Today we’re joined by Julieta Martinez, a senior research scientist at recently announced startup Waabi.
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Julieta was a keynote speaker at the recent LatinX in AI workshop at CVPR, and our conversation focuses on her talk “What do Large-Scale Visual Search and Neural Network Compression have in Common,” which shows that multiple ideas from large-scale visual search can be used to achieve state-of-the-art neural network compression. We explore the commonality between large databases and dealing with high dimensional, many-parameter neural networks, the advantages of using product quantization, and how that plays out when using it to compress a neural network.
We also dig into another paper Julieta presented at the conference, Deep Multi-Task Learning for Joint Localization, Perception, and Prediction, which details an architecture that is able to reuse computation between the three tasks, and is thus able to correct localization errors efficiently.
Thanks to our Sponsor!
I’d like to send a huge thank you to our friends at Qualcomm Technologies for their continued support of the podcast, and their sponsorship of this series of podcasts from the CVPR conference! Qualcomm AI Research is dedicated to advancing AI to make its core capabilities — perception, reasoning, and action — ubiquitous across devices. Their work makes it possible for billions of users around the world to have AI-enhanced experiences on devices powered by Qualcomm Technologies. To learn more about what Qualcomm Technologies is up to on the research front, visit twimlai.com/qualcomm.
Connect with Julieta!
- Paper: Revisiting additive quantization
- Paper: LSQ++: Lower running time and higher recall in multi-codebook quantization
- Paper: Permute, Quantize, and Fine-tune: Efficient Compression of Neural Networks
- Paper: On human motion prediction using recurrent neural networks
- Paper: A simple yet effective baseline for 3d human pose estimation
- Paper: Deep Multi-Task Learning for Joint Localization, Perception, and Prediction