Hyperparameter Optimization through Neural Network Partitioning with Christos Louizos
EPISODE 627
|
MAY
1,
2023
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About this Episode
Today we kick off our coverage of the 2023 ICLR conference joined by Christos Louizos, an ML researcher at Qualcomm Technologies. In our conversation with Christos, we explore his paper Hyperparameter Optimization through Neural Network Partitioning and a few of his colleague's works from the conference. We discuss methods for speeding up attention mechanisms in transformers, scheduling operations for computation graphs, estimating channels in indoor environments, and adapting to distribution shifts in test time with neural network modules. We also talk through the benefits and limitations of federated learning, exploring sparse models, optimizing communication between servers and devices, and much more.
About the Guest
Christos Louizos
Qualcomm
Connect with Christos
Thanks to our sponsor Qualcomm AI Research
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.
Resources
- Hyperparameter Optimization through Neural Network Partitioning
- Composite Slice Transformer: An Efficient Transformer with Composition of Multi-Scale Multi-Range Attentions
- WiNeRT: Towards Neural Ray Tracing for Wireless Channel Modelling and Differentiable Simulations
- Robust Scheduling with GFlowNets
- TTN: A Domain-Shift Aware Batch Normalization in Test-Time Adaptation
- Neural DAG Scheduling via One-Shot Priority Sampling
- Advances in Neural Compression with Auke Wiggers
- Optical Flow Estimation, Panoptic Segmentation, and Vision Transformers with Fatih Porikli
- Equivariant Priors for Compressed Sensing with Arash Behboodi
- Multi-Device, Multi-Use-Case Optimization with Jeff Gehlhaar
- Weakly Supervised Causal Representation Learning with Johann Brehmer