Machine Learning Blueprint Newsletter, Edition 24, 6/10/18

This newsletter is written and curated by Mike Tamir and Mike Mansour. 

June 10, 2018

Hi all,

Hope you enjoy this week's ML Blueprint. This week is brought to you by fastdata.io.

Spotlight Articles

Expanding on the Synthesising Obama DeepFake generation from about a year ago, new advancements to be presented at the SIGGRAPH 2018 this autumn take it to a whole new level. Now, given input video of an actor, they can produce corresponding output video that has minimal artifacts from the generation (see below gif -- note the image warping around the head). Aside from the obvious nefarious uses of this technology (DeepFakes & DeepPorno), one beneficial utilization may be to create really good dubbings of video into other languages.

Machine Learning Blueprint's Take

Just pop your csv or json data into the browser and start making To clear the waters, Google publishes seven objectives for how their ML applications will operate, and outline a few redline territories they will not develop applications in, like weapons, surveillance, or abuses of international human rights (all with some caveats). Here are their “guidelines”:

• Be socially beneficial.
• Avoid creating or reinforcing unfair bias.
• Be built and tested for safety.
• Be accountable to people.
• Incorporate privacy design principles.
• Uphold high standards of scientific excellence.

Adversarial attacks have recently been making a buzz in the ML space, but there remain other attack vectors against deployed models that should encourage engineers to develop defenses. One attack vector, data poisoning, occurs when a malicious attacker tries to change a trend in your training dataset via feedback mechanisms, like reporting not-spam as spam in an effort to throw off the classifier. Attackers may also try to steal your model or at least learn about its decision boundaries, which in effect is stealing the intellectual property and data-collection efforts. This article responsibly provides a number of defense strategies for all three attack vectors.

Machine Learning Blueprint's Take

WekaIO Matrix™, the world’s fastest distributed file system for machine learning workloads, has been named a Cool Vendor by Gartner, Inc., in the Gartner Cool Vendors in Storage Technologies, 2018 report.

Matrix is the first and only NVMe-native shared and distributed file system that has been written to support new high-performance workloads in machine learning. The demands of 2018 workloads cannot be met with a thirty-year old protocol such as NFS, that was designed when networks were slow relative to the storage media.

Learning Machine Learning

t-distributed Stochastic Neighbor Embedding is a method for EDA of high-dimensional data, but previously came with a high computational expense. A new optimization is presented, leveraging GPUs via WebGL to speed up the N-Body objective function, where attractions and repulsions are calculated to generate the small cluster-neighborhoods. Now it’s fast enough to compute t-SNE’s in realtime in the browser, whereas MNIST previously took 15-minutes in a C++ implementation. Technical details on the optimizations here.

A series of summaries tracking state of the art in object detection as it evolved, starting with two-stage detection methods like R-CNN, and ending with single-stage methods like YOLO/SSD. It’s useful for getting a quick scope of the territory and choosing which methodology to explore for your use case.

An alternative, and debatably better, solution to determining model capacity than VC dimension, Rademacher Complexity was recently used in the LeCun paper explaining why DNN’s don’t suffer generalizability with large parameter spaces. The post here steps through the equation and relates to it to intuitive research on human learning.

Learn about CuPy to bring Numpy arrays to the GPU via CUDA, Sparse for, well ... sparse arrays, and Dask for extending the Numpy API for parallel execution on multi-core workstations or distributed clusters.

Machine Learning News

According to emails accessed through the Freedom of Information Act between Cruise and the City of San Francisco, Cruise is pressuring the city to aid in some crucial data collection and pushing for a release. Mainly, they want the SFFD drive around some autonomous units with lights flashing to cover some potentially weak spots in their algorithm, however the administration refuses to proceed citing this as a poor use of resources. Nevertheless, many of the details within hint towards a nearing release.

Machine Learning Blueprint's Take

Following from Google’s AutoML, this tool uses reinforcement learning to find optimal image transformation policies to augment an image training data set on a number of different dimensions. Each image transformation depends on the entire training dataset and the image itself, so no two transformations will be the same. The research quotes a 0.83% increase in training error or CIFAR10.

Machine Learning Blueprint's Take

A group of researchers trained an image-captioning algorithm on the most disturbing of materials found over in the darkest corners of reddit, like /r/gore & /r/watchpeopledie, and then had it provide captions to the famous inkblot tests, used for detecting underlying thought disorders. The results, are well, unsettling.

Machine Learning Blueprint's Take

Berkeley AI Research collaborated with Nexar to release a 100K sample dataset of 40-second videos at 720p and 30fps, sourced from around the United States. This makes it the largest and most diverse public dataset to date. It comes with a suite of annotations including: Image tagging, road object bounding boxes, drivable areas, lane markings, and full-frame instance segmentation. After you’ve toyed with the data sufficiently, you can enter any of the three challenges they’re hosting at the CVPR 2018 Workshop on Autonomous Driving based on this data.

Some minor updates added, like the ability to subclass Models, and process symbolic tensors. Overall, the Keras engine is more modular, and this appears to have been a technical-debt cleansing release cycle for the team.

DataBricks releases the alpha version of their ML model lifecycle management tool, much in the vein of other enterprise versions like FBLearnerFlow (Facebook), TFX (Google), except that this is open source. MLFlow will help you track your experiments & code changes, provide reproducible training setups, and aid in managing and deploying models.

Interesting Research

Many defining characteristics of human intelligence, which developed under much different pressures, remain out of reach for current advances in using deep learning for AI. In this paper the authors argue that combinatorial generalization must be a top priority for AI to achieve human-like abilities, and that structured representations and computations are key to realizing this objective. They explore how using relational inductive biases within deep learning architectures can facilitate learning about entities, relations, and rules for composing them, suggesting that graph networks can lay the foundation for more sophisticated, interpretable, and flexible patterns of reasoning.

This article addresses the open question of why neural networks generalize better with over-parametrization. The researchers suggest a novel complexity measure based on unit-wise capacities. This new measure correlates with the behavior of test error with increasing network sizes, and could potentially explain the improvement in generalization.