Scale (ratio)
View article: An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale Open
While the Transformer architecture has become the de-facto standard for natural language processing tasks, its applications to computer vision remain limited. In vision, attention is either applied in conjunction with convolutional network…
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Google Earth Engine: Planetary-scale geospatial analysis for everyone Open
El presente artículo evidencia principalmente los beneficios de la plataforma Google Earth Engine para el análisis a nivel planetario del clima y la superficie de la tierra. El catálogo de varios petabytes de imágenes satelitales, con un r…
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MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications Open
We present a class of efficient models called MobileNets for mobile and embedded vision applications. MobileNets are based on a streamlined architecture that uses depth-wise separable convolutions to build light weight deep neural networks…
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TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems Open
TensorFlow is an interface for expressing machine learning algorithms, and an implementation for executing such algorithms. A computation expressed using TensorFlow can be executed with little or no change on a wide variety of heterogeneou…
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TensorFlow: A system for large-scale machine learning Open
TensorFlow is a machine learning system that operates at large scale and in heterogeneous environments. TensorFlow uses dataflow graphs to represent computation, shared state, and the operations that mutate that state. It maps the nodes of…
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PartitionFinder 2: New Methods for Selecting Partitioned Models of Evolution for Molecular and Morphological Phylogenetic Analyses Open
PartitionFinder 2 is a program for automatically selecting best-fit partitioning schemes and models of evolution for phylogenetic analyses. PartitionFinder 2 is substantially faster and more efficient than version 1, and incorporates many …
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EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks Open
Convolutional Neural Networks (ConvNets) are commonly developed at a fixed resource budget, and then scaled up for better accuracy if more resources are available. In this paper, we systematically study model scaling and identify that care…
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GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium Open
Generative Adversarial Networks (GANs) excel at creating realistic images with complex models for which maximum likelihood is infeasible. However, the convergence of GAN training has still not been proved. We propose a two time-scale updat…
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Best Practices for Developing and Validating Scales for Health, Social, and Behavioral Research: A Primer Open
Scale development and validation are critical to much of the work in the health, social, and behavioral sciences. However, the constellation of techniques required for scale development and evaluation can be onerous, jargon-filled, unfamil…
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Juicer Provides a One-Click System for Analyzing Loop-Resolution Hi-C Experiments Open
Hi-C experiments explore the 3D structure of the genome, generating terabases of data to create high-resolution contact maps. Here, we introduce Juicer, an open-source tool for analyzing terabase-scale Hi-C datasets. Juicer allows users wi…
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Carbon capture and storage (CCS): the way forward Open
Carbon capture and storage (CCS) is vital to climate change mitigation, and has application across the economy, in addition to facilitating atmospheric carbon dioxide removal resulting in emissions offsets and net negative emissions. This …
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Evolutionary-scale prediction of atomic-level protein structure with a language model Open
Recent advances in machine learning have leveraged evolutionary information in multiple sequence alignments to predict protein structure. We demonstrate direct inference of full atomic-level protein structure from primary sequence using a …
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The potential for artificial intelligence in healthcare Open
The complexity and rise of data in healthcare means that artificial intelligence (AI) will increasingly be applied within the field. Several types of AI are already being employed by payers and providers of care, and life sciences companie…
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Deep Neural Networks for YouTube Recommendations Open
YouTube represents one of the largest scale and most sophisticated industrial recommendation systems in existence. In this paper, we describe the system at a high level and focus on the dramatic performance improvements brought by deep lea…
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Res2Net: A New Multi-Scale Backbone Architecture Open
Representing features at multiple scales is of great importance for numerous vision tasks. Recent advances in backbone convolutional neural networks (CNNs) continually demonstrate stronger multi-scale representation ability, leading to con…
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AGREE—Analytical GREEnness Metric Approach and Software Open
Green analytical chemistry focuses on making analytical procedures more environmentally benign and safer to humans. The amounts and toxicity of reagents, generated waste, energy requirements, the number of procedural steps, miniaturization…
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DOTA: A Large-Scale Dataset for Object Detection in Aerial Images Open
Object detection is an important and challenging problem in computer vision. Although the past decade has witnessed major advances in object detection in natural scenes, such successes have been slow to aerial imagery, not only because of …
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The 30 m annual land cover dataset and its dynamics in China from 1990 to 2019 Open
Land cover (LC) determines the energy exchange, water and carbon cycle between Earth's spheres. Accurate LC information is a fundamental parameter for the environment and climate studies. Considering that the LC in China has been altered d…
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MoleculeNet: a benchmark for molecular machine learning Open
A large scale benchmark for molecular machine learning consisting of multiple public datasets, metrics, featurizations and learning algorithms.
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Llama 2: Open Foundation and Fine-Tuned Chat Models Open
In this work, we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama 2-Chat, are optimized for dial…
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Large Scale GAN Training for High Fidelity Natural Image Synthesis Open
Despite recent progress in generative image modeling, successfully generating high-resolution, diverse samples from complex datasets such as ImageNet remains an elusive goal. To this end, we train Generative Adversarial Networks at the lar…
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Multivariable association discovery in population-scale meta-omics studies Open
It is challenging to associate features such as human health outcomes, diet, environmental conditions, or other metadata to microbial community measurements, due in part to their quantitative properties. Microbiome multi-omics are typicall…
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GPT-4 Technical Report Open
We report the development of GPT-4, a large-scale, multimodal model which can accept image and text inputs and produce text outputs. While less capable than humans in many real-world scenarios, GPT-4 exhibits human-level performance on var…
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A global database of COVID-19 vaccinations Open
An effective rollout of vaccinations against COVID-19 offers the most promising prospect of bringing the pandemic to an end. We present the Our World in Data COVID-19 vaccination dataset, a global public dataset that tracks the scale and r…
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VoxCeleb: A Large-Scale Speaker Identification Dataset Open
Most existing datasets for speaker identification contain samples obtained under quite constrained conditions, and are usually hand-annotated, hence limited in size. The goal of this paper is to generate a large scale text-independent spea…
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<span>PANTHER</span>: Making genome‐scale phylogenetics accessible to all Open
Phylogenetics is a powerful tool for analyzing protein sequences, by inferring their evolutionary relationships to other proteins. However, phylogenetics analyses can be challenging: they are computationally expensive and must be performed…
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3D Semantic Parsing of Large-Scale Indoor Spaces Open
In this paper, we propose a method for semantic parsing the 3D point cloud of an entire building using a hierarchical approach: first, the raw data is parsed into semantically meaningful spaces (e.g. rooms, etc) that are aligned into a can…
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Conceptualizing soil organic matter into particulate and mineral‐associated forms to address global change in the 21st century Open
Managing soil organic matter (SOM) stocks to address global change challenges requires well‐substantiated knowledge of SOM behavior that can be clearly communicated between scientists, management practitioners, and policy makers. However, …
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Large Scale GAN Training for High Fidelity Natural Image Synthesis Open
Despite recent progress in generative image modeling, successfully generating high-resolution, diverse samples from complex datasets such as ImageNet remains an elusive goal. To this end, we train Generative Adversarial Networks at the lar…
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SoilGrids 2.0: producing soil information for the globe with quantified spatial uncertainty Open
SoilGrids produces maps of soil properties for the entire globe at medium spatial resolution (250 m cell size) using state-of-the-art machine learning methods to generate the necessary models. It takes as inputs soil observations from abou…