Search Results for author:
Found 9 papers, 3 papers with code
Enhancing Data Quality in Federated Fine-Tuning of Foundation Models
In the current landscape of foundation model training, there is a significant reliance on public domain data, which is nearing exhaustion according to recent research.
BLOX: Macro Neural Architecture Search Benchmark and Algorithms
To provide a systematic study of the performance of NAS algorithms on a macro search space, we release Blox - a benchmark that consists of 91k unique models trained on the CIFAR-100 dataset.
ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsity
When the available hardware cannot meet the memory and compute requirements to efficiently train high performing machine learning models, a compromise in either the training quality or the model complexity is needed.
Prospect Pruning: Finding Trainable Weights at Initialization using Meta-Gradients
Pruning neural networks at initialization would enable us to find sparse models that retain the accuracy of the original network while consuming fewer computational resources for training and inference.
Conditioning Sequence-to-sequence Networks with Learned Activations
Conditional neural networks play an important role in a number of sequence-to-sequence modeling tasks, including personalized sound enhancement (PSE), speaker dependent automatic speech recognition (ASR), and generative modeling such as text-to-speech synthesis.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Adaptive Filters for Low-Latency and Memory-Efficient Graph Neural Networks
Scaling and deploying graph neural networks (GNNs) remains difficult due to their high memory consumption and inference latency.
NAS-Bench-ASR: Reproducible Neural Architecture Search for Speech Recognition
These datasets, however, focus predominantly on computer vision and NLP tasks and thus suffer from the problem of limited coverage of application domains.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Scaling Unsupervised Domain Adaptation through Optimal Collaborator Selection and Lazy Discriminator Synchronization
Breakthroughs in unsupervised domain adaptation (uDA) have opened up the possibility of adapting models from a label-rich source domain to unlabeled target domains.
BinaryFlex: On-the-Fly Kernel Generation in Binary Convolutional Networks
In this work we present BinaryFlex, a neural network architecture that learns weighting coefficients of predefined orthogonal binary basis instead of the conventional approach of learning directly the convolutional filters.
