Research

In an age of instant everything, ensuring a positive customer experience has become a top priority for enterprises.

Inspired by progress in large-scale language modelling, we apply a similar approach towards building a single generalist agent beyond the realm of text outputs. The agent, which we refer to as...

DeepMind is a leading artificial intelligence (AI) research company that was founded in London in 2010. It is well known by all of us for…

AUSTIN, Texas – Alex Vermooten and Leah Lucio both feel lucky to have joined U.S. Army Futures Command headquarters as newly minted civil servants in ea...

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On October 29, the United States sanctioned four men and two companies for supporting Unmanned Aerial Vehicle (UAV) programs run by the Islamic Revolutionary Guard Corps (IRGC) and its external operations arms, the Qods Force. “Iran’s proliferation of UAVs across the region threatens international peace and stability. Iran and its proxy militants have used UAVs to attack U.S. forces, our partners, and international shipping,” Deputy Secretary of the Treasury Wally Adeyemo said. “Treasury will continue to hold Iran accountable for its irresponsible and violent acts.”

As Iran expands its global security footprint, it poses an existential threat to not only peace and security in the Middle East, but also to strategic American…

Contains many features across the full range of issues pertaining to international terrorism: terrorist groups, wanted terrorists, and technical pages on various threat-related topics

The transformer neural network was first proposed in a 2017 paper to solve some of the issues of a simple RNN. This guide will introduce you to its operations.

Org profile for Mosaic ML, Inc. on Hugging Face, the AI community building the future.

AGC CV Clips gradients based on the ratio of their norms with weights’ norms. Alibi NLP Replace attention with AliBi AugMix CV Image-preserving data augmentations BlurPool CV Applies blur before po...

[How to Use]-[Suggested Hyperparameters]-[Technical Details]-[Attribution] Computer Vision Layer Freezing gradually makes early modules untrainable (“freezing” them), saving the cost of backpropaga...

The image classification precision is vastly enhanced with the growing complexity of convolutional neural network (CNN) structures. However, the uneve…

The following papers by Leslie N. Smith are covered in this notebook :- A disciplined approach to neural network hyper-parameters: Part 1 – learning rate, batch size, momentum, and weight decay. paper Super-Convergence: Very Fast Training of Neural Networks Using Learning Rates. paper Exploring loss function topology with cyclical learning rates. paper Cyclical Learning Rates for Training Neural Networks. paper This notebook covers all the topics discussed with theory as well as the fasta...

This paper proposes an Information Bottleneck theory based filter pruning method that uses a statistical measure called Mutual Information (MI). The MI between filters and class labels, also called \textit{Relevance}, is computed using the filter's activation maps and the annotations. The filters having High Relevance (HRel) are considered to be more important. Consequently, the least important filters, which have lower Mutual Information with the class labels, are pruned. Unlike the existing MI based pruning methods, the proposed method determines the significance of the filters purely based on their corresponding activation map's relationship with the class labels. Architectures such as LeNet-5, VGG-16, ResNet-56\textcolor{myblue}{, ResNet-110 and ResNet-50 are utilized to demonstrate the efficacy of the proposed pruning method over MNIST, CIFAR-10 and ImageNet datasets. The proposed method shows the state-of-the-art pruning results for LeNet-5, VGG-16, ResNet-56, ResNet-110 and ResNet-50 architectures. In the experiments, we prune 97.98 \%, 84.85 \%, 76.89\%, 76.95\%, and 63.99\% of Floating Point Operation (FLOP)s from LeNet-5, VGG-16, ResNet-56, ResNet-110, and ResNet-50 respectively.} The proposed HRel pruning method outperforms recent state-of-the-art filter pruning methods. Even after pruning the filters from convolutional layers of LeNet-5 drastically (i.e. from 20, 50 to 2, 3, respectively), only a small accuracy drop of 0.52\% is observed. Notably, for VGG-16, 94.98\% parameters are reduced, only with a drop of 0.36\% in top-1 accuracy. \textcolor{myblue}{ResNet-50 has shown a 1.17\% drop in the top-5 accuracy after pruning 66.42\% of the FLOPs.} In addition to pruning, the Information Plane dynamics of Information Bottleneck theory is analyzed for various Convolutional Neural Network architectures with the effect of pruning.

In recent years, high quality deepfake face images generated by Generative Adversarial Networks (GAN) technology have caused serious negative impacts in many fields. Traditional image forensics methods are unable to deal with deepfake that relies on powerful...

Update in March 2019:After TensorFlow developers introduced the APIs of Tensorflow 2.0 on Tensorflow Dev Summit 2019, I have made my decision to turn to PyTorch. TensorFlow is a powerful open-source deep learning framework, supporting various lang

The problem of reducing processing time of large deep learning models is a fundamental challenge in many real-world applications. Early exit methods s…

The increasingly large structure of neural networks makes it difficult to deploy on edge devices with limited computing resources. Network pruning has become one of the most successful model compression methods in recent years. Existing works typically ...