Exploration of the high-dimensional state action space is one of the biggest challenges in Reinforcement Learning (RL), especially in multi-agent domain. We present a novel technique called Experience Augmentation, which enables a time-efficient and boosted learning based on a fast, fair and thorough exploration to the environment. It can be combined with arbitrary off-policy MARL algorithms and is applicable to either homogeneous or heterogeneous environments. We demonstrate our approach by combining it with MADDPG and verifing the performance in two homogeneous and one heterogeneous environments. In the best performing scenario, the MADDPG with experience augmentation reaches to the convergence reward of vanilla MADDPG with 1/4 realistic time, and its convergence beats the original model by a significant margin. Our ablation studies show that experience augmentation is a crucial ingredient which accelerates the training process and boosts the convergence.
Learning informative representations from image-based observations is of fundamental concern in deep Reinforcement Learning (RL). However, data-inefficiency remains a significant barrier to this objective. To overcome this obstacle, we propose to accelerate state representation learning by enforcing view-consistency on the dynamics. Firstly, we introduce a formalism of Multi-view Markov Decision Process (MMDP) that incorporates multiple views of the state. Following the structure of MMDP, our method, View-Consistent Dynamics (VCD), learns state representations by training a view-consistent dyn…
As more and more artificial intelligence (AI) technologies move from the laboratory to real-world applications, the open-set and robustness challenges brought by data from the real world have received increasing attention. Data augmentation is a widely used method to improve model performance, and some recent works have also confirmed its positive effect on the robustness of AI models. However, most of the existing data augmentation methods are heuristic, lacking the exploration of their internal mechanisms. We apply the explainable artificial intelligence (XAI) method, explore the internal me…
Google（谷歌）；University of Chicago（芝加哥大学）；EPFL（洛桑联邦理工学院）
Gradient Boosting Machine (GBM) is an extremely powerful supervised learning algorithm that is widely used in practice. GBM routinely features as a leading algorithm in machine learning competitions such as Kaggle and the KDDCup. In this work, we propose Accelerated Gradient Boosting Machine (AGBM) by incorporating Nesterov's acceleration techniques into the design of GBM. The difficulty in accelerating GBM lies in the fact that weak (inexact) learners are commonly used, and therefore the errors can accumulate in the momentum term. To overcome it, we design a "corrected pseudo residual" and fi…
Boosting Reinforcement Learning with Strongly Delayed Feedback Through Auxiliary Short Delays
The University of Liverpool（利物浦大学）；Northwestern University（西北大学）；King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）；National Taiwan University（国立台湾大学）；Nanyang Technological University（南洋理工大学）；The Swiss AI Lab IDSIA/USI/SUPSI（瑞士IDSIA人工智能实验室（隶属于瑞士意大利语区大学与瑞士应用科学大学））；The University of Southampton（南安普顿大学）
Data61-CSIRO（Data61-联邦科学与工业研究组织）；Australian National University（澳大利亚国立大学）；University of Adelaide（阿德莱德大学）
We present You Only Cut Once (YOCO) for performing data augmentations. YOCO cuts one image into two pieces and performs data augmentations individually within each piece. Applying YOCO improves the diversity of the augmentation per sample and encourages neural networks to recognize objects from partial information. YOCO enjoys the properties of parameter-free, easy usage, and boosting almost all augmentations for free. Thorough experiments are conducted to evaluate its effectiveness. We first demonstrate that YOCO can be seamlessly applied to varying data augmentations, neural network architec…
Accelerating Dataset Distillation via Model Augmentation
Zhejiang University（浙江大学）；Texas A&M University（德州农工大学）；Microsoft Research（微软研究院）；New York University（纽约大学）；Beijing Academy of Artificial Intelligence（北京智源人工智能研究院）；University of Connecticut（康涅狄格大学）；University of North Carolina, Chapel Hill（北卡罗来纳大学教堂山分校）；North Carolina State University（北卡罗来纳州立大学）
$\texttt{SynC}$: Synergistic Boosting of Structure and Representation for Deep Graph Clustering
School of Computer Science and Technology/the School of Artificial Intelligence, China University of Mining and Technology（计算机科学与技术学院/人工智能学院，中国矿业大学）；Mine Digitization Engineering Research Center of Ministry of Education（教育部矿山数字化工程研究中心）；College of Intelligence and Computing, Tianjin University（智能与计算学院，天津大学）；Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China), Hefei University of Technology（大数据知识工程重点实验室（教育部），合肥工业大学）
Edith Cowan University（伊迪斯科文大学）；University of Western Australia（西澳大学）
COVID-19 classification using chest Computed Tomography (CT) has been found pragmatically useful by several studies. Due to the lack of annotated samples, these studies recommend transfer learning and explore the choices of pre-trained models and data augmentation. However, it is still unknown if there are better strategies than vanilla transfer learning for more accurate COVID-19 classification with limited CT data. This paper provides an affirmative answer, devising a novel `model' augmentation technique that allows a considerable performance boost to transfer learning for the task. Our meth…
