73 citations · 164 across the 12 of their papers we have counts for
6 papers · 1 filter
Beyond task performance: Decoding bioacoustic embeddings with speech features
Ines Nolasco, Jules Cauzinille, Marius Miron +8
Pretrained audio embeddings are standard in bioacoustics, yet little is known about which acoustic features these models encode, nor which are useful for a given task. This hinders…
Beyond the Baseband: Adaptive Multi-Band Encoding for Full-Spectrum Bioacoustics Classification
Eklavya Sarkar, Marius Miron, David Robinson +6
Animals hear and vocalize across frequency ranges that differ substantially from humans, often extending into the ultrasonic domain. Yet most computational bioacoustics systems rel…
Prospect Pruning: Finding Trainable Weights at Initialization using Meta-Gradients
Milad Alizadeh, Shyam A. Tailor, Luisa M Zintgraf +4
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 t…
COIN++: Neural Compression Across Modalities
Emilien Dupont, Hrushikesh Loya, Milad Alizadeh +3
Neural compression algorithms are typically based on autoencoders that require specialized encoder and decoder architectures for different data modalities. In this paper, we propos…
Single Shot Structured Pruning Before Training
Joost van Amersfoort, Milad Alizadeh, Sebastian Farquhar +2
We introduce a method to speed up training by 2x and inference by 3x in deep neural networks using structured pruning applied before training. Unlike previous works on pruning befo…
Gradient Regularization for Quantization Robustness
Milad Alizadeh, Arash Behboodi, Mart van Baalen +3
We analyze the effect of quantizing weights and activations of neural networks on their loss and derive a simple regularization scheme that improves robustness against post-trainin…