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09/02/2023
RenAIssance: A Survey into AI Text-to-Image Generation in the Era of Large Model
Text-to-image generation (TTI) refers to the usage of models that could ...
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08/02/2023
DeepSpeed-Chat: Easy, Fast and Affordable RLHF Training of ChatGPT-like Models at All Scales
ChatGPT-like models have revolutionized various applications in artifici...
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07/19/2023
ZeroQuant-FP: A Leap Forward in LLMs Post-Training W4A8 Quantization Using Floating-Point Formats
In the complex domain of large language models (LLMs), striking a balanc...
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05/16/2023
Selective Guidance: Are All the Denoising Steps of Guided Diffusion Important?
This study examines the impact of optimizing the Stable Diffusion (SD) g...
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03/15/2023
A Comprehensive Study on Post-Training Quantization for Large Language Models
Post-training quantization () had been recently shown as a compromising ...
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03/13/2023
Scaling Vision-Language Models with Sparse Mixture of Experts
The field of natural language processing (NLP) has made significant stri...
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01/27/2023
Understanding INT4 Quantization for Transformer Models: Latency Speedup, Composability, and Failure Cases
Improving the deployment efficiency of transformer-based language models...
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12/07/2022
DeepSpeed Data Efficiency: Improving Deep Learning Model Quality and Training Efficiency via Efficient Data Sampling and Routing
Recent advances on deep learning models come at the price of formidable ...
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11/17/2022
Random-LTD: Random and Layerwise Token Dropping Brings Efficient Training for Large-scale Transformers
Large-scale transformer models have become the de-facto architectures fo...
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07/29/2022
BiFeat: Supercharge GNN Training via Graph Feature Quantization
Graph Neural Networks (GNNs) is a promising approach for applications wi...
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06/04/2022
ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers
How to efficiently serve ever-larger trained natural language models in ...
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06/04/2022
Extreme Compression for Pre-trained Transformers Made Simple and Efficient
Extreme compression, particularly ultra-low bit precision (binary/ternar...
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01/14/2022
DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale
As the training of giant dense models hits the boundary on the availabil...
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09/08/2021
What's Hidden in a One-layer Randomly Weighted Transformer?
We demonstrate that, hidden within one-layer randomly weighted neural ne...
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07/13/2021
How Much Can CLIP Benefit Vision-and-Language Tasks?
Most existing Vision-and-Language (V L) models rely on pre-trained vis...
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05/30/2021
MLPruning: A Multilevel Structured Pruning Framework for Transformer-based Models
Pruning is an effective method to reduce the memory footprint and comput...
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04/29/2021
ActNN: Reducing Training Memory Footprint via 2-Bit Activation Compressed Training
The increasing size of neural network models has been critical for impro...
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03/31/2021
Q-ASR: Integer-only Zero-shot Quantization for Efficient Speech Recognition
End-to-end neural network models achieve improved performance on various...
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03/25/2021
A Survey of Quantization Methods for Efficient Neural Network Inference
As soon as abstract mathematical computations were adapted to computatio...
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01/22/2021
Hessian-Aware Pruning and Optimal Neural Implant
Pruning is an effective method to reduce the memory footprint and FLOPs ...
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01/05/2021
I-BERT: Integer-only BERT Quantization
Transformer based models, like BERT and RoBERTa, have achieved state-of-...
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11/20/2020
HAWQV3: Dyadic Neural Network Quantization
Quantization is one of the key techniques used to make Neural Networks (...
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10/27/2020
A Statistical Framework for Low-bitwidth Training of Deep Neural Networks
Fully quantized training (FQT), which uses low-bitwidth hardware by quan...
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10/12/2020
MAF: Multimodal Alignment Framework for Weakly-Supervised Phrase Grounding
Phrase localization is a task that studies the mapping from textual phra...
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08/26/2020
Benchmarking Semi-supervised Federated Learning
Federated learning promises to use the computational power of edge devic...
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06/01/2020
ADAHESSIAN: An Adaptive Second Order Optimizer for Machine Learning
We introduce AdaHessian, a second order stochastic optimization algorith...
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03/17/2020
Rethinking Batch Normalization in Transformers
The standard normalization method for neural network (NN) models used in...
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01/01/2020
ZeroQ: A Novel Zero Shot Quantization Framework
Quantization is a promising approach for reducing the inference time and...
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12/16/2019
PyHessian: Neural Networks Through the Lens of the Hessian
We present PyHessian, a new scalable framework that enables fast computa...
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11/10/2019
HAWQ-V2: Hessian Aware trace-Weighted Quantization of Neural Networks
Quantization is an effective method for reducing memory footprint and in...
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09/12/2019
Q-BERT: Hessian Based Ultra Low Precision Quantization of BERT
Transformer based architectures have become de-facto models used for a r...
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06/10/2019
ANODEV2: A Coupled Neural ODE Evolution Framework
It has been observed that residual networks can be viewed as the explici...
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05/31/2019
Residual Networks as Nonlinear Systems: Stability Analysis using Linearization
We regard pre-trained residual networks (ResNets) as nonlinear systems a...
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04/07/2019
JumpReLU: A Retrofit Defense Strategy for Adversarial Attacks
It has been demonstrated that very simple attacks can fool highly-sophis...
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03/14/2019
Inefficiency of K-FAC for Large Batch Size Training
In stochastic optimization, large batch training can leverage parallel r...
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02/20/2019
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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12/16/2018
Trust Region Based Adversarial Attack on Neural Networks
Deep Neural Networks are quite vulnerable to adversarial perturbations. ...
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12/04/2018
Parameter Re-Initialization through Cyclical Batch Size Schedules
Optimal parameter initialization remains a crucial problem for neural ne...
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11/30/2018
On the Computational Inefficiency of Large Batch Sizes for Stochastic Gradient Descent
Increasing the mini-batch size for stochastic gradient descent offers si...
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10/02/2018
Large batch size training of neural networks with adversarial training and second-order information
Stochastic Gradient Descent (SGD) methods using randomly selected batche...
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02/22/2018
