Search Results for author: Andrew Lim

Found 16 papers, 10 papers with code

Proteus: A Self-Designing Range Filter

2 code implementations30 Jun 2022 Eric R. Knorr, Baptiste Lemaire, Andrew Lim, Siqiang Luo, Huanchen Zhang, Stratos Idreos, Michael Mitzenmacher

We introduce Proteus, a novel self-designing approximate range filter, which configures itself based on sampled data in order to optimize its false positive rate (FPR) for a given space requirement.

Deep Reinforcement Learning for Solving the Heterogeneous Capacitated Vehicle Routing Problem

1 code implementation6 Oct 2021 Jingwen Li, Yining Ma, Ruize Gao, Zhiguang Cao, Andrew Lim, Wen Song, Jie Zhang

To solve those problems, we propose a DRL method based on the attention mechanism with a vehicle selection decoder accounting for the heterogeneous fleet constraint and a node selection decoder accounting for the route construction, which learns to construct a solution by automatically selecting both a vehicle and a node for this vehicle at each step.

reinforcement-learning Reinforcement Learning (RL)

Heterogeneous Attentions for Solving Pickup and Delivery Problem via Deep Reinforcement Learning

no code implementations6 Oct 2021 Jingwen Li, Liang Xin, Zhiguang Cao, Andrew Lim, Wen Song, Jie Zhang

In particular, the heterogeneous attention mechanism specifically prescribes attentions for each role of the nodes while taking into account the precedence constraint, i. e., the pickup node must precede the pairing delivery node.

reinforcement-learning Reinforcement Learning (RL)

PointBA: Towards Backdoor Attacks in 3D Point Cloud

no code implementations ICCV 2021 Xinke Li, Zhirui Chen, Yue Zhao, Zekun Tong, Yabang Zhao, Andrew Lim, Joey Tianyi Zhou

We present the backdoor attacks in 3D point cloud with a unified framework that exploits the unique properties of 3D data and networks.

Backdoor Attack Disentanglement

Digraph Inception Convolutional Networks

1 code implementation NeurIPS 2020 Zekun Tong, Yuxuan Liang, Changsheng Sun, Xinke Li, David Rosenblum, Andrew Lim

Graph Convolutional Networks (GCNs) have shown promising results in modeling graph-structured data.

An Exponential Factorization Machine with Percentage Error Minimization to Retail Sales Forecasting

no code implementations22 Sep 2020 Chongshou Li, Brenda Cheang, Zhixing Luo, Andrew Lim

The EFM model is significantly different from the original Factorization Machines (FM) from two-fold: (1) the attribute-level formulation for explanatory variables and (2) exponential formulation for the positive response variable.

Attribute Feature Engineering

Revisiting Modified Greedy Algorithm for Monotone Submodular Maximization with a Knapsack Constraint

no code implementations12 Aug 2020 Jing Tang, Xueyan Tang, Andrew Lim, Kai Han, Chongshou Li, Junsong Yuan

Second, we enhance the modified greedy algorithm to derive a data-dependent upper bound on the optimum.

Campus3D: A Photogrammetry Point Cloud Benchmark for Hierarchical Understanding of Outdoor Scene

1 code implementation11 Aug 2020 Xinke Li, Chongshou Li, Zekun Tong, Andrew Lim, Junsong Yuan, Yuwei Wu, Jing Tang, Raymond Huang

Based on it, we formulate a hierarchical learning problem for 3D point cloud segmentation and propose a measurement evaluating consistency across various hierarchies.

Instance Segmentation Point Cloud Segmentation +3

Directed Graph Convolutional Network

1 code implementation29 Apr 2020 Zekun Tong, Yuxuan Liang, Changsheng Sun, David S. Rosenblum, Andrew Lim

Graph Convolutional Networks (GCNs) have been widely used due to their outstanding performance in processing graph-structured data.

Efficient Approximation Algorithms for Adaptive Influence Maximization

2 code implementations14 Apr 2020 Keke Huang, Jing Tang, Kai Han, Xiaokui Xiao, Wei Chen, Aixin Sun, Xueyan Tang, Andrew Lim

In this paper, we propose the first practical algorithm for the adaptive IM problem that could provide the worst-case approximation guarantee of $1-\mathrm{e}^{\rho_b(\varepsilon-1)}$, where $\rho_b=1-(1-1/b)^b$ and $\varepsilon \in (0, 1)$ is a user-specified parameter.

Social and Information Networks

On Isometry Robustness of Deep 3D Point Cloud Models under Adversarial Attacks

1 code implementation CVPR 2020 Yue Zhao, Yuwei Wu, Caihua Chen, Andrew Lim

Armed with the Thompson Sampling, we develop a black-box attack with success rate over 95% on ModelNet40 data set.

Thompson Sampling

Learning Variable Ordering Heuristics for Solving Constraint Satisfaction Problems

1 code implementation23 Dec 2019 Wen Song, Zhiguang Cao, Jie Zhang, Andrew Lim

In this paper, we propose a deep reinforcement learning based approach to automatically discover new variable ordering heuristics that are better adapted for a given class of CSP instances.

Learning Improvement Heuristics for Solving Routing Problems

1 code implementation12 Dec 2019 Yaoxin Wu, Wen Song, Zhiguang Cao, Jie Zhang, Andrew Lim

In this paper, we propose a deep reinforcement learning framework to learn the improvement heuristics for routing problems.

Learning Robust Features using Deep Learning for Automatic Seizure Detection

1 code implementation31 Jul 2016 Pierre Thodoroff, Joelle Pineau, Andrew Lim

We present and evaluate the capacity of a deep neural network to learn robust features from EEG to automatically detect seizures.

EEG Seizure Detection

A Tabu Search Algorithm for the Multi-period Inspector Scheduling Problem

no code implementations17 Sep 2014 Hu Qin, Zizhen Zhang, Yubin Xie, Andrew Lim

Therefore, the shortest transit time between any vertex pair is affected by the length of the period and the departure time.


An Enhanced Branch-and-bound Algorithm for the Talent Scheduling Problem

no code implementations23 Jan 2014 Zizhen Zhang, Hu Qin, Xiaocong Liang, Andrew Lim

The talent scheduling problem is a simplified version of the real-world film shooting problem, which aims to determine a shooting sequence so as to minimize the total cost of the actors involved.


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