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Found 84 papers, 39 papers with code
StarGen: A Spatiotemporal Autoregression Framework with Video Diffusion Model for Scalable and Controllable Scene Generation
Recent advances in large reconstruction and generative models have significantly improved scene reconstruction and novel view generation.
Deeply Learned Robust Matrix Completion for Large-scale Low-rank Data Recovery
This paper proposes a novel scalable and learnable non-convex approach, coined Learned Robust Matrix Completion (LRMC), for large-scale RMC problems.
Exploring Accuracy-Fairness Trade-off in Large Language Models
In our research, we delve deeper into the intricate challenge of harmonising accuracy and fairness in the enhancement of LLMs.
Learning Attentional Mixture of LoRAs for Language Model Continual Learning
The key of our approach is that we devise an attention mechanism as a knowledge mixture module to adaptively integrate information from each LoRA.
Fairness-aware Multiobjective Evolutionary Learning
The dynamically determined representative set is then used as optimising objectives of the MOEL framework and can vary with time.
S$^3$Attention: Improving Long Sequence Attention with Smoothed Skeleton Sketching
Attention based models have achieved many remarkable breakthroughs in numerous applications.
A Universal Flexible Near-sensor Neuromorphic Tactile System with Multi-threshold strategy for Pressure Characteristic Detection
Constructing the new generation information processing system by mimicking biological nervous system is a feasible way for implement of high-efficient intelligent sensing device and bionic robot.
Interpreting Multi-objective Evolutionary Algorithms via Sokoban Level Generation
Designed to bridge theoretical optimisation strategies with practical game generation applications, the interface is also accessible to both researchers and beginners to multi-objective evolutionary algorithms or procedural content generation on a website.
3D Building Generation in Minecraft via Large Language Models
Recently, procedural content generation has exhibited considerable advancements in the domain of 2D game level generation such as Super Mario Bros. and Sokoban through large language models (LLMs).
Expressive Power of Graph Neural Networks for (Mixed-Integer) Quadratic Programs
In this work, we investigate the expressive or representative power of GNNs, a crucial aspect of neural network theory, specifically in the context of QP tasks, with both continuous and mixed-integer settings.
Learning to optimize: A tutorial for continuous and mixed-integer optimization
Learning to Optimize (L2O) stands at the intersection of traditional optimization and machine learning, utilizing the capabilities of machine learning to enhance conventional optimization techniques.
Evolutionary Reinforcement Learning via Cooperative Coevolution
Recently, evolutionary reinforcement learning has obtained much attention in various domains.
Learning from Offline and Online Experiences: A Hybrid Adaptive Operator Selection Framework
A novel hybrid framework that learns to dynamically and adaptively select promising search operators is proposed.
Measuring Diversity of Game Scenarios
This survey comprehensively reviews the multi-dimensionality of game scenario diversity, spotlighting the innovative use of procedural content generation and other fields as cornerstones for enriching player experiences through diverse game scenarios.
Game Generation via Large Language Models
Recently, the emergence of large language models (LLMs) has unlocked new opportunities for procedural content generation.
Rethinking the Capacity of Graph Neural Networks for Branching Strategy
In the literature, message-passing GNN (MP-GNN), as the simplest GNN structure, is frequently used as a fast approximation of SB and we find that not all MILPs's SB can be represented with MP-GNN.
A manometric feature descriptor with linear-SVM to distinguish esophageal contraction vigor
We conducted image processing of HRM to predict the esophageal contraction vigor for assisting the evaluation of esophageal dynamic function.
A General Neural Causal Model for Interactive Recommendation
Mitigation of the survivor bias is achieved though counterfactual consistency.
Adversarial Batch Inverse Reinforcement Learning: Learn to Reward from Imperfect Demonstration for Interactive Recommendation
In this research, we focus on the problem of learning to reward (LTR), which is fundamental to reinforcement learning.
DIG-MILP: a Deep Instance Generator for Mixed-Integer Linear Programming with Feasibility Guarantee
Mixed-integer linear programming (MILP) stands as a notable NP-hard problem pivotal to numerous crucial industrial applications.
Generating Redstone Style Cities in Minecraft
Procedurally generating cities in Minecraft provides players more diverse scenarios and could help understand and improve the design of cities in other digital worlds and the real world.
Towards Constituting Mathematical Structures for Learning to Optimize
Learning to Optimize (L2O), a technique that utilizes machine learning to learn an optimization algorithm automatically from data, has gained arising attention in recent years.
Constrained Reinforcement Learning for Dynamic Material Handling
Experimental results on the problem instances demonstrate the outstanding performance of our proposed approach compared with eight state-of-the-art constrained and non-constrained reinforcement learning algorithms, and widely used dispatching rules for material handling.
Local Optima Correlation Assisted Adaptive Operator Selection
In this paper, we propose to empirically analyse the relationship between operators in terms of the correlation between their local optima and develop a measure for quantifying their relationship.
Games for Artificial Intelligence Research: A Review and Perspectives
Those platforms provide ideal benchmarks for exploring and comparing artificial intelligence ideas and techniques.
Evolving Constrained Reinforcement Learning Policy
Evolutionary algorithms have been used to evolve a population of actors to generate diverse experiences for training reinforcement learning agents, which helps to tackle the temporal credit assignment problem and improves the exploration efficiency.
Side Channel-Assisted Inference Leakage from Machine Learning-based ECG Classification
In particular, we first identify the vulnerability of DTW for ECG classification, i. e., the correlation between warping path choice and prediction results.
