Search Results for author: Jialin Liu

Found 51 papers, 26 papers with code

Hyperparameter Tuning is All You Need for LISTA

1 code implementation NeurIPS 2021 Xiaohan Chen, Jialin Liu, Zhangyang Wang, Wotao Yin

Learned Iterative Shrinkage-Thresholding Algorithm (LISTA) introduces the concept of unrolling an iterative algorithm and training it like a neural network.

Learned Robust PCA: A Scalable Deep Unfolding Approach for High-Dimensional Outlier Detection

1 code implementation NeurIPS 2021 HanQin Cai, Jialin Liu, Wotao Yin

Robust principal component analysis (RPCA) is a critical tool in modern machine learning, which detects outliers in the task of low-rank matrix reconstruction.

Outlier Detection

Keiki: Towards Realistic Danmaku Generation via Sequential GANs

1 code implementation7 Jul 2021 Ziqi Wang, Jialin Liu, Georgios N. Yannakakis

Search-based procedural content generation methods have recently been introduced for the autonomous creation of bullet hell games.

Time Series

Experience-Driven PCG via Reinforcement Learning: A Super Mario Bros Study

1 code implementation30 Jun 2021 Tianye Shu, Jialin Liu, Georgios N. Yannakakis

In particular, the RL designers of Super Mario Bros generate and concatenate level segments while considering the diversity among the segments.


Learning to Optimize: A Primer and A Benchmark

1 code implementation23 Mar 2021 Tianlong Chen, Xiaohan Chen, Wuyang Chen, Howard Heaton, Jialin Liu, Zhangyang Wang, Wotao Yin

It automates the design of an optimization method based on its performance on a set of training problems.

Learning A Minimax Optimizer: A Pilot Study

no code implementations ICLR 2021 Jiayi Shen, Xiaohan Chen, Howard Heaton, Tianlong Chen, Jialin Liu, Wotao Yin, Zhangyang Wang

We first present Twin L2O, the first dedicated minimax L2O framework consisting of two LSTMs for updating min and max variables, respectively.

Reinforcement Learning with Dual-Observation for General Video Game Playing

1 code implementation11 Nov 2020 Chengpeng Hu, Ziqi Wang, Tianye Shu, Hao Tong, Julian Togelius, Xin Yao, Jialin Liu

Our proposed technique is implemented with three state-of-the-art reinforcement learning algorithms and tested on the game set of the 2020 General Video Game AI Learning Competition.

Decision Making reinforcement-learning

Deep Learning for Procedural Content Generation

no code implementations9 Oct 2020 Jialin Liu, Sam Snodgrass, Ahmed Khalifa, Sebastian Risi, Georgios N. Yannakakis, Julian Togelius

This article surveys the various deep learning methods that have been applied to generate game content directly or indirectly, discusses deep learning methods that could be used for content generation purposes but are rarely used today, and envisages some limitations and potential future directions of deep learning for procedural content generation.

Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval

4 code implementations ICLR 2021 Lee Xiong, Chenyan Xiong, Ye Li, Kwok-Fung Tang, Jialin Liu, Paul Bennett, Junaid Ahmed, Arnold Overwijk

In this paper, we identify that the main bottleneck is in the training mechanisms, where the negative instances used in training are not representative of the irrelevant documents in testing.

Contrastive Learning Passage Retrieval +1

A Hybrid Evolutionary Algorithm for Reliable Facility Location Problem

no code implementations27 Jun 2020 Han Zhang, Jialin Liu, Xin Yao

The reliable facility location problem (RFLP) is an important research topic of operational research and plays a vital role in the decision-making and management of modern supply chain and logistics.

Decision Making

Wavelet Scattering Networks for Atomistic Systems with Extrapolation of Material Properties

no code implementations1 Jun 2020 Paul Sinz, Michael W. Swift, Xavier Brumwell, Jialin Liu, Kwang Jin Kim, Yue Qi, Matthew Hirn

The dream of machine learning in materials science is for a model to learn the underlying physics of an atomic system, allowing it to move beyond interpolation of the training set to the prediction of properties that were not present in the original training data.

feature selection

Towards in-store multi-person tracking using head detection and track heatmaps

1 code implementation16 May 2020 Aibek Musaev, Jiangping Wang, Liang Zhu, Cheng Li, Yi Chen, Jialin Liu, Wanqi Zhang, Juan Mei, De Wang

In addition, we describe an illustrative example of the use of this dataset for tracking participants based on a head tracking model in an effort to minimize errors due to occlusion.

