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  2. Exploring Semi-supervised Learning for Audio-based COVID-19…

    https://mobile-systems.cl.cam.ac.uk/papers/interspeech22.pdf
    5 Sep 2022: While the most commonly used test tools for COVID-19detection such as polymerase chain reaction (PCR) tests [1, 2]and lateral flow device antigen (LFD) tests [3] are effective, dig-ital ... patients. However, their audio recordings can be available
  3. ROBUST AND EFFICIENT UNCERTAINTY AWARE BIOSIGNAL CLASSIFICATIONVIA…

    https://mobile-systems.cl.cam.ac.uk/papers/icassp22-qendro.pdf
    22 Apr 2022: Table 3: Classification and uncertainty results. Entries aremean and standard deviation over 3 random splits of test data.Best results are indicated in bold. ... FLOPs: number of floating point opera-tions (Giga). Time: average inference time over test
  4. Exploring On-Device Learning Using Few Shots forAudio Classification…

    https://mobile-systems.cl.cam.ac.uk/papers/euspico22.pdf
    24 Aug 2022: Wealso tried cases where number of train ways can be higherthan test ways such as 4 (train) and 2 (test). ... The query set iscomposed of 15 samples for testing and 5 for training per way.We randomly sample 5000 training tasks, 1000 validation tasksand
  5. Stress Inference from Abdominal Sounds using Machine Learning Erika…

    https://mobile-systems.cl.cam.ac.uk/papers/embc22-bondareva.pdf
    22 Apr 2022: Remain seated. Complete the daily task. randomDo anything. Meditation(relaxing task). Stroop test(stressful task). ... Moleman, H. G. van Steenis, et al., “Characterizationof stress reactions to the stroop color word test,” Pharmacol.
  6. Investigating Domain-agnostic Performance in Activity Recognition…

    https://mobile-systems.cl.cam.ac.uk/papers/hasca22.pdf
    5 Sep 2022: Human activity recognition (HAR) models suffer significant performance degradation when faced with data heterogeneity(device, users, environments) at test time. ... This work presents the case for training models which are domain-agnostic, i.e., that
  7. Towards Adversarial Robustness with Early Exit Ensembles Lorena…

    https://mobile-systems.cl.cam.ac.uk/papers/embc22-qendro.pdf
    22 Apr 2022: All datasets are split into 80%/10%/10%train/validation/test maintaining class proportions. Eachdataset is paired with a different architecture: FCNet [17](fully-convolutional 5-layer network) for ECG and ... Additionally, we provide anal-ysis on
  8. Enabling On-Device Smartphone GPU basedTraining: Lessons Learned…

    https://mobile-systems.cl.cam.ac.uk/papers/perfail22.pdf
    4 Feb 2022: These details are described next. Model Architecture. Having failed at beating the CPU inthe experiments devised above, we move on to trying outdifferent architectures to test how size affects the
  9. IMPROVING FEATURE GENERALIZABILITY WITH MULTITASK LEARNING IN…

    https://mobile-systems.cl.cam.ac.uk/papers/icassp22-ma.pdf
    22 Apr 2022: Similarly,GSC dataset is split to 5-3-3-3-3-3. For both datasets, the (train,test, validation) splitting ratio is set to (0.7, 0.2, 0.1).
  10. Mobile Health with Head-Worn Devices: Challenges and Opportunities

    https://mobile-systems.cl.cam.ac.uk/papers/earable-survey22.pdf
    5 Sep 2022: Theseexisting forms have already been accepted by the users andhave been proved to withstand the test of time, therefore thetransition to a smart device is easier.
  11. YONO: Modeling Multiple Heterogeneous Neural Networks on…

    https://mobile-systems.cl.cam.ac.uk/papers/ipsn22.pdf
    7 Mar 2022: the number of layers 𝐿, error threshold 𝜖, heuristicsOutput: Reconstructed model weights Ŵ, model indices b̂Data: Train data D𝑇𝑅𝐴𝐼𝑁 , Test data D𝑇𝐸𝑆𝑇/ Perform an initial finetuning step /. 1 Ŵ C(b) //

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