A hybrid semi-supervised framework fusing Whisper embeddings with acoustic and prosodic features achieves 0.751 Macro-F1 for speaker confidence detection and outperforms baselines including WavLM, HuBERT, and Wav2Vec 2.0.
Adam: A method for stochastic optimization
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ShardTensor is a domain-parallelism system for SciML that enables flexible scaling of extreme-resolution spatial datasets by removing the constraint of batch size one per device.
Experiments on real industrial time series show that partial model sharing improves diffusion model performance in bandwidth-limited non-IID settings, while full sharing stabilizes GAN training but offers less robustness than VAE or DDPM alternatives.
Sequential Forward Floating Selection with a U-Net++ proxy identifies an 8-channel subset from multi-spectral and terrain data that matches or exceeds F1 scores of full 30-channel configurations for landslide segmentation.
citing papers explorer
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A Semi-Supervised Framework for Speech Confidence Detection using Whisper
A hybrid semi-supervised framework fusing Whisper embeddings with acoustic and prosodic features achieves 0.751 Macro-F1 for speaker confidence detection and outperforms baselines including WavLM, HuBERT, and Wav2Vec 2.0.
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ShardTensor: Domain Parallelism for Scientific Machine Learning
ShardTensor is a domain-parallelism system for SciML that enables flexible scaling of extreme-resolution spatial datasets by removing the constraint of batch size one per device.
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On the Tradeoffs of On-Device Generative Models in Federated Predictive Maintenance Systems
Experiments on real industrial time series show that partial model sharing improves diffusion model performance in bandwidth-limited non-IID settings, while full sharing stabilizes GAN training but offers less robustness than VAE or DDPM alternatives.
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Sequential Feature Selection for Efficient Landslide Segmentation from Multi-Spectral Data
Sequential Forward Floating Selection with a U-Net++ proxy identifies an 8-channel subset from multi-spectral and terrain data that matches or exceeds F1 scores of full 30-channel configurations for landslide segmentation.