Head Detection

A Novel CNet-assisted Evolutionary Level Repairer and Its Applications to Super Mario Bros

2 code implementations13 May 2020 Tianye Shu, Ziqi Wang, Jialin Liu, Xin Yao

However, defective levels with illegal patterns may be generated due to the violation of constraints for level design.

Versatile Black-Box Optimization

no code implementations29 Apr 2020 Jialin Liu, Antoine Moreau, Mike Preuss, Baptiste Roziere, Jeremy Rapin, Fabien Teytaud, Olivier Teytaud

Choosing automatically the right algorithm using problem descriptors is a classical component of combinatorial optimization.

Combinatorial Optimization

Interactive Evolution and Exploration Within Latent Level-Design Space of Generative Adversarial Networks

1 code implementation31 Mar 2020 Jacob Schrum, Jake Gutierrez, Vanessa Volz, Jialin Liu, Simon Lucas, Sebastian Risi

A user study shows that both the evolution and latent space exploration features are appreciated, with a slight preference for direct exploration, but combining these features allows users to discover even better levels.

Learning Convolutional Sparse Coding on Complex Domain for Interferometric Phase Restoration

no code implementations6 Mar 2020 Jian Kang, Danfeng Hong, Jialin Liu, Gerald Baier, Naoto Yokoya, Begüm Demir

Interferometric phase restoration has been investigated for decades and most of the state-of-the-art methods have achieved promising performances for InSAR phase restoration.

Task Augmentation by Rotating for Meta-Learning

1 code implementation arXiv 2020 Jialin Liu, Fei Chao, Chih-Min Lin

Data augmentation is one of the most effective approaches for improving the accuracy of modern machine learning models, and it is also indispensable to train a deep model for meta-learning.

Data Augmentation Few-Shot Learning

Decoder Choice Network for Meta-Learning

1 code implementation arXiv 2019 Jialin Liu, Fei Chao, Longzhi Yang, Chih-Min Lin, Qiang Shen

This work proposes a method that controls the gradient descent process of the model parameters of a neural network by limiting the model parameters in a low-dimensional latent space.

Ensemble Learning Few-Shot Learning +1

Stock Prices Prediction using Deep Learning Models

no code implementations25 Sep 2019 Jialin Liu, Fei Chao, Yu-Chen Lin, Chih-Min Lin

The results show that predicting stock price through price rate of change is better than predicting absolute prices directly.

Plug-and-Play Methods Provably Converge with Properly Trained Denoisers

1 code implementation14 May 2019 Ernest K. Ryu, Jialin Liu, Sicheng Wang, Xiaohan Chen, Zhangyang Wang, Wotao Yin

Plug-and-play (PnP) is a non-convex framework that integrates modern denoising priors, such as BM3D or deep learning-based denoisers, into ADMM or other proximal algorithms.


ALISTA: Analytic Weights Are As Good As Learned Weights in LISTA

no code implementations ICLR 2019 Jialin Liu, Xiaohan Chen, Zhangyang Wang, Wotao Yin

In this work, we propose Analytic LISTA (ALISTA), where the weight matrix in LISTA is computed as the solution to a data-free optimization problem, leaving only the stepsize and threshold parameters to data-driven learning.

Algorithm Portfolio for Individual-based Surrogate-Assisted Evolutionary Algorithms

no code implementations22 Apr 2019 Hao Tong, Jialin Liu, Xin Yao

Surrogate-assisted evolutionary algorithms (SAEAs) are powerful optimisation tools for computationally expensive problems (CEPs).

Rinascimento: Optimising Statistical Forward Planning Agents for Playing Splendor

1 code implementation3 Apr 2019 Ivan Bravi, Simon Lucas, Diego Perez-Liebana, Jialin Liu

Game-based benchmarks have been playing an essential role in the development of Artificial Intelligence (AI) techniques.

Voronoi-based Efficient Surrogate-assisted Evolutionary Algorithm for Very Expensive Problems

1 code implementation17 Jan 2019 Hao Tong, Changwu Huang, Jialin Liu, Xin Yao

A performance selector is designed to switch the search dynamically and automatically between the global and local search stages.

