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Found 15 papers, 1 papers with code
QuadraNet: Improving High-Order Neural Interaction Efficiency with Hardware-Aware Quadratic Neural Networks
Recent progress in computer vision-oriented neural network designs is mostly driven by capturing high-order neural interactions among inputs and features.
Hardware Aware Neural Architecture Search
Neural Architecture Search
Fed2: Feature-Aligned Federated Learning
Federated learning learns from scattered data by fusing collaborative models from local nodes.
A Survey of Large-Scale Deep Learning Serving System Optimization: Challenges and Opportunities
With both scaling trends, new problems and challenges emerge in DL inference serving systems, which gradually trends towards Large-scale Deep learning Serving systems (LDS).
Heterogeneous Federated Learning
Specifically, we design a feature-oriented regulation method ({$\Psi$-Net}) to ensure explicit feature information allocation in different neural network structures.
AntiDote: Attention-based Dynamic Optimization for Neural Network Runtime Efficiency
Based on the neural network attention mechanism, we propose a comprehensive dynamic optimization framework including (1) testing-phase channel and column feature map pruning, as well as (2) training-phase optimization by targeted dropout.
Beyond Short-Term Snippet: Video Relation Detection With Spatio-Temporal Global Context
Different from relationships in static images, videos contain an addition temporal channel.
Recurrent Attentive Zooming for Joint Crowd Counting and Precise Localization
To address this issue, this work proposes a novel framework that simultaneously solving two inherently related tasks - crowd counting and localization.
Interpreting and Evaluating Neural Network Robustness
Recently, adversarial deception becomes one of the most considerable threats to deep neural networks.
INTERPRETABLE CONVOLUTIONAL FILTER PRUNING
As significant redundancies inevitably present in such a structure, many works have been proposed to prune the convolutional filters for computation cost reduction.
Demystifying Neural Network Filter Pruning
We find that the filter magnitude based method fails to eliminate the filters with repetitive functionality.
Functionality-Oriented Convolutional Filter Pruning
As significant redundancies inevitably present in such a structure, many works have been proposed to prune the convolutional filters for computation cost reduction.
Interpreting Adversarial Robustness: A View from Decision Surface in Input Space
One popular hypothesis of neural network generalization is that the flat local minima of loss surface in parameter space leads to good generalization.
HASP: A High-Performance Adaptive Mobile Security Enhancement Against Malicious Speech Recognition
In this work, we propose HASP -- a high-performance security enhancement approach to solve this security issue on mobile devices.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Towards Robust Training of Neural Networks by Regularizing Adversarial Gradients
In recent years, neural networks have demonstrated outstanding effectiveness in a large amount of applications. However, recent works have shown that neural networks are susceptible to adversarial examples, indicating possible flaws intrinsic to the network structures.
How convolutional neural network see the world - A survey of convolutional neural network visualization methods
Nowadays, the Convolutional Neural Networks (CNNs) have achieved impressive performance on many computer vision related tasks, such as object detection, image recognition, image retrieval, etc.
