1. Interactive Machine Learning with a GPU-Accelerated Toolkit
Biye Jiang, Huasha Zhao, John Canny Computer Science Division
University of California, Berkeley
{bjiang, hzhao,
Berkeley
Institute of Design
Dashboard
Model overview (Topic matrix, image cluster centers)
Main loss and other evaluation metrics
Sliders to change model hyper-parameters
Dashboard
Other Visualized Metric
Cluster centers Pairwise distance
(MINST dataset) between clusters Silhouette graph
References
[1] Huasha Zhao, Biye Jiang and John Canny SAME but Different: Fast and High-Quality Gibbs Parameter Estimation Arxiv (2014)
[2] John Canny and Huasha Zhao. Big data analytics with small footprint: Squaring the cloud. In ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2013
[3] John Canny and Huasha Zhao BIDMach: Large-scale Learning with Zero Memory Allocation, BIGLearn Workshop, Neural Information Processing Conference (NIPS) 2013
Motivation
ML algorithms optimize mathematical criteria
People have informal notions of a “good” model
Real-world ML applications often involve trade-offs between multiple criteria (business logic):
Revenue
Advertiser satisfaction
User metrics
These goals should be addressed during training, not after.
Interactive ML allows users to understand the effects of these trade-offs on model quality and structure.
Tuning parameter: sizeWeight
Metric: Histogram of cluster size
Increase
sizeWeight
Interactive Interface
Minibatch learning supports continuous data streaming, model updates happen many times per second.
Parameters and visualizations are updated in real time.
Algorithms converge after a few updates:
Less than 10s to get stable result on MINST8M (20GB)
Encoding constraints as mixins functions
Users first figure out secondary optimization goal
Sparseness, Consistency, Independence
Then use its gradient to update the model
The mixin weight  is a hyper-parameter, the mixin value is a metric.
Users can interactively adjust the controls and see the effects on corresponding metrics
Visualization
in Browser
Web Server
BIDMach
Grab data from
GPU 10 times/s
Changed Hyper-Parameter
Model overview,
Evaluation metrics
Using D3.js
Each hyper-parameter
corresponds to a visualization of the metric
Data structure
User defined logging. Only used data will be logged.
Efficient internal matrix format in BIDMach
Using JSON to communicate between server and browser
Can easily support deep neural network
Temperature/Learning rate control
Control the window size for moving average update
Control the variance of the Gibbs sampler
User defined annealing learning schedule