ICASSP 2019音声&音響論文読み会(https://connpass.com/event/128527/)での発表資料です。
AASP (Audio and Acoustic Signal Processing) 分野の紹介と、ICASSP 2019での動向を紹介しています。#icassp2019jp
ICASSP 2019音声&音響論文読み会(https://connpass.com/event/128527/)での発表資料です。
AASP (Audio and Acoustic Signal Processing) 分野の紹介と、ICASSP 2019での動向を紹介しています。#icassp2019jp
My MPEG life: MPEG-2, MPEG-4, H264/AVC and H.265/HEVCOsaka University
(in Japanese), I was invited to a honorable speech hosted by Skyperfect TV. In that speech I'm recalling my life in MPEG. The topics cover mainly MPEG-4 and H.264.
「Ustream(ユーストリーム)で広報力UP講座(主催:徳島県・とくしま公衆無線LAN推進協議会)」において「地域におけるインターネットライブ配信(Ustream・ニコニコ生放送・YouTube)の活用」をテーマとしたお話を2013年03月12日(火)徳島県庁会議室にてさせて頂きました。
Ustream(ユーストリーム)で広報力UP講座(徳島県・とくしま公衆無線LAN推進協議会) | ノダタケオ on WEB
http://changmedia.jp/archives/115
Transformer based approaches for visual representation learningRyohei Suzuki
1) Transformer-based approaches for visual representation learning such as Vision Transformers (ViTs) have shown promising performance compared to CNNs on image classification tasks.
2) A pure Transformer architecture pre-trained on a very large dataset like JFT-300M can outperform modern CNNs without any convolutions.
3) Self-supervised pre-training methods like DINO that leverage knowledge distillation have been shown to obtain comparable performance to supervised pre-training of ViTs using only unlabeled ImageNet data.
Paper memo: persistent homology on biological problemsRyohei Suzuki
Shnier et al., Persistent homology analysis of brain transcriptome data in autism
Qaiser et al., Fast and accurate tumor segmentation of histology images using persistent homology and deep convolutional features
My MPEG life: MPEG-2, MPEG-4, H264/AVC and H.265/HEVCOsaka University
(in Japanese), I was invited to a honorable speech hosted by Skyperfect TV. In that speech I'm recalling my life in MPEG. The topics cover mainly MPEG-4 and H.264.
「Ustream(ユーストリーム)で広報力UP講座(主催:徳島県・とくしま公衆無線LAN推進協議会)」において「地域におけるインターネットライブ配信(Ustream・ニコニコ生放送・YouTube)の活用」をテーマとしたお話を2013年03月12日(火)徳島県庁会議室にてさせて頂きました。
Ustream(ユーストリーム)で広報力UP講座(徳島県・とくしま公衆無線LAN推進協議会) | ノダタケオ on WEB
http://changmedia.jp/archives/115
Transformer based approaches for visual representation learningRyohei Suzuki
1) Transformer-based approaches for visual representation learning such as Vision Transformers (ViTs) have shown promising performance compared to CNNs on image classification tasks.
2) A pure Transformer architecture pre-trained on a very large dataset like JFT-300M can outperform modern CNNs without any convolutions.
3) Self-supervised pre-training methods like DINO that leverage knowledge distillation have been shown to obtain comparable performance to supervised pre-training of ViTs using only unlabeled ImageNet data.
Paper memo: persistent homology on biological problemsRyohei Suzuki
Shnier et al., Persistent homology analysis of brain transcriptome data in autism
Qaiser et al., Fast and accurate tumor segmentation of histology images using persistent homology and deep convolutional features
Paper memo: Optimal-Transport Analysis of Single-Cell Gene Expression Identif...Ryohei Suzuki
This document summarizes a study that uses single-cell RNA sequencing data from mouse embryonic fibroblast cells undergoing reprogramming to induced pluripotent stem cells over 18 days. The study develops a new computational method called Waddington-optimal transport that models cell differentiation as an optimal transport problem to infer developmental trajectories and ancestor-descendant relationships between cells across time points. Applying this method to the reprogramming data revealed multiple cell fates including induced pluripotent stem cells, stromal cells, and trophoblast-like cells. It also identified transcription factors and signaling pathways important for reprogramming. The method provides a novel way to systematically analyze densely sampled temporal single-cell gene expression data.
Basic Concepts of Entanglement MeasuresRyohei Suzuki
1. Entanglement measures quantify the amount of entanglement in a quantum state. Measures must satisfy properties like monotonicity under local operations and classical communication.
2. Operational measures include distillable entanglement, which is the optimal rate of maximally entangled states that can be distilled, and entanglement cost, which is the number of maximally entangled states needed to create a given state.
3. Computable measures for mixed states include concurrence and negativity. Concurrence provides an explicit formula for entanglement of formation of qubit pairs.
Disentangled Representation Learning of Deep Generative ModelsRyohei Suzuki
This document discusses disentangled representation learning in deep generative models. It explains that generative models can generate realistic images but it is difficult to control specific attributes of the generated images. Recent research aims to learn disentangled representations where each latent variable corresponds to an independent perceptual factor, such as object pose or color. Methods described include InfoGAN, β-VAE, spatial conditional batch normalization, hierarchical latent variables, and StyleGAN's hierarchical modulation approach. Measuring entanglement through perceptual path length and linear separability is also discussed. The document suggests disentangled representation learning could help applications in biology and medicine by providing better explanatory variables for complex phenomena.
Ryohei Suzuki and Takeo Igarashi, Collaborative 3D Modeling by the Crowd, in Proceedings of the 43rd International Conference on Graphics, Visualization & Human-computer Interaction (GI 2017)
The document summarizes four presentations from the SIGGRAPH Seminar 2014 session on Shape Collection:
1. "Meta-representations of Shape Families" by Nar Fish et al. which analyzes shape families by computing probability distributions of relations between segmented parts.
2. "Organizing Heterogeneous Scene Collections through Contextual Focal Points" by Kai Xu et al. which extracts focal points from 3D indoor scenes to cluster them.
3. "Geometry and Context for Semantic Correspondences and Functionality Recognition in Man-made 3D Shapes" by Hamid Laga et al. which uses a graph representation and context-aware similarities to find semantic correspondences between parts.
Brief Introduction to Recent Spatial InterfacesRyohei Suzuki
This document discusses recent approaches to spatial interfaces. It identifies three main approaches: 1) Materialization of an alternative world using optical, acoustic, and haptic techniques, 2) Extension of spatial information and cognition by utilizing hidden information and changing perspectives, and 3) Improvement of real-world values such as pedestrian traffic control. Several examples are provided for each approach, including CAVE, IllumiRoom, Oculus Rift, and systems that simulate acoustics, add augmented surfaces, or control pedestrian flow.