scholarly journals The advanced CAD model of a cargo bike

2021 ◽  
Author(s):  
Sebastian Rzydzik ◽  
Marcin Adamiec
Keyword(s):  
Programming Languages ◽  
Parametric Model ◽  
Generative Models ◽  
Generative Model ◽  
Simple Object ◽  
Cad Model ◽  
The Right ◽  
Cargo Bike

This article describe a way to create generative model at the example of cargo bike model, which is very simple object which can be used to present all important rules applied during crating generative models. Great attention was paid to the issue of model parametrization which is elementary thing in all modelling. Besides these aspects, it is also shown how to transform parametric model into generative model using programming languages. In the last part of article was included tests of correct working of model which focused also to the right position cyclist on the bike and shows how model of cargo bike could change its sizes thanks to correctly created generative model.

scholarly journals Generative models, language and active inference

2020 ◽  
Author(s):  
Karl Friston ◽  
Thomas Parr ◽  
Yan Yufik ◽  
Noor Sajid ◽  
Cathy J. Price ◽  
...  
Keyword(s):  
Language Processing ◽  
Hierarchical Models ◽  
Message Passing ◽  
Generative Models ◽  
Generative Model ◽  
Active Inference ◽  
Factorial Structure ◽  
The Rich ◽  
The Right

This paper presents a biologically plausible generative model and inference scheme that is capable of simulating the generation and comprehension of language, when synthetic subjects talk to each other. Building on active inference formulations of dyadic interactions, we simulate linguistic exchange to explore generative models that support dialogues. These models employ high-order interactions among abstract (discrete) states in deep (hierarchical) models. The sequential nature of language processing mandates generative models with a particular factorial structure—necessary to accommodate the rich combinatorics of language. We illustrate this by simulating a synthetic subject who can play the ‘Twenty Questions’ game. In this game, synthetic subjects take the role of the questioner or answerer, using the same generative model. This simulation setup is used to illustrate some key architectural points and demonstrate that many behavioural and neurophysiological correlates of language processing emerge under variational (marginal) message passing, given the right kind of generative model. For example, we show that theta-gamma coupling is an emergent property of belief updating, when listening to another.

Equivariant Adversarial Network for Image-to-image Translation

2021 ◽  
Vol 17 (2s) ◽  
pp. 1-14
Author(s):  
Masoumeh Zareapoor ◽  
Jie Yang
Keyword(s):  
State Of The Art ◽  
Generative Models ◽  
Generative Model ◽  
Target Domain ◽  
Adversarial Network ◽  
Proposed Model ◽  
Image Translation ◽  
Great Performance ◽  
Representative Model ◽  
The Ideal

Image-to-Image translation aims to learn an image from a source domain to a target domain. However, there are three main challenges, such as lack of paired datasets, multimodality, and diversity, that are associated with these problems and need to be dealt with. Convolutional neural networks (CNNs), despite of having great performance in many computer vision tasks, they fail to detect the hierarchy of spatial relationships between different parts of an object and thus do not form the ideal representative model we look for. This article presents a new variation of generative models that aims to remedy this problem. We use a trainable transformer, which explicitly allows the spatial manipulation of data within training. This differentiable module can be augmented into the convolutional layers in the generative model, and it allows to freely alter the generated distributions for image-to-image translation. To reap the benefits of proposed module into generative model, our architecture incorporates a new loss function to facilitate an effective end-to-end generative learning for image-to-image translation. The proposed model is evaluated through comprehensive experiments on image synthesizing and image-to-image translation, along with comparisons with several state-of-the-art algorithms.

scholarly journals Monte Carlo and Reconstruction Membership Inference Attacks against Generative Models

2019 ◽  
Vol 2019 (4) ◽  
pp. 232-249 ◽  
Author(s):  
Benjamin Hilprecht ◽  
Martin Härterich ◽  
Daniel Bernau
Keyword(s):  
Data Privacy ◽  
Information Leakage ◽  
Generative Models ◽  
Generative Model ◽  
Training Data ◽  
Generative Adversarial Networks ◽  
Data Sets ◽  
Success Rates ◽  
Model Quality ◽  
Type Formalization

Abstract We present two information leakage attacks that outperform previous work on membership inference against generative models. The first attack allows membership inference without assumptions on the type of the generative model. Contrary to previous evaluation metrics for generative models, like Kernel Density Estimation, it only considers samples of the model which are close to training data records. The second attack specifically targets Variational Autoencoders, achieving high membership inference accuracy. Furthermore, previous work mostly considers membership inference adversaries who perform single record membership inference. We argue for considering regulatory actors who perform set membership inference to identify the use of specific datasets for training. The attacks are evaluated on two generative model architectures, Generative Adversarial Networks (GANs) and Variational Autoen-coders (VAEs), trained on standard image datasets. Our results show that the two attacks yield success rates superior to previous work on most data sets while at the same time having only very mild assumptions. We envision the two attacks in combination with the membership inference attack type formalization as especially useful. For example, to enforce data privacy standards and automatically assessing model quality in machine learning as a service setups. In practice, our work motivates the use of GANs since they prove less vulnerable against information leakage attacks while producing detailed samples.

