Poisson HMM: The model of count time series
SAS' Ji Shen introduces you to an effective solution for modeling and forecasting count time series.
Tag: Analytics R&D
Using SAS Viya Machine Learning to classify COVID from non-COVID
In her first blog post, SAS' Mu He shows you how to train a convolutional neural network that can accurately detect patients with COVID-19 using the transfer learning technique.
Using the dlModelZoo action set to import PyTorch models into SAS
SAS' Scott Pope introduces you to an action set in SAS Viya called dlModelZoo that supports importing PyTorch models.
Improving the state-of-the-art algorithm for vehicle routing problems
SAS' Brandon Reese the EURO Meets NeurIPS 2022 Vehicle Routing Competition, which combined efforts of operations research and machine learning experts.
Accelerating delivery with CI/CD – Navigating the journey
SAS' Mary Carter details challenges and benefits of accelerating the delivery of SAS software.
Innovative contributions to NeurIPS 2022
NeurIPS 2022 allowed researchers and practitioners to share progress and brainstorm new ideas for advancing machine learning and its related fields.
Using PROC DEEPPRICE to personalize prices and optimize revenue
SAS' Sylvia Kabisa shows you how an online media company might use SAS to offer targeted discounts through personalized pricing.
Automate your machine learning pipeline in SAS Viya
Anuja Nagpal and Yonglin Zhu of SAS R&D reveal how, MLPA – without any code and within a given timeframe – finds an effective pipeline for a data set after applying data preprocessing, feature engineering and modeling with hyperparameter tuning.
Accelerating what-if analysis with machine learning
SAS' Bahar Biller expounds on the idea that stochastic simulations are large-data generation programs for highly complex and dynamic stochastic systems.
Using the cspade action to find frequent gene sequences
Building on a previous post on how the seqmc action can be used to mine frequent sequences, SAS' Amod Ankulkar explores an alternative algorithm.
Powering disruption: A DevOps journey at SAS
Billy Dickerson of SAS R&D chronicles three key challenges and lessons learned in SAS' journey to continuous integration (CI) and continuous delivery (CD).
Using sequence action set to mine long sequences
SAS' Yogender Kushawah introduces you to mining long sequences efficiently using the seqmc action in SAS Visual Analytics.
Risk driver analysis for complex supply chains
SAS' Bahar Biller and Xi Jiang use the example of a semiconductor manufacturing plant to illustrate the role of sensitivity analysis in assessing supply chain risk.
Find similar words in ESG data with Natural Language Processing
Using such features and Natural Language Processing capabilities like text parsing and information extraction in SAS Visual Text Analytics (VTA) helps us uncover emerging trends and unlock the value of unstructured text data.
Find duplicates and near-duplicates in a corpus with Natural Language Processing
To find exact duplicates, matching all string pairs is the simplest approach, but it is not a very efficient or sufficient technique. Using the MD5 or SHA-1 hash algorithms can get us a correct outcome with a faster speed, yet near-duplicates would still not be on the radar. Text similarity is useful for finding files that look alike. There are various approaches to this and each of them has its own way to define documents that are considered duplicates. Furthermore, the definition of duplicate documents has implications for the type of processing and the results produced. Below are some of the options. Using SAS Visual Text Analytics, you can customize and accomplish this task during your corpus analysis journey either with Python SWAT package or with PROC SQL in SAS.
Using PROC DEEPCAUSAL to optimize revenue through policy evaluation
SAS' Gunce Walton introduces to you a new scoring capability, how it utilizes Deep Neural Networks (DNNs) and shares use cases with PROC DEEPCAUSAL.
Identifying influential users in a social network to aid public health
SAS' Damian Herrick chronicles the refresh of a 2002 social-network analysis aimed at identifying influential peer educators among former drug users.
How to reveal new connections in a knowledge graph with link prediction
In this introduction to powerful knowledge graph tools, SAS' Brandon Reese shows you how they can predict disease similarity and compound similarity using an unsupervised approach.
Digital twin development: Why simulations are critical
SAS' Bahar Biller reveals how simulations enable KPI generation, risk quantification, risk management and more.
The SAS Batting Lab: The model powering the analysis
SAS research statistician Ji Shen reveals how to train a machine to be a batting coach.
Using Bayesian Additive Regression Trees for Flexible Outcome Modeling
SAS' Michael Lamm gives an overview of Bayesian Additive Regression Trees (BART) and demonstrates training and scoring BART models in SAS Visual Statistics.
Assess model bias in SAS Viya 4
SAS' Ricky Tharrington and Jagruti Kanjia explain two ways bias shows up in model predictions.
High-performance calculation of heat maps using CAS
SAS' Jordan Leiker shows you how any CAS action can be used with image data to create heat maps.
Computer vision solutions with privacy-by-design
SAS' Hamza Ghadyali reveals how to build computer vision solutions with privacy baked into their design.
World Creativity & Innovation Week 2022: The Return - Part 2
In the second of two Q&As with R&D colleagues, SAS' Udo Sglavo provides a window into how we approach drug-development challenges with machine learning.
World Creativity and Innovation Week 2022: The Return - Part 1
A note from Udo Sglavo: At SAS, what we deliver to our customers is a product of creative minds thinking differently, challenging the norm, taking risks, and learning from trial and error (The greatest teacher, failure is). For the return of World Creativity & Innovation Week, we want to share
Accuracy versus interpretability? With generalized additive models (GAMs), you can have both
SAS' Brian Gaines provides a primer on GAMs.
Collaborative Filtering and Supervised Learning: A Tale of Two Methods
SAS' Xuejun Liao weighs the pros and cons of collaborative filtering and supervised learning and explores their use in a unified framework.
Computer vision in cancer treatment: assessing lesion therapy response with SAS Visual Data Mining and Machine Learning
SAS' Courtney Ambrozic highlights how to use SAS VDMML to assess lesion response to chemotherapy for patients with colorectal liver cancer.
Image-specific processing functions in computer vision
SAS' Pankaj Telang shows you new image-specific processing capabilities in SAS Visual Data Mining and Machine Learning.