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Found 10 papers, 3 papers with code
A Multi-modal one-class generative adversarial network for anomaly detection in manufacturing
Since the generated samples simulate the low density area for each modal, the discriminator could directly detect anomalies from normal data.
SHAPNN: Shapley Value Regularized Tabular Neural Network
Additionally, prediction with explanation serves as a regularizer, which improves the model's performance.
Error-aware Quantization through Noise Tempering
In this work, we incorporate exponentially decaying quantization-error-aware noise together with a learnable scale of task loss gradient to approximate the effect of a quantization operator.
SQuAT: Sharpness- and Quantization-Aware Training for BERT
Quantization is an effective technique to reduce memory footprint, inference latency, and power consumption of deep learning models.
End-to-end Quantized Training via Log-Barrier Extensions
Quantization of neural network parameters and activations has emerged as a successful approach to reducing the model size and inference time on hardware that sup-ports native low-precision arithmetic.
Audio-Visual Event Recognition through the lens of Adversary
This work aims to study several key questions related to multimodal learning through the lens of adversarial noises: 1) The trade-off between early/middle/late fusion affecting its robustness and accuracy 2) How do different frequency/time domain features contribute to the robustness?
Adversarial Music: Real World Audio Adversary Against Wake-word Detection System
In this work, we target our attack on the wake-word detection system, jamming the model with some inconspicuous background music to deactivate the VAs while our audio adversary is present.
A Comparison of deep learning methods for environmental sound
On these features, we apply five models: Gaussian Mixture Model (GMM), Deep Neural Network (DNN), Recurrent Neural Network (RNN), Convolutional Deep Neural Net- work (CNN) and i-vector.
Learning Filter Banks Using Deep Learning For Acoustic Signals
Based on the procedure of log Mel-filter banks, we design a filter bank learning layer.
Very Deep Convolutional Neural Networks for Raw Waveforms
Our CNNs, with up to 34 weight layers, are efficient to optimize over very long sequences (e. g., vector of size 32000), necessary for processing acoustic waveforms.
