Search Results for author: Daniel L. Silver

Found 6 papers, 0 papers with code

The Roles of Symbols in Neural-based AI: They are Not What You Think!

no code implementations26 Apr 2023 Daniel L. Silver, Tom M. Mitchell

We propose that symbols are first and foremost external communication tools used between intelligent agents that allow knowledge to be transferred in a more efficient and effective manner than having to experience the world directly.

Inductive Bias

Forecasting COVID-19 Case Counts Based on 2020 Ontario Data

no code implementations18 Mar 2023 Daniel L. Silver, Rinda Digamarthi

Results: The best LSTM models forecasted tomorrow's daily COVID case counts with 90. 7% accuracy, and the 7-day rolling average COVID case counts with 98. 1% accuracy using independent test data.

Estimating Grape Yield on the Vine from Multiple Images

no code implementations8 Apr 2020 Daniel L. Silver, Jabun Nasa

Estimating grape yield prior to harvest is important to commercial vineyard production as it informs many vineyard and winery decisions.

AutoML @ NeurIPS 2018 challenge: Design and Results

no code implementations12 Mar 2019 Hugo Jair Escalante, Wei-Wei Tu, Isabelle Guyon, Daniel L. Silver, Evelyne Viegas, Yuqiang Chen, Wenyuan Dai, Qiang Yang

We organized a competition on Autonomous Lifelong Machine Learning with Drift that was part of the competition program of NeurIPS 2018.

AutoML BIG-bench Machine Learning

TrajectoryNet: An Embedded GPS Trajectory Representation for Point-based Classification Using Recurrent Neural Networks

no code implementations7 May 2017 Xiang Jiang, Erico N de Souza, Ahmad Pesaranghader, Baifan Hu, Daniel L. Silver, Stan Matwin

Understanding and discovering knowledge from GPS (Global Positioning System) traces of human activities is an essential topic in mobility-based urban computing.

General Classification

Learning Paired-associate Images with An Unsupervised Deep Learning Architecture

no code implementations20 Dec 2013 Ti Wang, Daniel L. Silver

This paper presents an unsupervised multi-modal learning system that learns associative representation from two input modalities, or channels, such that input on one channel will correctly generate the associated response at the other and vice versa.

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