M2HGCL: Multi-Scale Meta-Path Integrated Heterogeneous Graph Contrastive Learning

Inspired by the successful application of contrastive learning on graphs, researchers attempt to impose graph contrastive learning approaches on heterogeneous information networks. Orthogonal to homogeneous graphs, the types of nodes and edges in heterogeneous graphs are diverse so that specialized graph contrastive learning methods are required. Most existing methods for heterogeneous graph contrastive learning are implemented by transforming heterogeneous graphs into homogeneous graphs, which may lead to ramifications that the valuable information carried by non-target nodes is undermined thereby exacerbating the performance of contrastive learning models. Additionally, current heterogeneous graph contrastive learning methods are mainly based on initial meta-paths given by the dataset, yet according to our deep-going exploration, we derive empirical conclusions: only initial meta-paths cannot contain sufficiently discriminative information; and various types of meta-paths can effectively promote the performance of heterogeneous graph contrastive learning methods. To this end, we propose a new multi-scale meta-path integrated heterogeneous graph contrastive learning (M2HGCL) model, which discards the conventional heterogeneity-homogeneity transformation and performs the graph contrastive learning in a joint manner. Specifically, we expand the meta-paths and jointly aggregate the direct neighbor information, the initial meta-path neighbor information and the expanded meta-path neighbor information to sufficiently capture discriminative information. A specific positive sampling strategy is further imposed to remedy the intrinsic deficiency of contrastive learning, i.e., the hard negative sample sampling issue. Through extensive experiments on three real-world datasets, we demonstrate that M2HGCL outperforms the current state-of-the-art baseline models.Comment: Accepted to the conference of ADMA2023 as an Oral presentatio

Logic Programs as Declarative and Procedural Bias in Inductive Logic Programming

Machine Learning is necessary for the development of Artificial Intelligence, as pointed out by Turing in his 1950 article ``Computing Machinery and Intelligence''. It is in the same article that Turing suggested the use of computational logic and background knowledge for learning. This thesis follows a logic-based machine learning approach called Inductive Logic Programming (ILP), which is advantageous over other machine learning approaches in terms of relational learning and utilising background knowledge. ILP uses logic programs as a uniform representation for hypothesis, background knowledge and examples, but its declarative bias is usually encoded using metalogical statements. This thesis advocates the use of logic programs to represent declarative and procedural bias, which results in a framework of single-language representation. We show in this thesis that using a logic program called the top theory as declarative bias leads to a sound and complete multi-clause learning system MC-TopLog. It overcomes the entailment-incompleteness of Progol, thus outperforms Progol in terms of predictive accuracies on learning grammars and strategies for playing Nim game. MC-TopLog has been applied to two real-world applications funded by Syngenta, which is an agriculture company. A higher-order extension on top theories results in meta-interpreters, which allow the introduction of new predicate symbols. Thus the resulting ILP system Metagol can do predicate invention, which is an intrinsically higher-order logic operation. Metagol also leverages the procedural semantic of Prolog to encode procedural bias, so that it can outperform both its ASP version and ILP systems without an equivalent procedural bias in terms of efficiency and accuracy. This is demonstrated by the experiments on learning Regular, Context-free and Natural grammars. Metagol is also applied to non-grammar learning tasks involving recursion and predicate invention, such as learning a definition of staircases and robot strategy learning. Both MC-TopLog and Metagol are based on a $\top$-directed framework, which is different from other multi-clause learning systems based on Inverse Entailment, such as CF-Induction, XHAIL and IMPARO. Compared to another $\top$-directed multi-clause learning system TAL, Metagol allows the explicit form of higher-order assumption to be encoded in the form of meta-rules.Open Acces

Attention Graph for Multi-Robot Social Navigation with Deep Reinforcement Learning

