
HAWQV3: Dyadic Neural Network Quantization
Quantization is one of the key techniques used to make Neural Networks (...
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A Statistical Framework for Lowbitwidth Training of Deep Neural Networks
Fully quantized training (FQT), which uses lowbitwidth hardware by quan...
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MAF: Multimodal Alignment Framework for WeaklySupervised Phrase Grounding
Phrase localization is a task that studies the mapping from textual phra...
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Benchmarking Semisupervised Federated Learning
Federated learning promises to use the computational power of edge devic...
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ADAHESSIAN: An Adaptive Second Order Optimizer for Machine Learning
We introduce AdaHessian, a second order stochastic optimization algorith...
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Rethinking Batch Normalization in Transformers
The standard normalization method for neural network (NN) models used in...
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ZeroQ: A Novel Zero Shot Quantization Framework
Quantization is a promising approach for reducing the inference time and...
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PyHessian: Neural Networks Through the Lens of the Hessian
We present PyHessian, a new scalable framework that enables fast computa...
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HAWQV2: Hessian Aware traceWeighted Quantization of Neural Networks
Quantization is an effective method for reducing memory footprint and in...
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QBERT: Hessian Based Ultra Low Precision Quantization of BERT
Transformer based architectures have become defacto models used for a r...
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ANODEV2: A Coupled Neural ODE Evolution Framework
It has been observed that residual networks can be viewed as the explici...
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Residual Networks as Nonlinear Systems: Stability Analysis using Linearization
We regard pretrained residual networks (ResNets) as nonlinear systems a...
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JumpReLU: A Retrofit Defense Strategy for Adversarial Attacks
It has been demonstrated that very simple attacks can fool highlysophis...
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Inefficiency of KFAC for Large Batch Size Training
In stochastic optimization, large batch training can leverage parallel r...
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Shallow Learning for Fluid Flow Reconstruction with Limited Sensors and Limited Data
In many applications, it is important to reconstruct a fluid flow field,...
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Trust Region Based Adversarial Attack on Neural Networks
Deep Neural Networks are quite vulnerable to adversarial perturbations. ...
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Parameter ReInitialization through Cyclical Batch Size Schedules
Optimal parameter initialization remains a crucial problem for neural ne...
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On the Computational Inefficiency of Large Batch Sizes for Stochastic Gradient Descent
Increasing the minibatch size for stochastic gradient descent offers si...
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Large batch size training of neural networks with adversarial training and secondorder information
Stochastic Gradient Descent (SGD) methods using randomly selected batche...
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Hessianbased Analysis of Large Batch Training and Robustness to Adversaries
Large batch size training of Neural Networks has been shown to incur acc...
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Zhewei Yao
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