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SigOpt at INFORMS Annual Meeting 2022

Numenta Uses Intelligent Experimentation for Neuroscience

ICYMI - SigOpt Summit Recap: Accelerating Model Training with Habana® Gaudi® Processors and SigOpt Hyperparameter Optimization

Representation Learning for Materials Science with MIT

ICYMI – Recap: Intelligent Experimentation Overview for Ai4 Webinar

Numenta Brings Brain-Based Principles to AI with SigOpt

A Better Approach to Experimentation

SigOpt Summit 2021

SigOpt Intelligent Experimentation Platform Overview in Japanese

Academic Interview: Optimizing a Robotics Use Case with SigOpt

Cost matters: on the importance of cost-aware hyperparameter optimization

Highlight: Beyond the Pareto Efficient Frontier: Constraint Active Search for Multiobjective Experimental Design, ICML 2021

Simple Neural Architecture Search with SigOpt

Constraint Active Search for Experimental Design

SigOpt Overview on Product, Features, and Use Cases

Naivete be gone: budget-constrained Bayesian Optimization for better results

Store Visual Artifacts in SigOpt to See the Bigger Modeling Picture

Results from the NeurIPS Black Box Optimization Competition

Best Practices for Metrics, Training, Tuning

A Better Approach to Metrics, Training, and Tuning with MLconf

Efficient BERT at Ray Summit

Learn Experiment Management for Image Classification and Fraud Detection at MLconf 2020

Dealing with Model Performance Drift in the Pandemic

Using SigOpt on BERT for an NLP Task

Efficient BERT with Multimetric Optimization, part 3

Efficient BERT with Multimetric Optimization, part 2

Efficient BERT with Multimetric Optimization, part 1

Efficient BERT: An Overview

Deep Learning for Prediction and Optimization of Fast-Flow Peptide Synthesis

Take the Pain out of Training and Tuning

Finding your way to the finish line: model comparisons and visualizations

Efficient BERT: Find your optimal model with Multimetric Bayesian Optimization

Keeping track of it all: recording and organizing model training runs

ICYMI Recap: Detecting COVID-19 with Deep Learning and DarwinAI

ICYMI Recap: Warm Start Tuning with Prior Beliefs

A Comparison of Hyperparameter Optimization Methods

ICYMI Recap: Introducing Metric Management

Applying Prior Beliefs in SigOpt

ICYMI Recap: T4ST Talk 3: Training, Tuning, and Metric Strategy

Tuning for Systematic Trading: Training, Tuning and Metric Strategy

Feature Walkthrough: Metric Constraints

Metric Management, a Comprehensive Stack to Track and Adapt Metrics to Fit Your Business

Metric Constraints help you closely align your models with your business objectives

Parametrizing Data Augmentation in COVID-Net

ICYMI Recap: T4ST Talk 2: Efficient Training and Tuning for Deep Learning Models

ICYMI Recap: Modeling in Place—a panel discussion with Alectio, NVIDIA, Pandora, and Yelp

SigOpt GTC 2020: Optimized Image Classification on the Cheap

Tuning for Systematic Trading

ICYMI Recap: T4ST Talk 1: Intuition behind Bayesian optimization with and without multiple metrics

Tuning for Systematic Trading: Intuition behind Bayesian optimization with and without multiple metrics

On variable and random shape Gaussian interpolations

Short Term Blood Glucose Prediction based on Continuous Glucose Monitoring Data

Bayesian Optimization for Adaptive Experimental Design: A Review

Reducing Reflection in Additive Manufacturing of Optoelectronics

Podcast: Automated Model Tuning with SigOpt

University of Fribourg uses SigOpt to develop a spectral initialization strategy to increase classification accuracy on medical images by 2.2%

SigOpt Webinar: Tuning Data Augmentation to Boost Model Performance

SigOpt at MLconf: Optimized Image Classification on the Cheap

Trainable Spectrally Initializable Matrix Transformations in Convolutional Neural Networks

SigOpt Webinar: Critical Capabilities of Optimization in the Enterprise

Modeling at Scale: SigOpt at TWIMLcon 2019

Black-Box Image Augmentation for Better Classification

Tuning 2.0: Advanced Optimization Techniques Webinar Recording

Tuning 2.0: Advanced Optimization Techniques Webinar

Interactive Tradeoffs Between Competing Offline Metrics with Bayesian Optimization

Butterfly Wings Inspire New Glass Structure

Insights for Building High-Performing Image Classification Models

SigOpt at GTC: Reducing Operational Barriers to Optimization

SigOpt at O'Reilly: Best Practices for Scaling Modeling Platforms

Field Report: GPU Technology Conference 2019 #GTC19

Should you build or buy your data science platform?

SigOpt at GTC: Tuning the Untunable - Lessons for Deep Learning Optimization

SigOpt at GTC: Tuning the Untunable

SigOpt at Ai4 Healthcare: Lessons for an Enterprise Approach to Model Optimization

Let's talk Bayesian optimization

Tuning the Untunable: Insights for Deep Learning Optimization

Tuning the Untunable: Insights for Deep Learning Optimization

Tutorials in Optimization Research: Bayesian Optimization

Feature Spotlight: Multitask Optimization

The Next Battleground for Deep Learning Performance

Using Bayesian Optimization to Simultaneously Tune Multiple Metrics

Metis: Robustly Optimizing Tail Latencies of Cloud Systems

MONAS: Multi-Objective Neural Architecture Search using Reinforcement Learning

Model Optimization with Competing Objectives

Applying Multimetric Optimization to Solve Business Problems

Sequential Preference-Based Optimization

Low-Level Augmented Bayesian Optimization for Finding the Best Cloud VM

Practical Bayesian optimization in the presence of outliers

Filtering Outliers in Bayesian Optimization

Policy Search using Robust Bayesian Optimization

A Strategy for Ranking Optimization Methods using Multiple Criteria

Use Case - Fast CNN Tuning with AWS GPU Instances and SigOpt

If Machine Learning is the question, open source is the answer. Right?

Deep Learning Hyperparameter Optimization with Competing Objectives

Convolutional Neural Networks for Atomistic Systems

Getting Hyped for Deep Learning Configs

Using Optimal Learning to tune Deep Learning / AI Models

Fast CNN Tuning with AWS GPU Instances and SigOpt

Interactive Preference Learning of Utility Functions for Multi-Objective Optimization

Preemptive Termination of Suggestions during Sequential Kriging Optimization of a Brain Activity Reconstruction Simulation

SigOpt at NeurIPS 2018

Multimetric Updates in the Experiment Insights Dashboard

AutoML at ICML 2018

New Advanced Feature: Constraints

Building a Better Mousetrap via Multicriteria Bayesian Optimization

Intro to Multicriteria Optimization

Deep Neural Network Optimization with SigOpt and Nervana Cloud

TensorFlow ConvNets on a Budget with Bayesian Optimization

Unsupervised Learning with Even Less Supervision Using Bayesian Optimization