Integrated optimization of train timetables rescheduling and response vehicles on a disrupted metro line
Numerical cases of Beijing Metro Line 9 verify the efficiency and effectiveness of our proposed model, and results show that: (1) when occurring a disruption event during peak hours, the impact on the normal timetable is greater, and passengers in the direction with fewer train services are more affected; (2) if passengers stranded at the terminal stations of disruption area are not transported in time, they will rapidly increase at a speed of more than 300 passengers per minute; (3) compared with the fixed shortest path, using the response vehicles reduces the total travel time about 7%.
State Space Closure: Revisiting Endless Online Level Generation via Reinforcement Learning
Concluding our outcomes and analysis, future work on endless online level generation via reinforcement learning should address the issue of diversity while assuring the occurrence of state space closure and quality.
On Representing Mixed-Integer Linear Programs by Graph Neural Networks
While Mixed-integer linear programming (MILP) is NP-hard in general, practical MILP has received roughly 100--fold speedup in the past twenty years.
On Representing Linear Programs by Graph Neural Networks
In particular, the graph neural network (GNN) is considered a suitable ML model for optimization problems whose variables and constraints are permutation--invariant, for example, the linear program (LP).
Online Game Level Generation from Music
Game consists of multiple types of content, while the harmony of different content types play an essential role in game design.
Generating Game Levels of Diverse Behaviour Engagement
Recent years, there has been growing interests in experience-driven procedural level generation.
Hyperparameter Tuning is All You Need for LISTA
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
Robust principal component analysis (RPCA) is a critical tool in modern machine learning, which detects outliers in the task of low-rank matrix reconstruction.
Keiki: Towards Realistic Danmaku Generation via Sequential GANs
Search-based procedural content generation methods have recently been introduced for the autonomous creation of bullet hell games.
Experience-Driven PCG via Reinforcement Learning: A Super Mario Bros Study
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
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
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
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.
Deep Learning for Procedural Content Generation
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
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.
Ranked #7 on
Passage Retrieval
on Natural Questions
A Hybrid Evolutionary Algorithm for Reliable Facility Location Problem
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.
Wavelet Scattering Networks for Atomistic Systems with Extrapolation of Material Properties
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.
Towards in-store multi-person tracking using head detection and track heatmaps
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.
A Novel CNet-assisted Evolutionary Level Repairer and Its Applications to Super Mario Bros
However, defective levels with illegal patterns may be generated due to the violation of constraints for level design.
Versatile Black-Box Optimization
Choosing automatically the right algorithm using problem descriptors is a classical component of combinatorial optimization.
Interactive Evolution and Exploration Within Latent Level-Design Space of Generative Adversarial Networks
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
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
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.
Stock Prices Prediction using Deep Learning Models
The results show that predicting stock price through price rate of change is better than predicting absolute prices directly.
Decoder Choice Network for Meta-Learning
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.
Gradient Boost with Convolution Neural Network for Stock Forecast
Market economy closely connects aspects to all walks of life.
Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale
Probabilistic programming languages (PPLs) are receiving widespread attention for performing Bayesian inference in complex generative models.
Plug-and-Play Methods Provably Converge with Properly Trained Denoisers
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
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
Surrogate-assisted evolutionary algorithms (SAEAs) are powerful optimisation tools for computationally expensive problems (CEPs).
Rinascimento: Optimising Statistical Forward Planning Agents for Playing Splendor
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
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
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
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.
Theoretical Linear Convergence of Unfolded ISTA and its Practical Weights and Thresholds
In this work, we study unfolded ISTA (Iterative Shrinkage Thresholding Algorithm) for sparse signal recovery.
Helix: Accelerating Human-in-the-loop Machine Learning
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.
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model
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.
Deep Reinforcement Learning for General Video Game AI
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.
Shallow decision-making analysis in General Video Game Playing
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.
Evolving Mario Levels in the Latent Space of a Deep Convolutional Generative Adversarial Network
This paper trains a GAN to generate levels for Super Mario Bros using a level from the Video Game Level Corpus.
General Video Game AI: a Multi-Track Framework for Evaluating Agents, Games and Content Generation Algorithms
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
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
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
Convolutional sparse representations are a form of sparse representation with a structured, translation invariant dictionary.
Efficient Noisy Optimisation with the Sliding Window Compact Genetic Algorithm
The compact genetic algorithm is an Estimation of Distribution Algorithm for binary optimisation problems.
Online Convolutional Dictionary Learning
While a number of different algorithms have recently been proposed for convolutional dictionary learning, this remains an expensive problem.
Evaluating Noisy Optimisation Algorithms: First Hitting Time is Problematic
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
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.
The N-Tuple Bandit Evolutionary Algorithm for Automatic Game Improvement
This paper describes a new evolutionary algorithm that is especially well suited to AI-Assisted Game Design.
Evolving Game Skill-Depth using General Video Game AI Agents
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.
Automatically Reinforcing a Game AI
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.
Optimal resampling for the noisy OneMax problem
The OneMax problem is a standard benchmark optimisation problem for a binary search space.
Learning opening books in partially observable games: using random seeds in Phantom Go
Many artificial intelligences (AIs) are randomized.
Rolling Horizon Coevolutionary Planning for Two-Player Video Games
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.
Matrix Factorization at Scale: a Comparison of Scientific Data Analytics in Spark and C+MPI Using Three Case Studies
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
The Random Mutation Hill-Climbing algorithm is a direct search technique mostly used in discrete domains.
Random Multi-Constraint Projection: Stochastic Gradient Methods for Convex Optimization with Many Constraints
Consider convex optimization problems subject to a large number of constraints.