MedDiffusion: Boosting Health Risk Prediction via Diffusion-based Data Augmentation
The Pennsylvania State University（宾夕法尼亚州立大学）；Purdue University（普渡大学）；Georgia State University（佐治亚州立大学）；Iowa State University（爱荷华州立大学）；Stony Brook University（石溪大学）
Boosting Classification with Quantum-Inspired Augmentations
German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）；RPTU Kaiserslautern-Landau（科隆-兰道大学）；Department of Computer Science and Research Initiative QC-AI（计算机科学系和量子人工智能研究计划）；Department of Physics（物理系）
University of Pittsburgh（匹兹堡大学）；Nordita, KTH（北欧理论物理研究所，瑞典皇家理工学院）；University of Toronto（多伦多大学）；ICSI（国际计算机科学研究所）；UC Berkeley（加州大学伯克利分校）
For many real-world applications, obtaining stable and robust statistical performance is more important than simply achieving state-of-the-art predictive test accuracy, and thus robustness of neural networks is an increasingly important topic. Relatedly, data augmentation schemes have been shown to improve robustness with respect to input perturbations and domain shifts. Motivated by this, we introduce NoisyMix, a novel training scheme that promotes stability as well as leverages noisy augmentations in input and feature space to improve both model robustness and in-domain accuracy. NoisyMix pr…
Boosting Statistic Learning with Synthetic Data from Pretrained Large Models
Shanghai University of Finance and Economics（上海财经大学）；The Hong Kong Polytechnic University（香港理工大学）；School of Mathematics and Statistics, Wuhan University（武汉大学数学与统计学院）
Towards Effective Graph Rationalization via Boosting Environment Diversity
School of Computer Science and Information Engineering, Hefei University of Technology（合肥工业大学计算机与信息工程学院）；School of Computer and Information Technology, Shanxi University（山西大学计算机与信息技术学院）
Boosting Convolutional Neural Networks' Protein Binding Site Prediction Capacity Using SE(3)-invariant transformers, Transfer Learning and Homology-based Augmentation
A central challenge in training one-shot learning models is the limited representativeness of the available shots of the data space. Particularly in the field of network neuroscience where the brain is represented as a graph, such models may lead to low performance when classifying brain states (e.g., typical vs. autistic). To cope with this, most of the existing works involve a data augmentation step to increase the size of the training set, its diversity and representativeness. Though effective, such augmentation methods are limited to generating samples with the same size as the input shots…
Data augmentation, a technique in which a training set is expanded with class-preserving transformations, is ubiquitous in modern machine learning pipelines. In this paper, we seek to establish a theoretical framework for understanding data augmentation. We approach this from two directions: First, we provide a general model of augmentation as a Markov process, and show that kernels appear naturally with respect to this model, even when we do not employ kernel classification. Next, we analyze more directly the effect of augmentation on kernel classifiers, showing that data augmentation can be …
Baidu Inc.（百度公司）；Beijing University of Posts and Telecommunications（北京邮电大学）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
Federated machine learning systems have been widely used to facilitate the joint data analytics across the distributed datasets owned by the different parties that do not trust each others. In this paper, we proposed a novel Gradient Boosting Machines (GBM) framework SecureGBM built-up with a multi-party computation model based on semi-homomorphic encryption, where every involved party can jointly obtain a shared Gradient Boosting machines model while protecting their own data from the potential privacy leakage and inferential identification. More specific, our work focused on a specific "dual…
Data sparsity is one of the key challenges associated with model development in Natural Language Understanding (NLU) for conversational agents. The challenge is made more complex by the demand for high quality annotated utterances commonly required for supervised learning, usually resulting in weeks of manual labor and high cost. In this paper, we present our results on boosting NLU model performance through training data augmentation using a sequential generative adversarial network (GAN). We explore data generation in the context of two tasks, the bootstrapping of a new language and the hand…
Vicomtech Foundation, Basque Research and Technology Alliance (BRTA)（维康科技基金会，巴斯克研究与技术联盟（BRTA））；University of the Basque Country（巴斯克大学）
In this paper, we address the problem of face recognition with masks. Given the global health crisis caused by COVID-19, mouth and nose-covering masks have become an essential everyday-clothing-accessory. This sanitary measure has put the state-of-the-art face recognition models on the ropes since they have not been designed to work with masked faces. In addition, the need has arisen for applications capable of detecting whether the subjects are wearing masks to control the spread of the virus. To overcome these problems a full training pipeline is presented based on the ArcFace work, with sev…
