This document discusses ultra-large scale integration (ULSI) circuits and semiconductor manufacturing processes. It introduces ULSI and its applications. It then summarizes the key steps in the IC fabrication process, including crystal growth, thin film deposition, oxidation, etching, lithography and metallization. Finally, it discusses future trends in ULSI, such as following Moore's Law to continue increasing transistor density, performance and functionality through advances in device physics, materials and technology to shrink dimensions below physical limits.
Categorical reparameterization with gumbel softmaxぱんいち すみもと
This document discusses two semi-supervised deep generative models:
(1) A VAE model (M1) that learns latent representations from both labeled and unlabeled data.
(2) An extended VAE model (M2) that uses Gumbel-Softmax to learn discrete latent variables from unlabeled data.
Combining M1 and M2 (M1+M2) allows learning of both continuous and discrete disentangled representations in an end-to-end manner, achieving better performance than the individual models. The document provides technical details on how both models work and are combined.
This document discusses ultra-large scale integration (ULSI) circuits and semiconductor manufacturing processes. It introduces ULSI and its applications. It then summarizes the key steps in the IC fabrication process, including crystal growth, thin film deposition, oxidation, etching, lithography and metallization. Finally, it discusses future trends in ULSI, such as following Moore's Law to continue increasing transistor density, performance and functionality through advances in device physics, materials and technology to shrink dimensions below physical limits.
Categorical reparameterization with gumbel softmaxぱんいち すみもと
This document discusses two semi-supervised deep generative models:
(1) A VAE model (M1) that learns latent representations from both labeled and unlabeled data.
(2) An extended VAE model (M2) that uses Gumbel-Softmax to learn discrete latent variables from unlabeled data.
Combining M1 and M2 (M1+M2) allows learning of both continuous and discrete disentangled representations in an end-to-end manner, achieving better performance than the individual models. The document provides technical details on how both models work and are combined.
This document discusses several semi-supervised deep generative models for multimodal data, including the Semi-Supervised Multimodal Variational AutoEncoder (SS-MVAE), Semi-Supervised Hierarchical Multimodal Variational AutoEncoder (SS-HMVAE), and their training procedures. The SS-MVAE extends the Joint Multimodal Variational Autoencoder (JMVAE) to semi-supervised learning. The SS-HMVAE introduces auxiliary variables to model dependencies between modalities more flexibly. Both models maximize a variational lower bound with supervised and unsupervised objectives. The document provides technical details of the generative processes, variational approximations, and optimization of these semi-supervised deep generative models.
The document contains charts and graphs showing the results of experiments comparing different systems for trustworthiness analysis of web search results. One chart shows that a combination of the authors' system and Google performed better than Google alone across 10 different query categories. Another chart shows the average precision of 4 different algorithms for determining the credibility of a data pair.
1. The document discusses knowledge representation and deep learning techniques for knowledge graphs, including embedding models like TransE, TransH, and neural network models.
2. It provides an overview of methods for tasks like link prediction, question answering, and language modeling using recurrent neural networks and memory networks.
3. The document references several papers on knowledge graph embedding models and their applications to natural language processing tasks.
This document summarizes two papers presented at NIPS 2018 on anomaly detection and out-of-distribution detection. The first paper proposes a simple unified framework using geometric transformations and Dirichlet density estimation to detect anomalies and adversarial examples. The second paper introduces a method that uses an ensemble of neural networks to detect out-of-distribution samples and adversarial attacks with state-of-the-art performance on CIFAR-10, SVHN and FGSM attacks. It also explores applications to class-incremental learning.
[DL輪読会]Recent Advances in Autoencoder-Based Representation LearningDeep Learning JP
1. Recent advances in autoencoder-based representation learning include incorporating meta-priors to encourage disentanglement and using rate-distortion and rate-distortion-usefulness tradeoffs to balance compression and reconstruction.
2. Variational autoencoders introduce priors to disentangle latent factors, but recent work aggregates posteriors to directly encourage disentanglement.
3. The rate-distortion framework balances the rate of information transmission against reconstruction distortion, while rate-distortion-usefulness also considers downstream task usefulness.
[DL輪読会]A Style-Based Generator Architecture for Generative Adversarial NetworksDeep Learning JP
This document discusses style-based generative adversarial networks and techniques used in them. It introduces adaptive instance normalization (AdaIN) which aligns the mean and variance of features to match a target style. It also discusses mixing regularization which combines styles at the latent space level and perceptual path length which measures diversity of generated images.
