【DL輪読会】Efficiently Modeling Long Sequences with Structured State SpacesDeep Learning JP
This document summarizes a research paper on modeling long-range dependencies in sequence data using structured state space models and deep learning. The proposed S4 model (1) derives recurrent and convolutional representations of state space models, (2) improves long-term memory using HiPPO matrices, and (3) efficiently computes state space model convolution kernels. Experiments show S4 outperforms existing methods on various long-range dependency tasks, achieves fast and memory-efficient computation comparable to efficient Transformers, and performs competitively as a general sequence model.
AAAI2023「Are Transformers Effective for Time Series Forecasting?」と、HuggingFace「Yes, Transformers are Effective for Time Series Forecasting (+ Autoformer)」の紹介です。
The document discusses distances between data and similarity measures in data analysis. It introduces the concept of distance between data as a quantitative measure of how different two data points are, with smaller distances indicating greater similarity. Distances are useful for tasks like clustering data, detecting anomalies, data recognition, and measuring approximation errors. The most common distance measure, Euclidean distance, is explained for vectors of any dimension using the concept of norm from geometry. Caution is advised when calculating distances between data with differing scales.
【DL輪読会】Efficiently Modeling Long Sequences with Structured State SpacesDeep Learning JP
This document summarizes a research paper on modeling long-range dependencies in sequence data using structured state space models and deep learning. The proposed S4 model (1) derives recurrent and convolutional representations of state space models, (2) improves long-term memory using HiPPO matrices, and (3) efficiently computes state space model convolution kernels. Experiments show S4 outperforms existing methods on various long-range dependency tasks, achieves fast and memory-efficient computation comparable to efficient Transformers, and performs competitively as a general sequence model.
AAAI2023「Are Transformers Effective for Time Series Forecasting?」と、HuggingFace「Yes, Transformers are Effective for Time Series Forecasting (+ Autoformer)」の紹介です。
The document discusses distances between data and similarity measures in data analysis. It introduces the concept of distance between data as a quantitative measure of how different two data points are, with smaller distances indicating greater similarity. Distances are useful for tasks like clustering data, detecting anomalies, data recognition, and measuring approximation errors. The most common distance measure, Euclidean distance, is explained for vectors of any dimension using the concept of norm from geometry. Caution is advised when calculating distances between data with differing scales.
Trinity outperforms Newbler 2.5 in de novo assembly of 454 transcriptome data. Trinity assembles more contigs with longer N50 and total bases. It also reconstructs more full-length transcripts matching the usagi CDS reference sequences compared to Newbler. However, Trinity also assembles more poly-A/T sequences which may reflect non-coding RNA.
The document discusses genome sequencing data analysis. It mentions using TopHat and Cufflinks software to analyze NG-Seq data from genomeDB and NCBI SRA. It also discusses sorting transcript expression data and accessing gene information from databases like MGI and UCSC.
The document discusses using R with interactive design and Ruby on Rails. It provides examples of using RSRuby to integrate R functions and graphics with Ruby code. This allows R output like histograms and graphs to be generated from within Ruby on Rails applications and displayed on web pages. It also describes how R can be used for data analysis and visualization independently or with tools like TIBCO Spotfire and Excel.
This document discusses analyzing biomedical literature using R and summarizes statistics on terms from PubMed. It shows how to import a CSV file of PubMed records into R, filter the data by year, and calculate sums of term frequencies. Over 19 million records are in PubMed with over 111,000 containing terms like Southern blotting, Northern blotting, and Western blotting. R can be used to efficiently analyze large text corpora from sources like PubMed.
The document appears to be a menu listing options for statistical software including R, ESS, help menus, and demos. It includes options for R and ESS statistical packages, help menus, and demos as well as listings for Bible software.