scholarly journals Towards Consistent Variational Auto-Encoding (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13869-13870
Author(s):  
Yijing Liu ◽  
Shuyu Lin ◽  
Ronald Clark
Keyword(s):  
Original Data ◽  
Generative Models ◽  
Generative Model ◽  
Approximate Inference ◽  
Inference Model ◽  
Approach To Learning ◽  
Successful Approach ◽  
Data Point ◽  
Reconstructed Data

Variational autoencoders (VAEs) have been a successful approach to learning meaningful representations of data in an unsupervised manner. However, suboptimal representations are often learned because the approximate inference model fails to match the true posterior of the generative model, i.e. an inconsistency exists between the learnt inference and generative models. In this paper, we introduce a novel consistency loss that directly requires the encoding of the reconstructed data point to match the encoding of the original data, leading to better representations. Through experiments on MNIST and Fashion MNIST, we demonstrate the existence of the inconsistency in VAE learning and that our method can effectively reduce such inconsistency.

scholarly journals Robust Data Programming with Precision-guided Labeling Functions

2020 ◽  
Vol 34 (04) ◽  
pp. 3397-3404 ◽  
Author(s):  
Oishik Chatterjee ◽  
Ganesh Ramakrishnan ◽  
Sunita Sarawagi
Keyword(s):  
Generative Models ◽  
Generative Model ◽  
Learning Models ◽  
Training Process ◽  
Programming Paradigm ◽  
Learning Rates ◽  
Elegant Method ◽  
Accuracy Constraints ◽  
Existing Data ◽  
Noisy Labels

Scarcity of labeled data is a bottleneck for supervised learning models. A paradigm that has evolved for dealing with this problem is data programming. An existing data programming paradigm allows human supervision to be provided as a set of discrete labeling functions (LF) that output possibly noisy labels to input instances and a generative model for consolidating the weak labels. We enhance and generalize this paradigm by supporting functions that output a continuous score (instead of a hard label) that noisily correlates with labels. We show across five applications that continuous LFs are more natural to program and lead to improved recall. We also show that accuracy of existing generative models is unstable with respect to initialization, training epochs, and learning rates. We give control to the data programmer to guide the training process by providing intuitive quality guides with each LF. We propose an elegant method of incorporating these guides into the generative model. Our overall method, called CAGE, makes the data programming paradigm more reliable than other tricks based on initialization, sign-penalties, or soft-accuracy constraints.

scholarly journals Learning effective physical laws for generating cosmological hydrodynamics with Lagrangian deep learning

2021 ◽  
Vol 118 (16) ◽  
pp. e2020324118
Author(s):  
Biwei Dai ◽  
Uroš Seljak
Keyword(s):  
Dark Matter ◽  
Deep Learning ◽  
Generative Models ◽  
Generative Model ◽  
Effective Theory ◽  
High Dimensions ◽  
Wide Range ◽  
Hydrodynamical Simulations ◽  
Physical Laws ◽  
Very High

The goal of generative models is to learn the intricate relations between the data to create new simulated data, but current approaches fail in very high dimensions. When the true data-generating process is based on physical processes, these impose symmetries and constraints, and the generative model can be created by learning an effective description of the underlying physics, which enables scaling of the generative model to very high dimensions. In this work, we propose Lagrangian deep learning (LDL) for this purpose, applying it to learn outputs of cosmological hydrodynamical simulations. The model uses layers of Lagrangian displacements of particles describing the observables to learn the effective physical laws. The displacements are modeled as the gradient of an effective potential, which explicitly satisfies the translational and rotational invariance. The total number of learned parameters is only of order 10, and they can be viewed as effective theory parameters. We combine N-body solver fast particle mesh (FastPM) with LDL and apply it to a wide range of cosmological outputs, from the dark matter to the stellar maps, gas density, and temperature. The computational cost of LDL is nearly four orders of magnitude lower than that of the full hydrodynamical simulations, yet it outperforms them at the same resolution. We achieve this with only of order 10 layers from the initial conditions to the final output, in contrast to typical cosmological simulations with thousands of time steps. This opens up the possibility of analyzing cosmological observations entirely within this framework, without the need for large dark-matter simulations.

scholarly journals An In-Depth Comparison Of Software Frameworks For Developing Desktop Applications Using Web Technologies

2019 ◽  
Vol 54 (4) ◽  
Author(s):  
Zinah Hussein Toman ◽  
Sarah Hussein Toman ◽  
Manar Joundy Hazar
Keyword(s):  
Programming Languages ◽  
System Integration ◽  
Web Pages ◽  
Web Browser ◽  
Web Technologies ◽  
Software Products ◽  
System User ◽  
Scripting Language ◽  
The Right ◽  
The Internet Of Things

