1,103 research outputs found
Generative models for natural images
Nous traitons de modeĢles geĢneĢratifs construits avec des reĢseaux de neurones dans le contexte de la modeĢlisation dāimages. De nos jours, trois types de modeĢles sont particulieĢrement preĢdominants: les modeĢles aĢ variables latentes, tel que lāauto-encodeur variationnel (VAE), les modeĢles autoreĢgressifs, tel que le reĢseau de neurones reĢcurrent pixel (PixelRNN), et les modeĢles geĢneĢratifs antagonistes (GANs), qui sont des modeĢles aĢ transformation de bruit entraineĢs aĢ lāaide dāun adversaire. Cette theĢse traite de chacun de ces modeĢles.
Le premier chapitre couvre la base des modeĢles geĢneĢratifs, ainsi que les reĢseaux de neurones pro- fonds, qui constituent la technologie principalement utiliseĢe aĢ lāheure actuelle pour lāimpleĢmentation de modeĢles statistiques puissants.
Dans le deuxieĢme chapitre, nous impleĢmentons un auto-encodeur variationnel avec un deĢcodeur auto-reĢgressif. Cela permet de se libeĢrer de lāhypotheĢse dāindeĢpendance des dimensions de sortie du deĢcodeur variationnel, en modeĢlisant une distribution jointe tracĢ§able aĢ la place, et de doter le modeĢle auto-reĢgressif dāun code latent. De plus, notre impleĢmentation a un couĢt computationnel significativement reĢduit, si on le compare aĢ un modeĢle purement auto-reĢgressif ayant les meĢmes hypotheĢses de modeĢlisation et la meĢme performance. Nous deĢcrivons lāespace latent de facĢ§on hieĢrarchique, et montrons de manieĢre qualitative la deĢcomposition seĢmantique des causes latente induites par ce design. Finalement, nous preĢsentons des reĢsultats obtenus avec des jeux de donneĢes standards et deĢmontrant que la performance de notre impleĢmentation est fortement compeĢtitive.
Dans le troisieĢme chapitre, nous preĢsentons une proceĢdure dāentrainement ameĢlioreĢe pour une variante reĢcente de modeĢles geĢneĢratifs antagoniste. Le Ā«Wasserstein GANĀ» minimise la distance, mesureĢe avec la meĢtrique de Wasserstein, entre la distribution reĢelle et celle geĢneĢreĢe par le modeĢle, ce qui le rend plus facile aĢ entrainer quāun GAN avec un objectif minimax. Cependant, en fonction des parameĢtres, il preĢsente toujours des cas dāeĢchecs avec certain modes dāentrainement. Nous avons deĢcouvert que le coupable est le coupage des poids, et nous le remplacĢ§ons par une peĢnaliteĢ sur la norme des gradients. Ceci ameĢliore et stabilise lāentrainement, et ce sur diffeĢrents types du parameĢtres (incluant des modeĢles de langue sur des donneĢes discreĢtes), et permet de geĢneĢrer des eĢchantillons de haute qualiteĢs sur CIFAR-10 et LSUN bedrooms.
Finalement, dans le quatrieĢme chapitre, nous consideĢrons lāusage de modeĢles geĢneĢratifs modernes comme modeĢles de normaliteĢ dans un cadre de deĢtection hors-distribution Ā«zero-shotĀ». Nous avons eĢvalueĢ certains des modeĢles preĢceĢdemment preĢsenteĢs dans la theĢse, et avons trouveĢ que les VAEs sont les plus prometteurs, bien que leurs performances laissent encore un large place aĢ lāameĢlioration. Cette partie de la theĢse constitue un travail en cours.
Nous concluons en reĢpeĢtant lāimportance des modeĢles geĢneĢratifs dans le deĢveloppement de lāintelligence artificielle et mentionnons quelques deĢfis futurs.We discuss modern generative modelling of natural images based on neural networks. Three varieties of such models are particularly predominant at the time of writing: latent variable models such as variational autoencoders (VAE), autoregressive models such as pixel recurrent neural networks (PixelRNN), and generative adversarial networks (GAN), which are noise-transformation models trained with an adversary. This thesis touches on all three kinds.
The first chapter covers background on generative models, along with relevant discussions about deep neural networks, which are currently the dominant technology for implementing powerful statistical models.
In the second chapter, we implement variational autoencoders with autoregressive decoders. This removes the strong assumption of output dimensions being conditionally independent in variational autoencoders, instead tractably modelling a joint distribution, while also endowing autoregressive models with a latent code. Additionally, this model has significantly reduced computational cost compared to that of a purely autoregressive model with similar modelling assumptions and performance. We express the latent space as a hierarchy, and qualitatively demonstrate the semantic decomposition of latent causes induced by this design. Finally, we present results on standard datasets that demonstrate strongly competitive performance.
