AI News, NIPS Proceedingsβ

NIPS Proceedingsβ

Part of: Advances in Neural Information Processing Systems 27 (NIPS 2014) Gaussian processes (GPs) are a powerful tool for probabilistic inference over functions.

They have been applied to both regression and non-linear dimensionality reduction, and offer desirable properties such as uncertainty estimates, robustness to over-fitting, and principled ways for tuning hyper-parameters.

We show that GP performance improves with increasing amounts of data in regression (on flight data with 2 million records) and latent variable modelling (on MNIST).

NIPS Proceedingsβ

Part of: Advances in Neural Information Processing Systems 27 (NIPS 2014) Gaussian processes (GPs) are a powerful tool for probabilistic inference over functions.

They have been applied to both regression and non-linear dimensionality reduction, and offer desirable properties such as uncertainty estimates, robustness to over-fitting, and principled ways for tuning hyper-parameters.

We show that GP performance improves with increasing amounts of data in regression (on flight data with 2 million records) and latent variable modelling (on MNIST).

Inference in the age of big data: Future perspectives on neuroscience

In the last 10 years neuroscience spawned quantitative datasets of unprecedented breadth (e.g., microanatomy, synaptic connections, and optogenetic brain-behavior assays) and size (e.g., cognition, brain imaging, and genetics).

While growing data availability and information granularity have been amply discussed, we direct attention to a less explored question: How will the unprecedented data richness shape data analysis practices?

Peadar Coyle: Variational Inference and Python

Filmed at PyData London 2017 Description Recent improvements in Probabilistic Programming have led to a new method called Variational Inference. This is an alternative method to the standard...

Introduction to Data Science with R - Data Analysis Part 1

Part 1 in a in-depth hands-on tutorial introducing the viewer to Data Science with R programming. The video provides end-to-end data science training, including data exploration, data wrangling,...

4. Parametric Inference (cont.) and Maximum Likelihood Estimation

MIT 18.650 Statistics for Applications, Fall 2016 View the complete course: Instructor: Philippe Rigollet In this lecture, Prof. Rigollet talked about confidence..

"Demystifying Big Data: From Causality to Correlation" Dr. Jaap van den Herik (ICAART 2014)

Keynote Title: Demystifying Big Data: From Causality to Correlation Keynote Lecturer: Dr. Jaap van den Herik Keynote Chair: Dr. Joaquim Filipe Presented on: 07-03-2014, Angers, France Abstract:...

Data Modeling and Inference Overview

Dr. Rafael Irizarry from Harvard University presents a lecture titled "Data Modeling and Inference." View slides Coming soon... Lecture Abstract This lecture will describe the importance...

Inverse modelling - 1 (Frédéric Chevallier)

Inverse modelling is a term that groups a number of mathematical techniques that allow inferring information on parameters and quantities that are not directly observed, but are linked via...

Matt Gardner: Feature Generation from Knowledge Graphs

Matt Gardner: Feature Generation from Knowledge Graphs Abstract: A lot of attention has recently been given to the creation of large knowledge bases that contain millions of facts about...

Theory and Methods of Data Science

Scalable inference for a full multivariate stochastic volatility model Petros Dellaportas, Anastasios Plataniotis, Michalis Titsias University College London We introduce a multivariate stochastic...

"Market Timing, Big Data, And Machine Learning" by by Dr. Xiao Qiao

Talk by Dr. Xiao Qiao, Finance PhD at the University of Chicago and consultant for Hull Investments. From QuantCon NYC 2016. Return predictability has been a controversial topic in finance...

Exploratory Data Analysis

Dr. Brian Caffo from Johns Hopkins presents a lecture on "Exploratory Data Analysis." Lecture Abstract Exploratory data analysis (EDA) is the backbone of data science and statistical analysis....