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Found 11 papers, 2 papers with code
Federated Remote Physiological Measurement with Imperfect Data
The growing need for technology that supports remote healthcare is being acutely highlighted by an aging population and the COVID-19 pandemic.
EfficientPhys: Enabling Simple, Fast and Accurate Camera-Based Vitals Measurement
Camera-based physiological measurement is a growing field with neural models providing state-the-art-performance.
Face Detection
Photoplethysmography (PPG) heart rate estimation
EfficientPhys: Enabling Simple, Fast, and Accurate Camera-Based Vitals Measurement
Camera-based physiological measurement is a growing field with neural models providing state-the-art-performance.
Automated Backend-Aware Post-Training Quantization
Quantization is a key technique to reduce the resource requirement and improve the performance of neural network deployment.
SplitSR: An End-to-End Approach to Super-Resolution on Mobile Devices
In this work, we demonstrate state-of-the-art latency and accuracy for on-device super-resolution using a novel hybrid architecture called SplitSR and a novel lightweight residual block called SplitSRBlock.
MetaPhys: Few-Shot Adaptation for Non-Contact Physiological Measurement
There are large individual differences in physiological processes, making designing personalized health sensing algorithms challenging.
Characterizing Structural Regularities of Labeled Data in Overparameterized Models
We obtain empirical estimates of this score for individual instances in multiple data sets, and we show that the score identifies out-of-distribution and mislabeled examples at one end of the continuum and strongly regular examples at the other end.
Relay: A High-Level Compiler for Deep Learning
Using these extension mechanisms, Relay supports a unified compiler that can target a variety of hardware platforms.
A Hardware-Software Blueprint for Flexible Deep Learning Specialization
Specialized Deep Learning (DL) acceleration stacks, designed for a specific set of frameworks, model architectures, operators, and data types, offer the allure of high performance while sacrificing flexibility.
Learning to Optimize Tensor Programs
Efficient implementations of tensor operators, such as matrix multiplication and high dimensional convolution, are key enablers of effective deep learning systems.
TVM: An Automated End-to-End Optimizing Compiler for Deep Learning
Experimental results show that TVM delivers performance across hardware back-ends that are competitive with state-of-the-art, hand-tuned libraries for low-power CPU, mobile GPU, and server-class GPUs.