[DL輪読会]Weakly-Supervised Disentanglement Without CompromisesDeep Learning JP
1. The document presents a method for deep learning using variational autoencoders that model the relationship between pairs of data points (x1, x2).
2. It introduces variables to represent latent vectors z and z~ that are used to generate x1 and x2, as well as a subset S of dimensions that relate x1 and x2.
3. The method works by training an encoder qφ(z|x) to approximate a prior p(z) and maximize the likelihood of generating x1 and x2 from their respective latent representations, while minimizing the KL divergence between the encoder and prior.
This document discusses several semi-supervised deep generative models for multimodal data, including the Semi-Supervised Multimodal Variational AutoEncoder (SS-MVAE), Semi-Supervised Hierarchical Multimodal Variational AutoEncoder (SS-HMVAE), and their training procedures. The SS-MVAE extends the Joint Multimodal Variational Autoencoder (JMVAE) to semi-supervised learning. The SS-HMVAE introduces auxiliary variables to model dependencies between modalities more flexibly. Both models maximize a variational lower bound with supervised and unsupervised objectives. The document provides technical details of the generative processes, variational approximations, and optimization of these semi-supervised deep generative models.
The document contains charts and graphs showing the results of experiments comparing different systems for trustworthiness analysis of web search results. One chart shows that a combination of the authors' system and Google performed better than Google alone across 10 different query categories. Another chart shows the average precision of 4 different algorithms for determining the credibility of a data pair.
1. The document discusses knowledge representation and deep learning techniques for knowledge graphs, including embedding models like TransE, TransH, and neural network models.
2. It provides an overview of methods for tasks like link prediction, question answering, and language modeling using recurrent neural networks and memory networks.
3. The document references several papers on knowledge graph embedding models and their applications to natural language processing tasks.
This document summarizes two papers presented at NIPS 2018 on anomaly detection and out-of-distribution detection. The first paper proposes a simple unified framework using geometric transformations and Dirichlet density estimation to detect anomalies and adversarial examples. The second paper introduces a method that uses an ensemble of neural networks to detect out-of-distribution samples and adversarial attacks with state-of-the-art performance on CIFAR-10, SVHN and FGSM attacks. It also explores applications to class-incremental learning.
[DL輪読会]Recent Advances in Autoencoder-Based Representation LearningDeep Learning JP
1. Recent advances in autoencoder-based representation learning include incorporating meta-priors to encourage disentanglement and using rate-distortion and rate-distortion-usefulness tradeoffs to balance compression and reconstruction.
2. Variational autoencoders introduce priors to disentangle latent factors, but recent work aggregates posteriors to directly encourage disentanglement.
3. The rate-distortion framework balances the rate of information transmission against reconstruction distortion, while rate-distortion-usefulness also considers downstream task usefulness.
[DL輪読会]A Style-Based Generator Architecture for Generative Adversarial NetworksDeep Learning JP
This document discusses style-based generative adversarial networks and techniques used in them. It introduces adaptive instance normalization (AdaIN) which aligns the mean and variance of features to match a target style. It also discusses mixing regularization which combines styles at the latent space level and perceptual path length which measures diversity of generated images.
[DL輪読会]Weakly-Supervised Disentanglement Without CompromisesDeep Learning JP
1. The document presents a method for deep learning using variational autoencoders that model the relationship between pairs of data points (x1, x2).
2. It introduces variables to represent latent vectors z and z~ that are used to generate x1 and x2, as well as a subset S of dimensions that relate x1 and x2.
3. The method works by training an encoder qφ(z|x) to approximate a prior p(z) and maximize the likelihood of generating x1 and x2 from their respective latent representations, while minimizing the KL divergence between the encoder and prior.
The document lists 13 famous gardens around the world, including Versailles in Paris, France; The Garden of Cosmic Speculation in Scotland, UK; Boboli Gardens in Florence, Italy; Rikugien Gardens in Tokyo, Japan; Claude Monet Gardens in Giverny, West Paris, France; Butchart Gardens in Victoria, BC, Canada; Kirstenbosch Botanical Gardens in Cape Town, South Africa; Guarapiranga Sacred Grounds in Sao Paulo, Brazil; Yu Gardens in Shanghai, China; Exbury Gardens in New Forest, England; Keukenhof Gardens in Holland; Mirabell Garden in Salzburg, Austria; and Zen Garden of Ryoan Temple in Kyoto
Erik Johansson is a 23-year-old Swedish student who creates impossible pictures that play tricks on the viewer's mind. His pictures use optical illusions and unusual perspectives to make scenes and objects appear in ways that defy perception and logic.