Efficient Evolutionary Methods for Game Agent Optimisation: Model-Based is Best

1 code implementation3 Jan 2019 Simon M. Lucas, Jialin Liu, Ivan Bravi, Raluca D. Gaina, John Woodward, Vanessa Volz, Diego Perez-Liebana

This paper introduces a simple and fast variant of Planet Wars as a test-bed for statistical planning based Game AI agents, and for noisy hyper-parameter optimisation.


Helix: Holistic Optimization for Accelerating Iterative Machine Learning

no code implementations14 Dec 2018 Doris Xin, Stephen Macke, Litian Ma, Jialin Liu, Shuchen Song, Aditya Parameswaran

Machine learning workflow development is a process of trial-and-error: developers iterate on workflows by testing out small modifications until the desired accuracy is achieved.

Helix: Accelerating Human-in-the-loop Machine Learning

no code implementations3 Aug 2018 Doris Xin, Litian Ma, Jialin Liu, Stephen Macke, Shuchen Song, Aditya Parameswaran

Data application developers and data scientists spend an inordinate amount of time iterating on machine learning (ML) workflows -- by modifying the data pre-processing, model training, and post-processing steps -- via trial-and-error to achieve the desired model performance.

Structured Prediction

Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model

3 code implementations NeurIPS 2019 Atılım Güneş Baydin, Lukas Heinrich, Wahid Bhimji, Lei Shao, Saeid Naderiparizi, Andreas Munk, Jialin Liu, Bradley Gram-Hansen, Gilles Louppe, Lawrence Meadows, Philip Torr, Victor Lee, Prabhat, Kyle Cranmer, Frank Wood

We present a novel probabilistic programming framework that couples directly to existing large-scale simulators through a cross-platform probabilistic execution protocol, which allows general-purpose inference engines to record and control random number draws within simulators in a language-agnostic way.

Probabilistic Programming

Deep Reinforcement Learning for General Video Game AI

1 code implementation6 Jun 2018 Ruben Rodriguez Torrado, Philip Bontrager, Julian Togelius, Jialin Liu, Diego Perez-Liebana

In this paper, we describe how we interface GVGAI to the OpenAI Gym environment, a widely used way of connecting agents to reinforcement learning problems.

Atari Games OpenAI Gym +1

Shallow decision-making analysis in General Video Game Playing

1 code implementation4 Jun 2018 Ivan Bravi, Jialin Liu, Diego Perez-Liebana, Simon Lucas

The General Video Game AI competitions have been the testing ground for several techniques for game playing, such as evolutionary computation techniques, tree search algorithms, hyper heuristic based or knowledge based algorithms.

Decision Making

Evolving Mario Levels in the Latent Space of a Deep Convolutional Generative Adversarial Network

3 code implementations2 May 2018 Vanessa Volz, Jacob Schrum, Jialin Liu, Simon M. Lucas, Adam Smith, Sebastian Risi

This paper trains a GAN to generate levels for Super Mario Bros using a level from the Video Game Level Corpus.

SNES Games

General Video Game AI: a Multi-Track Framework for Evaluating Agents, Games and Content Generation Algorithms

1 code implementation28 Feb 2018 Diego Perez-Liebana, Jialin Liu, Ahmed Khalifa, Raluca D. Gaina, Julian Togelius, Simon M. Lucas

In 2014, The General Video Game AI (GVGAI) competition framework was created and released with the purpose of providing researchers a common open-source and easy to use platform for testing their AI methods with potentially infinity of games created using Video Game Description Language (VGDL).

PSO-based Fuzzy Markup Language for Student Learning Performance Evaluation and Educational Application

no code implementations24 Feb 2018 Chang-Shing Lee, Mei-Hui Wang, Chi-Shiang Wang, Olivier Teytaud, Jialin Liu, Su-Wei Lin, Pi-Hsia Hung

This paper proposes an agent with particle swarm optimization (PSO) based on a Fuzzy Markup Language (FML) for students learning performance evaluation and educational applications, and the proposed agent is according to the response data from a conventional test and an item response theory.

The N-Tuple Bandit Evolutionary Algorithm for Game Agent Optimisation

4 code implementations16 Feb 2018 Simon M. Lucas, Jialin Liu, Diego Perez-Liebana

This paper describes the N-Tuple Bandit Evolutionary Algorithm (NTBEA), an optimisation algorithm developed for noisy and expensive discrete (combinatorial) optimisation problems.

