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Found 41 papers, 31 papers with code
What’s Hidden in a One-layer Randomly Weighted Transformer?
Hidden within a one-layer randomly weighted Transformer, we find that subnetworks that can achieve 29. 45/17. 29 BLEU on IWSLT14/WMT14.
Selective Guidance: Are All the Denoising Steps of Guided Diffusion Important?
This study examines the impact of optimizing the Stable Diffusion (SD) guided inference pipeline.
ZeroQuant-V2: Exploring Post-training Quantization in LLMs from Comprehensive Study to Low Rank Compensation
Post-training quantization (PTQ) has emerged as a promising technique for mitigating memory consumption and computational costs in large language models (LLMs).
Scaling Vision-Language Models with Sparse Mixture of Experts
The field of natural language processing (NLP) has made significant strides in recent years, particularly in the development of large-scale vision-language models (VLMs).
Understanding INT4 Quantization for Transformer Models: Latency Speedup, Composability, and Failure Cases
Improving the deployment efficiency of transformer-based language models has been challenging given their high computation and memory cost.
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 training cost.
Random-LTD: Random and Layerwise Token Dropping Brings Efficient Training for Large-scale Transformers
Large-scale transformer models have become the de-facto architectures for various machine learning applications, e. g., CV and NLP.
BiFeat: Supercharge GNN Training via Graph Feature Quantization
We identify the main accuracy impact factors in graph feature quantization and theoretically prove that BiFeat training converges to a network where the loss is within $\epsilon$ of the optimal loss of uncompressed network.
ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers
How to efficiently serve ever-larger trained natural language models in practice has become exceptionally challenging even for powerful cloud servers due to their prohibitive memory/computation requirements.
Extreme Compression for Pre-trained Transformers Made Simple and Efficient
Extreme compression, particularly ultra-low bit precision (binary/ternary) quantization, has been proposed to fit large NLP models on resource-constraint devices.
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 availability and capability of the hardware resources today, Mixture-of-Experts (MoE) models become one of the most promising model architectures due to their significant training cost reduction compared to a quality-equivalent dense model.
What's Hidden in a One-layer Randomly Weighted Transformer?
Hidden within a one-layer randomly weighted Transformer, we find that subnetworks that can achieve 29. 45/17. 29 BLEU on IWSLT14/WMT14.
How Much Can CLIP Benefit Vision-and-Language Tasks?
Most existing Vision-and-Language (V&L) models rely on pre-trained visual encoders, using a relatively small set of manually-annotated data (as compared to web-crawled data), to perceive the visual world.
Ranked #4 on
Vision and Language Navigation
on RxR
(using extra training data)
LEAP: Learnable Pruning for Transformer-based Models
Moreover, in order to reduce hyperparameter tuning, a novel adaptive regularization coefficient is deployed to control the regularization penalty adaptively.
ActNN: Reducing Training Memory Footprint via 2-Bit Activation Compressed Training
On all these tasks, ActNN compresses the activation to 2 bits on average, with negligible accuracy loss.
Integer-only Zero-shot Quantization for Efficient Speech Recognition
End-to-end neural network models achieve improved performance on various automatic speech recognition (ASR) tasks.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
A Survey of Quantization Methods for Efficient Neural Network Inference
Thus, it is not surprising that quantization has emerged recently as an important and very active sub-area of research in the efficient implementation of computations associated with Neural Networks.
Hessian-Aware Pruning and Optimal Neural Implant
To address this problem, we introduce a new Hessian Aware Pruning (HAP) method coupled with a Neural Implant approach that uses second-order sensitivity as a metric for structured pruning.
I-BERT: Integer-only BERT Quantization
Transformer based models, like BERT and RoBERTa, have achieved state-of-the-art results in many Natural Language Processing tasks.
Natural Language Inference
Natural Language Understanding
+1
HAWQV3: Dyadic Neural Network Quantization
Current low-precision quantization algorithms often have the hidden cost of conversion back and forth from floating point to quantized integer values.
A Statistical Framework for Low-bitwidth Training of Deep Neural Networks
We show that the FQT gradient is an unbiased estimator of the QAT gradient, and we discuss the impact of gradient quantization on its variance.
MAF: Multimodal Alignment Framework for Weakly-Supervised Phrase Grounding
Phrase localization is a task that studies the mapping from textual phrases to regions of an image.