Learning robot navigation strategies among pedestrian is crucial for domain based applications. Combining perception, planning and prediction allows us to model the interactions between robots and pedestrians, resulting in impressive outcomes especially with recent approaches based on deep reinforcement learning (RL). However, these works do not consider multi-robot scenarios. In this paper, we present MultiSoc, a new method for learning multi-agent socially aware navigation strategies using RL. Inspired by recent works on multi-agent deep RL, our method leverages graph-based representation of agent interactions, combining the positions and fields of view of entities (pedestrians and agents). Each agent uses a model based on two Graph Neural Network combined with attention mechanisms. First an edge-selector produces a sparse graph, then a crowd coordinator applies node attention to produce a graph representing the influence of each entity on the others. This is incorporated into a model-free RL framework to learn multi-agent policies. We evaluate our approach on simulation and provide a series of experiments in a set of various conditions (number of agents / pedestrians). Empirical results show that our method learns faster than social navigation deep RL mono-agent techniques, and enables efficient multi-agent implicit coordination in challenging crowd navigation with multiple heterogeneous humans. Furthermore, by incorporating customizable meta-parameters, we can adjust the neighborhood density to take into account in our navigation strategy

Exploiting Style Transfer-based Task Augmentation for Cross-Domain Few-Shot Learning

In cross-domain few-shot learning, the core issue is that the model trained on source domains struggles to generalize to the target domain, especially when the domain shift is large. Motivated by the observation that the domain shift between training tasks and target tasks usually can reflect in their style variation, we propose Task Augmented Meta-Learning (TAML) to conduct style transfer-based task augmentation to improve the domain generalization ability. Firstly, Multi-task Interpolation (MTI) is introduced to fuse features from multiple tasks with different styles, which makes more diverse styles available. Furthermore, a novel task-augmentation strategy called Multi-Task Style Transfer (MTST) is proposed to perform style transfer on existing tasks to learn discriminative style-independent features. We also introduce a Feature Modulation module (FM) to add random styles and improve generalization of the model. The proposed TAML increases the diversity of styles of training tasks, and contributes to training a model with better domain generalization ability. The effectiveness is demonstrated via theoretical analysis and thorough experiments on two popular cross-domain few-shot benchmarks

Neural Interactive Collaborative Filtering

In this paper, we study collaborative filtering in an interactive setting, in which the recommender agents iterate between making recommendations and updating the user profile based on the interactive feedback. The most challenging problem in this scenario is how to suggest items when the user profile has not been well established, i.e., recommend for cold-start users or warm-start users with taste drifting. Existing approaches either rely on overly pessimistic linear exploration strategy or adopt meta-learning based algorithms in a full exploitation way. In this work, to quickly catch up with the user's interests, we propose to represent the exploration policy with a neural network and directly learn it from the feedback data. Specifically, the exploration policy is encoded in the weights of multi-channel stacked self-attention neural networks and trained with efficient Q-learning by maximizing users' overall satisfaction in the recommender systems. The key insight is that the satisfied recommendations triggered by the exploration recommendation can be viewed as the exploration bonus (delayed reward) for its contribution on improving the quality of the user profile. Therefore, the proposed exploration policy, to balance between learning the user profile and making accurate recommendations, can be directly optimized by maximizing users' long-term satisfaction with reinforcement learning. Extensive experiments and analysis conducted on three benchmark collaborative filtering datasets have demonstrated the advantage of our method over state-of-the-art methods

