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The technical tagteam behind this blog. We aim to showcase the latest research, tools, and hardware for developing AI applications.

Model Explainability With GradCAM

Though accustomed to evaluating ML models with respect to performance statistics like accuracy, real-world deployment scenarios must weigh multiple models performing comparably. Deciding which to launch in A/B experiment can be challenging when the offline metrics are just a proxy for online metrics core to business decisions. Experiment time is precious and for large experiments on foundational models, the tolerance for error is limited hence it is critical to base experiment launch decisions on a collection of diverse metrics....

 · 3 min · Terry Rodriguez & Salma Mayorquin

Data Sketching

In applied machine learning, engineers may spend considerable effort optimizing model performance with respect to metrics like accuracy on hold-out data. At times, more nuanced engineering decisions may weigh additional factors, such as latency or algorithmic simplicity. Across many problem domains, approximate, algorithmic solutions are preferred to more accurate techniques with poor scalability. It’s said that “what’s past is prologue”, an idea which manifests in the most foundational of problem solving methods: use prior information....

 · 5 min · Terry Rodriguez & Salma Mayorquin

Efficient Transformers

Convolutional Neural Networks have been a boon to the computer vision community. Deep learning from high-bandwidth image/video datasets can be computationally and statistically much more efficient using the inductive bias of strong locality. This streamlines inference over big datasets or on resource-limited hardware. To model sequential dependence in short sequences of low-dimensional data, we have often used LSTMs. However, researchers have recently found success adapting Transformer architectures to learn from image and video, both applications traditionally dominated by CNNs....

 · 7 min · Terry Rodriguez & Salma Mayorquin

TF Microcontroller Challenge: Droop, There It Is

Repo for this project here! A seasoned gardener can diagnose plant stress by visual inspection. For our entry to the Tensorflow Microcontroller Challenge, we chose to highlight the issue of water conservation while pushing the limits of computer vision applications. Our submission, dubbed “Droop, There It Is” builds on previous work to identify droopy, wilted plants. Drought stress in plants typically manifests as visually discernible drooping and wilting, also known as plasmolysis, indicating low turgidity or water pressure....

 · 5 min · Terry Rodriguez & Salma Mayorquin

Make Some Noise for Score Based Models

Blob Pitt's next big blockbuster Generative models have reached a remarkable capacity to synthesize original instances after learning a data distribution. In the arena of image generation, the recent SOTA tracks alongside advances in a family of models called generative adversarial networks or GANs. This framework of jointly training two networks gives rise to a learnable loss. Despite these successes, GANs are challenged by training instabilities. The latest StyleGAN2-ada mitigates mode collapse arising from overfit discriminators using test time data augmentation....

 · 4 min · Terry Rodriguez & Salma Mayorquin