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Found 6 papers, 2 papers with code
Hyper-Tune: Towards Efficient Hyper-parameter Tuning at Scale
The ever-growing demand and complexity of machine learning are putting pressure on hyper-parameter tuning systems: while the evaluation cost of models continues to increase, the scalability of state-of-the-arts starts to become a crucial bottleneck.
TNASP: A Transformer-based NAS Predictor with a Self-evolution Framework
Predictor-based Neural Architecture Search (NAS) continues to be an important topic because it aims to mitigate the time-consuming search procedure of traditional NAS methods.
Ranked #21 on
Neural Architecture Search
on CIFAR-10
ProxyBO: Accelerating Neural Architecture Search via Bayesian Optimization with Zero-cost Proxies
Designing neural architectures requires immense manual efforts.
SpeechNAS: Towards Better Trade-off between Latency and Accuracy for Large-Scale Speaker Verification
Recently, x-vector has been a successful and popular approach for speaker verification, which employs a time delay neural network (TDNN) and statistics pooling to extract speaker characterizing embedding from variable-length utterances.
Ranked #13 on
Speaker Verification
on VoxCeleb1
Neural Architecture Search on Acoustic Scene Classification
Convolutional neural networks are widely adopted in Acoustic Scene Classification (ASC) tasks, but they generally carry a heavy computational burden.
Fair DARTS: Eliminating Unfair Advantages in Differentiable Architecture Search
Differentiable Architecture Search (DARTS) is now a widely disseminated weight-sharing neural architecture search method.
Ranked #24 on
Neural Architecture Search
on CIFAR-10