심층 신경망 검색 기법을 사용한 이미지 복원

학위논문(박사) -- 서울대학교대학원 : 공과대학 전기·정보공학부, 2021.8. 안준영.Image restoration is an important technology which can be used as a pre-processing step to increase the performances of various vision tasks. Image super-resolution is one of the important task in image restoration which restores a high-resolution (HR) image from low-resolution (LR) observation. The recent progress of deep convolutional neural networks has enabled great success in single image super-resolution (SISR). its performance is also being increased by deepening the networks and developing more sophisticated network structures. However, finding an optimal structure for the given problem is a difficult task, even for human experts. For this reason, neural architecture search (NAS) methods have been introduced, which automate the procedure of constructing the structures. In this dissertation, I propose a new single image super-resolution framework by using neural architecture search (NAS) method. As the performance improves, the network becomes more complex and deeper, so I apply NAS algorithm to find the optimal network while reducing the effort in network design. In detail, the proposed scheme is summarized to three topics: image super-resolution using efficient neural architecture search, multi-branch neural architecture search for lightweight image super-resolution, and neural architecture search for image super-resolution using meta-transfer learning. At first, I expand the NAS to the super-resolution domain and find a lightweight densely connected network named DeCoNASNet. I use a hierarchical search strategy to find the best connection with local and global features. In this process, I define a complexity-based-penalty and add it to the reward term of REINFORCE algorithm. Experiments show that my DeCoNASNet outperforms the state-of-the-art lightweight super-resolution networks designed by handcraft methods and existing NAS-based design. I propose a new search space design with multi-branch structure to enlarge the search space for capturing multi-scale features, resulting in better reconstruction on grainy areas. I also adopt parameter sharing scheme in multi-branch network to share their information and reduce the whole network parameter. Experiments show that the proposed method finds an optimal SISR network about twenty times faster than the existing methods, while showing comparable performance in terms of PSNR vs. parameters. Comparison of visual quality validates that the proposed SISR network reconstructs texture areas better than the previous methods because of the enlarged search space to find multi-scale features. Lastly, I apply meta-transfer learning to the NAS procedure for image super-resolution. I train the controller and child network with the meta-learning scheme, which enables the controllers to find promising network for several scale simultaneously. Furthermore, meta-trained child network is reused as the pre-trained parameters for final evaluation phase to improve the final image super-resolution results even better and search-evaluation gap problem is efficiently reduced.이미지 복원은 다양한 영상처리 문제의 성능을 높이기 위한 전 처리 단계로 사용할 수 있는 중요한 기술이다. 이미지 고해상도화는 이미지 복원방법 중 중요한 문제의 하나로써 저해상도의 이미지를 고해상도의 이미지로 복원하는 방법이다. 최근에는 컨벌루션 신경망 (CNN)을 사용하는 딥 러닝(deep learning) 기반의 방법들이 단일 이미지 고해상도화 (SISR) 문제를 푸는데 많이 사용되고 있다. 일반적으로 이미지 고해상도화 성능은 CNN을 깊게 쌓거나 복잡한 구조를 설계함으로써 향상시킬 수 있다. 그러나 주어진 문제에 대한 최적의 구조를 찾는 것은 해당 분야의 전문가라 할 지라도 어렵고 시간이 오래 걸리는 작업이다. 이러한 이유로 신경망 구축 절차를 자동화하는 신경망 구조 검색 (NAS) 방법이 도입되었다. 이 논문에서는 신경망 구조 검색 (NAS) 방법을 사용하여 새로운 단일 이미지 고해상도화 방법을 제안하였다. 이 논문에서 제안한 방법은 크게 세 가지로 요약 할 수 있다. 이는 효율적인 신경망 검색기법(ENAS)을 이용한 이미지 고해상도화, 병렬 신경망 검색 기법을 이용한 이미지 고해상도화, 메타 전송 학습을 이용하는 신경망 검색기법을 통한 이미지 고해상도화 이다. 우선, 우리는 주로 영상 분류에 쓰이던 신경망 검색 기법을 영상 고해상도화에 적용하였으며, DeCoNASNet이라 명명된 신경망 구조를 설계하였다. 또한 계층적 검색 전략을 사용하여 지역/전역 피쳐(feature) 합병을 위한 최상의 연결 방법을 검색하였다. 이 과정에서 필요 변수가 적으면서 좋은 성능을 낼 수 있도록 복잡성 기반 페널티 (complexity-based penalty) 를 정의하고 이를 REINFORCE 알고리즘의 보상 신호에 추가하였다. 실험 결과 DeCoNASNet은 기존의 사람이 직접 설계한 신경망과 신경망 검색 기법을 기반으로 설계된 최근의 고해상도화 구조의 성능을 능가하는 것을 확인 할 수 있었다. 우리는 또한 여러 크기의 피쳐(feature)를 학습하기 위해 신경망 검색 기법의 검색 공간을 확대하여 병렬 신경망을 설계하는 방법을 제안하였다. 이 때, 병렬신경망의 각 위치에서 매개 변수를 공유할 수 있도록 하여 병렬신경망의 각 구조끼리 정보를 공유하고 전체 구조를 설계하는데 필요한 매개 변수를 줄이도록 하였다. 실험 결과 제안된 방법을 통해 매개 변수 크기 대비 성능이 좋은 신경망 구조를 찾을 수 있었다. 실험 결과를 통해 확장된 검색 공간에서 여러 크기의 피쳐 (feature)를 학습하였기 때문에 이전 방법보다 복잡한 영역을 더 잘 복원하는 것을 확인하였다. 마지막으로 메타 전송 학습(meta-transfer learning)을 신경망 검색에 적용하여 다양한 크기의 이미지 고해상도화 문제를 해결하는 방법을 제안하였다. 이 논문에서는 메타 전송 학습 방법을 통해 제어기가 여러 크기의 좋은 신경망 구조를 동시에 찾을 수 있도록 설계하였다. 또한 메타 훈련된 신경망 구조는 최종 성능 평가 시 학습의 시작점으로 재사용 되어 최종 이미지 고해상도화 성능을 더욱 향상시킬 수 있었으며, 효과적으로 검색-평가 괴리 문제를 해결하였다.1 INTRODUCTION 1 1.1 contribution 3 1.2 contents 4 2 Neural Architecture Search for Image Super-Resolution Using Densely Constructed Search Space: DeCoNAS 5 2.1 Introduction 5 2.2 Proposed Method 9 2.2.1 Overall structure of DeCoNASNet 9 2.2.2 Constructing the DNB 11 2.2.3 Constructing controller for the DeCoNASNet 13 2.2.4 Training DeCoNAS and complexity-based penalty 13 2.3 Experimental results 15 2.3.1 Settings 15 2.3.2 Results 16 2.3.3 Ablation study 21 2.4 Summary 22 3 Multi-Branch Neural Architecture Search for Lightweight Image Super-resolution 23 3.1 Introduction 23 3.2 Related Work 26 3.2.1 Single image super-resolution 26 3.2.2 Neural architecture search 27 3.2.3 Image super-resolution with neural architecture search 29 3.3 Method 32 3.3.1 Overview of the Proposed MBNAS 32 3.3.2 Controller and complexity-based penalty 33 3.3.3 MBNASNet 35 3.3.4 Multi-scale block with partially shared Nodes 37 3.3.5 MBNAS 38 3.4 datasets and experiments 39 3.4.1 Settings 39 3.4.2 Experiments on single image super-resolution (SISR) 41 3.5 Discussion 48 3.5.1 Effect of the complexity-based penalty to the performance of controller 49 3.5.2 Effect of multi-branch structure and partial parameter sharing scheme 50 3.5.3 Effect of gradient flow control weights and complexity-based penalty coefficient 51 3.6 Summary 52 4 Meta-transfer learning for simultaneous search of various scale image super-resolution 54 4.1 Introduction 54 4.2 Related Work 56 4.2.1 Single image super-resolution 56 4.2.2 Neural architecture search 57 4.2.3 Image super-resolution with neural architecture search 58 4.2.4 Meta-learning 59 4.3 Method 59 4.3.1 Meta-learning 60 4.3.2 Meta-transfer learning 62 4.3.3 Transfer-learning 63 4.4 datasets and experiments 63 4.4.1 Settings 63 4.4.2 Experiments on single image super-resolution(SISR) 64 4.5 Summary 66 5 Conclusion 69 Abstract (In Korean) 80박