First and Second Order Methods for Online Convolutional Dictionary Learning

no code implementations31 Aug 2017 Jialin Liu, Cristina Garcia-Cardona, Brendt Wohlberg, Wotao Yin

Convolutional sparse representations are a form of sparse representation with a structured, translation invariant dictionary.

Dictionary Learning Second-order methods +1

Efficient Noisy Optimisation with the Sliding Window Compact Genetic Algorithm

no code implementations7 Aug 2017 Simon M. Lucas, Jialin Liu, Diego Pérez-Liébana

The compact genetic algorithm is an Estimation of Distribution Algorithm for binary optimisation problems.

Online Convolutional Dictionary Learning

no code implementations29 Jun 2017 Jialin Liu, Cristina Garcia-Cardona, Brendt Wohlberg, Wotao Yin

While a number of different algorithms have recently been proposed for convolutional dictionary learning, this remains an expensive problem.

Dictionary Learning

Evaluating Noisy Optimisation Algorithms: First Hitting Time is Problematic

no code implementations13 Jun 2017 Simon M. Lucas, Jialin Liu, Diego Pérez-Liébana

A frequently used stopping condition in runtime analysis, known as "First Hitting Time", is to stop the algorithm as soon as it encounters the optimal solution.

Analysis of Vanilla Rolling Horizon Evolution Parameters in General Video Game Playing

no code implementations24 Apr 2017 Raluca D. Gaina, Jialin Liu, Simon M. Lucas, Diego Perez-Liebana

Monte Carlo Tree Search techniques have generally dominated General Video Game Playing, but recent research has started looking at Evolutionary Algorithms and their potential at matching Tree Search level of play or even outperforming these methods.

Evolving Game Skill-Depth using General Video Game AI Agents

no code implementations18 Mar 2017 Jialin Liu, Julian Togelius, Diego Perez-Liebana, Simon M. Lucas

The space of possible parameter settings can be seen as a search space, and we can therefore use a Random Mutation Hill Climbing algorithm or other search methods to find the parameter settings that induce the best games.

The N-Tuple Bandit Evolutionary Algorithm for Automatic Game Improvement

2 code implementations18 Mar 2017 Kamolwan Kunanusont, Raluca D. Gaina, Jialin Liu, Diego Perez-Liebana, Simon M. Lucas

This paper describes a new evolutionary algorithm that is especially well suited to AI-Assisted Game Design.

Automatically Reinforcing a Game AI

no code implementations27 Jul 2016 David L. St-Pierre, Jean-Baptiste Hoock, Jialin Liu, Fabien Teytaud, Olivier Teytaud

In addition, we consider the case in which only one GPP is available - by decomposing this single GPP into several ones through the use of parameters or even simply random seeds.

online learning

Optimal resampling for the noisy OneMax problem

no code implementations22 Jul 2016 Jialin Liu, Michael Fairbank, Diego Pérez-Liébana, Simon M. Lucas

The OneMax problem is a standard benchmark optimisation problem for a binary search space.

Rolling Horizon Coevolutionary Planning for Two-Player Video Games

no code implementations6 Jul 2016 Jialin Liu, Diego Pérez-Liébana, Simon M. Lucas

To select an action the algorithm co-evolves two (or in the general case N) populations, one for each player, where each individual is a sequence of actions for the respective player.

Decision Making

Matrix Factorization at Scale: a Comparison of Scientific Data Analytics in Spark and C+MPI Using Three Case Studies

1 code implementation5 Jul 2016 Alex Gittens, Aditya Devarakonda, Evan Racah, Michael Ringenburg, Lisa Gerhardt, Jey Kottalam, Jialin Liu, Kristyn Maschhoff, Shane Canon, Jatin Chhugani, Pramod Sharma, Jiyan Yang, James Demmel, Jim Harrell, Venkat Krishnamurthy, Michael W. Mahoney, Prabhat

We explore the trade-offs of performing linear algebra using Apache Spark, compared to traditional C and MPI implementations on HPC platforms.

Distributed, Parallel, and Cluster Computing G.1.3; C.2.4

Bandit-Based Random Mutation Hill-Climbing

no code implementations20 Jun 2016 Jialin Liu, Diego Peŕez-Liebana, Simon M. Lucas

The Random Mutation Hill-Climbing algorithm is a direct search technique mostly used in discrete domains.

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