developed.

An Inter-molecular Adaptive Collision Scheme for Chemical Reaction Optimization

no code implementations1 Feb 2015 James J. Q. Yu, Victor O. K. Li, Albert Y. S. Lam

However, the functionality of the inter-molecular ineffective collision operator in the canonical CRO design overlaps that of the on-wall ineffective collision operator, which can potential impair the overall performance.

Evolutionary Algorithms

Evolutionary Artificial Neural Network Based on Chemical Reaction Optimization

no code implementations1 Feb 2015 James J. Q. Yu, Albert Y. S. Lam, Victor O. K. Li

Evolutionary algorithms (EAs) are very popular tools to design and evolve artificial neural networks (ANNs), especially to train them.

Evolutionary Algorithms

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