The goal of this paper is to deal with a data scarcity scenario where deep learning techniques use to fail. We compare the use of two well established techniques, Restricted Boltzmann Machines and Variational Auto-encoders, as generative models in order to increase the training set in a classification framework. Essentially, we rely on Markov Chain Monte Carlo (MCMC) algorithms for generating new samples. We show that generalization can be improved comparing this methodology to other state-of-the-art techniques, e.g. semi-supervised learning with ladder networks. Furthermore, we show that RBM is better than VAE generating new samples for training a classifier with good generalization capabilities.
Losing dimensions: Geometric memorization in generative diffusion
Computing Sciences Department, Bocconi University（博科尼大学计算科学系）；Bocconi Institute for Data Science and Analytics (BIDSA)（博科尼数据科学与分析研究所）；Donders Institute, Radboud University（拉德堡德大学多纳尔斯研究所）；OnePlanet Research Center, Wageningen（瓦赫宁根大学OnePlanet研究中心）；Rensselaer Polytechnic Institute（伦斯勒理工学院）；Jheronimus Academy of Data Science (JADS)（杰霍尼穆斯数据科学学院）；IBM Research, Cambridge（IBM研究院，剑桥）
Auxiliary Discrminator Sequence Generative Adversarial Networks (ADSeqGAN) for Few Sample Molecule Generation
School of Pharmacy, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States；School of Software \& Microelectronics, Peking University, Beijing, 100871, China
Generative multi-domain transfer learning for fault detection in data-scarce wind turbines
School of Engineering and Computer Science, Bern University of Applied Sciences（伯尔尼应用科学大学工程与计算机科学学院）；Faculty of Informatics, Università della Svizzera italiana（瑞士意大利语大学信息学院）；Department of Geoscience and Remote Sensing, Delft University of Technology（代尔夫特理工大学地球科学与遥感系）
A Framework for Demonstrating Practical Quantum Advantage: Racing Quantum against Classical Generative Models
Zapata Computing Canada Inc.（扎帕塔计算加拿大公司）；Vector Institute（向量研究所）；Department of Physics and Astronomy（物理与天文学系）；University of Waterloo（滑铁卢大学）；Department of Physics（物理系）；University of Toronto（多伦多大学）
QTabGAN: A Hybrid Quantum-Classical GAN for Tabular Data Synthesis
Department of Computer Science and Engineering, Indian Institute of Technology (BHU), Varanasi, India（计算机科学与工程系，印度理工学院（BHU），瓦拉纳西，印度）；Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, United Kingdom（应用数学与理论物理系，剑桥大学，剑桥，英国）
Generating Physically Plausible Parachute Dynamics with Deep Generative Modeling
PhD Candidate, Mechanical and Aerospace Engineering, Engineering Quadrangle, Princeton, NJ 08544（普林斯顿大学机械与航空航天工程系博士研究生）；Group Supervisor, Dynamics, Modeling, Flight Mechanics & UQ, 4800 Oak Grove Dr, M/S 198-326, Pasadena, CA 91011, AIAA Member（加州帕萨迪纳AIAA会员，动力学、建模、飞行力学及不确定性量化组负责人）；Assistant Professor, Mechanical and Aerospace Engineering, Engineering Quadrangle, Princeton, NJ 08544（普林斯顿大学机械与航空航天工程系助理教授）；Assistant Professor, Center for Statistics and Machine Learning, Engineering Quadrangle, Princeton, NJ 08544（普林斯顿大学统计与机器学习中心助理教授）
VAE-Inf: A statistically interpretable generative paradigm for imbalanced classification
Department of Data Science and Artificial Intelligence（数据科学与人工智能系）；The Hong Kong Polytechnic University（香港理工大学）；Department of Applied Mathematics（应用数学系）
TabularQGAN: A quantum generative model for tabular data synthesis
SAP SE（SAP公司）；BASF Digital Solutions（巴斯夫数字解决方案）；QUTAC Quantum Technology and Application Consortium（QUTAC量子技术与应用联盟）；Technical University of Munich CIT（慕尼黑技术大学CIT）；Ludwig-Maximilians-Universität München（慕尼黑路德维希-马克西米利安大学）；Merck KGaA（默克公司）
cMelGAN: An Efficient Conditional Generative Model Based on Mel Spectrograms
University of Toronto（多伦多大学）
在机器学习领域分析音乐是一个非常困难的问题，需要考虑许多限制。音频数据的性质，其非常高的维度和广泛变化的结构尺度，是它如此难以建模的主要原因之一。机器学习在音乐中有许多应用，例如对一段音乐的情绪进行分类，有条件的音乐生成或流行预测。该项目的目标是开发一个基于Mel声谱图的音乐体裁条件生成模型，并通过将其与使用基于音符表示的现有生成音乐模型进行比较来评估其性能。我们最初实现了一个自回归的、基于RNN的生成模型，称为MelNet。然而，由于其速度慢且输出保真度低，我们决定创建一种新的全卷积架构，该架构基于Mel GAN [4]和条件GAN架构，称为cMel GAN。
Shape-informed cardiac mechanics surrogates in data-scarce regimes via geometric encoding and generative augmentation
MOX Laboratory, Department of Mathematics, Politecnico di Milano（米兰Politecnico大学数学系MOX实验室）；Division 2.2 Process Simulation, Bundesanstalt für Materialforschung und -prüfung (BAM)（材料研究所（BAM）第2.2过程模拟部）；Department of Mathematics, University of Trento（特伦托大学数学系）
Fault Detection in New Wind Turbines with Limited Data by Generative Transfer Learning
School of Engineering and Computer Science, Bern University of Applied Sciences, Biel, Switzerland（工程与计算机科学学院，伯恩应用科学大学，瑞士比尔）；Faculty of Informatics, Università della Svizzera italiana, Lugano, Switzerland（信息学院，瑞士意大利大学，瑞士卢加诺）；Department of Geoscience and Remote Sensing, Delft University of Technology, Delft, The Netherlands（地质与遥感系，代尔夫特理工大学，荷兰代尔夫特）
Contrastive and Generative Graph Convolutional Networks for Graph-based Semi-Supervised Learning
School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）；Faculty of IT, Monash University（莫纳什大学信息技术学院）
Generative Modeling under Non-Monotone MAR Missingness via Approximate Wasserstein Gradient Flows
Department of Mathematics（数学系）；Research Institute for Statistics and Information Science（统计与信息科学研究所）；University of Geneva（日内瓦大学）；University of Hamburg（汉堡大学）