Today JavaScript is one of the most popular and fastest growing programming languages. Initially designed as a web browser scripting language, its adoption has reached beyond web pages: the Internet of Things, mobile and desktop applications. Lately, an increased interest can be observed over developing desktop software using JavaScript and other web technologies such as HTML and CSS. Many popular software products followed this path: Skype, Visual Studio Code, Atom, Brackets, Light Table, Microsoft Teams, Microsoft SQL Operations Studio, GitHub Desktop, Signal, etc. The aim of this article is to aid developers to choose the right framework for their needs, through a comprehensive side-by-side comparison of Electron and NW.js, the two frameworks available for developing desktop software with JavaScript, HTML and CSS. Electron despite being a younger project. It was concluded in this article that this software framework outperforms NW.js in the matter of capabilities on most areas such as file system, user interface, system integration and multimedia; except printing. However, NW.js is easier to use and debug.

scholarly journals Graph-based Genetic Algorithm and Generative Model/Monte Carlo Tree Search for the Exploration of Chemical Space

2018 ◽  
Author(s):  
Jan H. Jensen
Keyword(s):  
Genetic Algorithm ◽  
Monte Carlo ◽  
Chemical Space ◽  
Generative Models ◽  
Generative Model ◽  
Small Data ◽  
Data Sets ◽  
Tree Search ◽  
Monte Carlo Tree Search ◽  
Small Data Sets

This paper presents a comparison of a graph-based genetic algorithm (GB-GA) and machine learning (ML) results for the optimisation of logP values with a constraint for synthetic accessibility and shows that GA is as good or better than the ML approaches for this particular property. The molecules found by GB-GA bear little resemblance to the molecules used to construct the initial mating pool, indicating that the GB-GA approach can traverse a relatively large distance in chemical space using relatively few (50) generations. The paper also introduces a new non-ML graph-based generative model (GB-GM) that can be parameterized using very small data sets and combined with a Monte Carlo tree search (MCTS) algorithm. The results are comparable to previously published results (Sci. Technol. Adv. Mater. 2017, 18, 972-976) using a recurrent neural network (RNN) generative model, while the GB-GM-based method is orders of magnitude faster. The MCTS results seem more dependent on the composition of the training set than the GA approach for this particular property. Our results suggest that the performance of new ML-based generative models should be compared to more traditional, and often simpler, approaches such a GA.

scholarly journals One shot generalization in humans revealed through a drawing task

2021 ◽  
Author(s):  
Henning Tiedemann ◽  
Yaniv Morgenstern ◽  
Filipp Schmidt ◽  
Roland W. Fleming
Keyword(s):  
Generative Models ◽  
Generative Model ◽  
Shape Features ◽  
Novel Object ◽  
Drawing Task ◽  
Visual Concepts ◽  
Part Structure

Humans have the striking ability to learn and generalize new visual concepts from just a single exemplar. We suggest that when presented with a novel object, observers identify its significant features and infer a generative model of its shape, allowing them to mentally synthesize plausible variants. To test this, we showed participants abstract 2D shapes ("Exemplars") and asked them to draw new objects ("Variations") belonging to the same class. We show that this procedure created genuine novel categories. In line with our hypothesis, particular features of each Exemplar were preserved in its Variations and there was striking agreement between participants about which shape features were most distinctive. Also, we show that strategies to create Variations were strongly driven by part structure: new objects typically modified individual parts (e.g., limbs) of the Exemplar, often preserving part order, sometimes altering it. Together, our findings suggest that sophisticated internal generative models are key to how humans analyze and generalize from single exemplars.

scholarly journals Generative Model for Skeletal Human Movements Based on Conditional DC-GAN Applied to Pseudo-Images

Algorithms ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 319
Author(s):  
Wang Xi ◽  
Guillaume Devineau ◽  
Fabien Moutarde ◽  
Jie Yang
Keyword(s):  
Data Augmentation ◽  
Human Movement ◽  
Generative Models ◽  
Generative Model ◽  
Great Success ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Human Movements ◽  
Action Type ◽  
Skeleton Image

Generative models for images, audio, text, and other low-dimension data have achieved great success in recent years. Generating artificial human movements can also be useful for many applications, including improvement of data augmentation methods for human gesture recognition. The objective of this research is to develop a generative model for skeletal human movement, allowing to control the action type of generated motion while keeping the authenticity of the result and the natural style variability of gesture execution. We propose to use a conditional Deep Convolutional Generative Adversarial Network (DC-GAN) applied to pseudo-images representing skeletal pose sequences using tree structure skeleton image format. We evaluate our approach on the 3D skeletal data provided in the large NTU_RGB+D public dataset. Our generative model can output qualitatively correct skeletal human movements for any of the 60 action classes. We also quantitatively evaluate the performance of our model by computing Fréchet inception distances, which shows strong correlation to human judgement. To the best of our knowledge, our work is the first successful class-conditioned generative model for human skeletal motions based on pseudo-image representation of skeletal pose sequences.

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