Search Results for author:
Found 8 papers, 6 papers with code
PA&DA: Jointly Sampling PAth and DAta for Consistent NAS
Our method only requires negligible computation cost for optimizing the sampling distributions of path and data, but achieves lower gradient variance during supernet training and better generalization performance for the supernet, resulting in a more consistent NAS.
Unleashing the Power of Gradient Signal-to-Noise Ratio for Zero-Shot NAS
Neural Architecture Search (NAS) aims to automatically find optimal neural network architectures in an efficient way.
AGNAS: Attention-Guided Micro- and Macro-Architecture Search
We utilize the attention weights to represent the importance of the relevant operations for the micro search or the importance of the relevant blocks for the macro search.
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
DU-DARTS: Decreasing the Uncertainty of Differentiable Architecture Search
Differentiable Neural Architecture Search (DARTS) recently attracts a lot of research attention because of its high efficiency.
Ranked #9 on
Neural Architecture Search
on CIFAR-100
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 #1 on
Speaker Verification
on VoxCeleb1
DARTS-: Robustly Stepping out of Performance Collapse Without Indicators
We call this approach DARTS-.
Ranked #20 on
Neural Architecture Search
on NAS-Bench-201, CIFAR-10
MixPath: A Unified Approach for One-shot Neural Architecture Search
However, current two-stage neural architecture search methods are mainly limited to single-path search spaces.