Spiking Generative Adversarial Networks With a Neural Network Discriminator: Local Training, Bayesian Models, and Continual Meta-Learning

Neuromorphic data carries information in spatio-temporal patterns encoded by spikes. Accordingly, a central problem in neuromorphic computing is training spiking neural networks (SNNs) to reproduce spatio-temporal spiking patterns in response to given spiking stimuli. Most existing approaches model the input-output behavior of an SNN in a deterministic fashion by assigning each input to a specific desired output spiking sequence. In contrast, in order to fully leverage the time-encoding capacity of spikes, this work proposes to train SNNs so as to match distributions of spiking signals rather than individual spiking signals. To this end, the paper introduces a novel hybrid architecture comprising a conditional generator, implemented via an SNN, and a discriminator, implemented by a conventional artificial neural network (ANN). The role of the ANN is to provide feedback during training to the SNN within an adversarial iterative learning strategy that follows the principle of generative adversarial network (GANs). In order to better capture multi-modal spatio-temporal distribution, the proposed approach -- termed SpikeGAN -- is further extended to support Bayesian learning of the generator's weight. Finally, settings with time-varying statistics are addressed by proposing an online meta-learning variant of SpikeGAN. Experiments bring insights into the merits of the proposed approach as compared to existing solutions based on (static) belief networks and maximum likelihood (or empirical risk minimization)