
HessianAware Pruning and Optimal Neural Implant
Pruning is an effective method to reduce the memory footprint and FLOPs ...
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IBERT: Integeronly BERT Quantization
Transformer based models, like BERT and RoBERTa, have achieved stateof...
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HAWQV3: Dyadic Neural Network Quantization
Quantization is one of the key techniques used to make Neural Networks (...
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Boundary thickness and robustness in learning models
Robustness of machine learning models to various adversarial and nonadv...
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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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Checkmate: Breaking the Memory Wall with Optimal Tensor Rematerialization
Modern neural networks are increasingly bottlenecked by the limited capa...
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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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Inefficiency of KFAC for Large Batch Size Training
In stochastic optimization, large batch training can leverage parallel r...
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ANODE: Unconditionally Accurate MemoryEfficient Gradients for Neural ODEs
Residual neural networks can be viewed as the forward Euler discretizati...
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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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Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
Gliomas are the most common primary brain malignancies, with different d...
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A Novel Domain Adaptation Framework for Medical Image Segmentation
We propose a segmentation framework that uses deep neural networks and i...
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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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CLAIRE: A distributedmemory solver for constrained large deformation diffeomorphic image registration
We introduce CLAIRE, a distributedmemory algorithm and software for sol...
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CoDesign of Deep Neural Nets and Neural Net Accelerators for Embedded Vision Applications
Deep Learning is arguably the most rapidly evolving research area in rec...
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SqueezeNext: HardwareAware Neural Network Design
One of the main barriers for deploying neural networks on embedded syste...
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PDEconstrained optimization in medical image analysis
PDEconstrained optimization problems find many applications in medical ...
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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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Integrated Model, Batch and Domain Parallelism in Training Neural Networks
We propose a new integrated method of exploiting model, batch and domain...
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Integrated Model and Data Parallelism in Training Neural Networks
We propose a new integrated method of exploiting both model and data par...
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Distributedmemory large deformation diffeomorphic 3D image registration
We present a parallel distributedmemory algorithm for large deformation...
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Amir Gholami
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