Improving Semi-supervised Federated Learning by Reducing the Gradient Diversity of Models
Replacing BN with the recently-proposed Group Normalization (GN) can reduce gradient diversity and improve test accuracy.
ADAHESSIAN: An Adaptive Second Order Optimizer for Machine Learning
We introduce ADAHESSIAN, a second order stochastic optimization algorithm which dynamically incorporates the curvature of the loss function via ADAptive estimates of the HESSIAN.
PowerNorm: Rethinking Batch Normalization in Transformers
To address this, we propose Power Normalization (PN), a novel normalization scheme that resolves this issue by (i) relaxing zero-mean normalization in BN, (ii) incorporating a running quadratic mean instead of per batch statistics to stabilize fluctuations, and (iii) using an approximate backpropagation for incorporating the running statistics in the forward pass.
Ranked #12 on
Machine Translation
on WMT2014 English-German
ZeroQ: A Novel Zero Shot Quantization Framework
Importantly, ZeroQ has a very low computational overhead, and it can finish the entire quantization process in less than 30s (0. 5\% of one epoch training time of ResNet50 on ImageNet).
Ranked #1 on
Data Free Quantization
on CIFAR10
(CIFAR-10 W8A8 Top-1 Accuracy metric)
PyHessian: Neural Networks Through the Lens of the Hessian
To illustrate this, we analyze the effect of residual connections and Batch Normalization layers on the trainability of neural networks.
ANODEV2: A Coupled Neural ODE Framework
It has been observed that residual networks can be viewed as the explicit Euler discretization of an Ordinary Differential Equation (ODE).
HAWQ-V2: Hessian Aware trace-Weighted Quantization of Neural Networks
However, the search space for a mixed-precision quantization is exponential in the number of layers.
Q-BERT: Hessian Based Ultra Low Precision Quantization of BERT
In particular, we propose a new group-wise quantization scheme, and we use a Hessian based mix-precision method to compress the model further.
ANODEV2: A Coupled Neural ODE Evolution Framework
It has been observed that residual networks can be viewed as the explicit Euler discretization of an Ordinary Differential Equation (ODE).
Residual Networks as Nonlinear Systems: Stability Analysis using Linearization
We regard pre-trained residual networks (ResNets) as nonlinear systems and use linearization, a common method used in the qualitative analysis of nonlinear systems, to understand the behavior of the networks under small perturbations of the input images.
HAWQ: Hessian AWare Quantization of Neural Networks with Mixed-Precision
Another challenge is a similar factorial complexity for determining block-wise fine-tuning order when quantizing the model to a target precision.
JumpReLU: A Retrofit Defense Strategy for Adversarial Attacks
To complement these approaches, we propose a very simple and inexpensive strategy which can be used to ``retrofit'' a previously-trained network to improve its resilience to adversarial attacks.
Inefficiency of K-FAC for Large Batch Size Training
In stochastic optimization, using large batch sizes during training can leverage parallel resources to produce faster wall-clock training times per training epoch.
Shallow Neural Networks for Fluid Flow Reconstruction with Limited Sensors
In many applications, it is important to reconstruct a fluid flow field, or some other high-dimensional state, from limited measurements and limited data.
Trust Region Based Adversarial Attack on Neural Networks
To address this problem, we present a new family of trust region based adversarial attacks, with the goal of computing adversarial perturbations efficiently.
Parameter Re-Initialization through Cyclical Batch Size Schedules
We demonstrate the ability of our method to improve language modeling performance by up to 7. 91 perplexity and reduce training iterations by up to $61\%$, in addition to its flexibility in enabling snapshot ensembling and use with adversarial training.
Ranked #51 on
Natural Language Inference
on SNLI
On the Computational Inefficiency of Large Batch Sizes for Stochastic Gradient Descent
Increasing the mini-batch size for stochastic gradient descent offers significant opportunities to reduce wall-clock training time, but there are a variety of theoretical and systems challenges that impede the widespread success of this technique.
Large batch size training of neural networks with adversarial training and second-order information
Our method exceeds the performance of existing solutions in terms of both accuracy and the number of SGD iterations (up to 1\% and $5\times$, respectively).
Hessian-based Analysis of Large Batch Training and Robustness to Adversaries
Extensive experiments on multiple networks show that saddle-points are not the cause for generalization gap of large batch size training, and the results consistently show that large batch converges to points with noticeably higher Hessian spectrum.