In the third chapter, we present an improved training procedure for a recent variant on generative adversarial networks. Wasserstein GANs minimize the Earth-Moverās distance between the real and generated distributions and have been shown to be much easier to train than with the standard minimax objective of GANs. However, they still exhibit some failure modes in training for some settings. We identify weight clipping as a culprit and replace it with a penalty on the gradient norm. This improves training further, and we demonstrate stability on a wide variety of settings (including language models over discrete data), and samples of high quality on the CIFAR-10 and LSUN bedrooms datasets.
Finally, in the fourth chapter, we present work in development, where we consider the use of modern generative models as normality models in a zero-shot out-of-distribution detection setting. We evaluate some of the models we have discussed previously in the thesis, and find that VAEs are the most promising, although their overall performance leaves a lot of room for improvement.
We conclude by reiterating the significance of generative modelling in the development of artificial intelligence, and mention some of the challenges ahead
BaCO: A Fast and Portable Bayesian Compiler Optimization Framework
We introduce the Bayesian Compiler Optimization framework (BaCO), a general
purpose autotuner for modern compilers targeting CPUs, GPUs, and FPGAs. BaCO
provides the flexibility needed to handle the requirements of modern autotuning
tasks. Particularly, it deals with permutation, ordered, and continuous
parameter types along with both known and unknown parameter constraints. To
reason about these parameter types and efficiently deliver high-quality code,
BaCO uses Bayesian optimiza tion algorithms specialized towards the autotuning
domain. We demonstrate BaCO's effectiveness on three modern compiler systems:
TACO, RISE & ELEVATE, and HPVM2FPGA for CPUs, GPUs, and FPGAs respectively. For
these domains, BaCO outperforms current state-of-the-art autotuners by
delivering on average 1.36x-1.56x faster code with a tiny search budget, and
BaCO is able to reach expert-level performance 2.9x-3.9x faster
Accelerate Microstructure Evolution Simulation Using Graph Neural Networks with Adaptive Spatiotemporal Resolution
Surrogate models driven by sizeable datasets and scientific machine-learning
methods have emerged as an attractive microstructure simulation tool with the
potential to deliver predictive microstructure evolution dynamics with huge
savings in computational costs. Taking 2D and 3D grain growth simulations as an
example, we present a completely overhauled computational framework based on
graph neural networks with not only excellent agreement to both the ground
truth phase-field methods and theoretical predictions, but enhanced accuracy
and efficiency compared to previous works based on convolutional neural
networks. These improvements can be attributed to the graph representation,
both improved predictive power and a more flexible data structure amenable to
adaptive mesh refinement. As the simulated microstructures coarsen, our method
can adaptively adopt remeshed grids and larger timesteps to achieve further
speedup. The data-to-model pipeline with training procedures together with the
source codes are provided.Comment: 28 pages, 11 figure
Generative Adversarial Networks (GANs): Challenges, Solutions, and Future Directions
Generative Adversarial Networks (GANs) is a novel class of deep generative
models which has recently gained significant attention. GANs learns complex and
high-dimensional distributions implicitly over images, audio, and data.
However, there exists major challenges in training of GANs, i.e., mode
collapse, non-convergence and instability, due to inappropriate design of
network architecture, use of objective function and selection of optimization
algorithm. Recently, to address these challenges, several solutions for better
design and optimization of GANs have been investigated based on techniques of
re-engineered network architectures, new objective functions and alternative
optimization algorithms. To the best of our knowledge, there is no existing
survey that has particularly focused on broad and systematic developments of
these solutions. In this study, we perform a comprehensive survey of the
advancements in GANs design and optimization solutions proposed to handle GANs
challenges. We first identify key research issues within each design and
optimization technique and then propose a new taxonomy to structure solutions
by key research issues. In accordance with the taxonomy, we provide a detailed
discussion on different GANs variants proposed within each solution and their
relationships. Finally, based on the insights gained, we present the promising
research directions in this rapidly growing field.Comment: 42 pages, Figure 13, Table
A Data-Driven Evolutionary Transfer Optimization for Expensive Problems in Dynamic Environments
Many real-world problems are usually computationally costly and the objective
functions evolve over time. Data-driven, a.k.a. surrogate-assisted,
evolutionary optimization has been recognized as an effective approach for
tackling expensive black-box optimization problems in a static environment
whereas it has rarely been studied under dynamic environments. This paper
proposes a simple but effective transfer learning framework to empower
data-driven evolutionary optimization to solve dynamic optimization problems.
Specifically, it applies a hierarchical multi-output Gaussian process to
capture the correlation between data collected from different time steps with a
linearly increased number of hyperparameters. Furthermore, an adaptive source
task selection along with a bespoke warm staring initialization mechanisms are
proposed to better leverage the knowledge extracted from previous optimization
exercises. By doing so, the data-driven evolutionary optimization can jump
start the optimization in the new environment with a strictly limited
computational budget. Experiments on synthetic benchmark test problems and a
real-world case study demonstrate the effectiveness of our proposed algorithm
against nine state-of-the-art peer algorithms
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