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[1711.05225] CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning

arxiv.org | 1 min read | listed 2 days ago in reddit/MachineLearning/[D] [P] A TensorFlow Re-Implementation of CheXNet - Classification and Localization of Thoracic Diseases

We develop an algorithm that can detect pneumonia from chest X-rays at a level exceeding practicing radiologists. Our algorithm, CheXNet, is a 121-layer convolutional neural network trained on ChestX-ray14, currently the largest publicly available chest X-ray dataset, containing over 100,000 frontal-view X-ray images with 14 diseases. Four practicing academic radiologists annotate a test set, on which we compare the performance of CheXNet to that of radiologists. We find that CheXNet exceeds average radiologist performance on the F1 metric. We extend CheXNet to detect all 14 diseases in ChestX-ray14 and achieve state of the art results on all...

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Learning to Play Minecraft with Video PreTraining (VPT)

openai.com | 7 min read | listed 2 days ago in OpenAI

We trained a neural network to play Minecraft by Video PreTraining (VPT) on a massive unlabeled video dataset of human Minecraft play, while using only a small amount of labeled contractor data. With fine-tuning, our model can learn to craft diamond tools, a task that usually takes proficient humans over

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Transparent memory offloading: more memory at a fraction of the cost and power - Engineering at Meta

engineering.fb.com | 12 min read | listed 5 days ago in Hacker News

-Transparent memory offloading (TMO) is Meta’s data center solution for offering more memory at a fraction of the cost and power of existing technologies -In production since 2021, TMO saves 20 percent to 32 percent of memory per server across millions of servers in our data center fleet We are witnessing massive growth in the [...]Read More...

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Deep learning for necrosis detection using canine perivascular wall tumour whole slide images | Scientific Reports

www.nature.com | 14 min read | listed 2 days ago in Google News
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Swin Transformer supports 3-billion-parameter vision models that can train with higher-resolution images for greater task applicability - Microsoft Research

www.microsoft.com | 10 min read | listed 4 days ago in Microsoft Research Blog

Early last year, our research team from the Visual Computing Group introduced Swin Transformer, a Transformer-based general-purpose computer vision architecture that for the first time beat convolutional neural networks on the important vision benchmark of COCO object detection and did so by a large margin. Convolutional neural networks (CNNs) have long been the architecture of […]

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At the crossroad of the search for spontaneous radiation and the Orch OR consciousness theory - ScienceDirect

www.sciencedirect.com | 1 min read | listed 5 days ago in reddit/singularity/Some scientists attribute consciousness to quantum computations in the brain. This in turn hinges on the notion that gravity could play a role in how quantum effects disappear, or "collapse." But a series of experiments failed to find evidence in support of a gravity-related quantum collapse model.

In this paper we perform a critical analysis of the Orch OR consciousness theory at the crossroad with the newest experimental results coming from the…

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[2007.08554] A New Look at Ghost Normalization

arxiv.org | 1 min read | listed 2 days ago in reddit/MachineLearning/[P] Farewell, CUDA OOM: Automatic Gradient Accumulation

Batch normalization (BatchNorm) is an effective yet poorly understood technique for neural network optimization. It is often assumed that the degradation in BatchNorm performance to smaller batch sizes stems from it having to estimate layer statistics using smaller sample sizes. However, recently, Ghost normalization (GhostNorm), a variant of BatchNorm that explicitly uses smaller sample sizes for normalization, has been shown to improve upon BatchNorm in some datasets. Our contributions are: (i) we uncover a source of regularization that is unique to GhostNorm, and not simply an extension from BatchNorm, (ii) three types of GhostNorm impleme...

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[2206.10498] Large Language Models Still Can't Plan (A Benchmark for LLMs on Planning and Reasoning about Change)

arxiv.org | 2 min read | listed 3 days ago in reddit/singularity/AI Planning and Spatial Reasoning

The recent advances in large language models (LLMs) have transformed the field of natural language processing (NLP). From GPT-3 to PaLM, the state-of-the-art performance on natural language tasks is being pushed forward with every new large language model. Along with natural language abilities, there has been a significant interest in understanding whether such models, trained on enormous amounts of data, exhibit reasoning capabilities. Hence there has been interest in developing benchmarks for various reasoning tasks and the preliminary results from testing LLMs over such benchmarks seem mostly positive. However, the current benchmarks are r...

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Engineering topological states in atom-based semiconductor quantum dots | Nature

www.nature.com | 19 min read | listed 9 hours ago in Synced Global AI Weekly — Synced Global AI Weekly 2022.6.25

Precision-engineered devices consisting of a linear array of ten quantum dots are used to realize both the trivial and topological phases of the many-body Su–Schrieffer–Heeger model.

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Improving AI-based defenses to disrupt human-operated ransomware - Microsoft Security Blog

www.microsoft.com | 8 min read | listed 4 days ago in Bing News

To disrupt human-operated ransomware attacks as early as possible, we enhanced the AI-based protections in Microsoft Defender for Endpoint with a range of specialized machine learning techniques that swiftly identify and block malicious files, processes, or behavior observed during active attacks.

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Hypersim: A Photorealistic Synthetic Dataset for Holistic Indoor Scene Understanding - Apple Machine Learning Research

machinelearning.apple.com | 1 min read | listed 3 days ago in reddit/deeplearning/Framework for synthetic dataset generation (computer vision/object detection/image classification)

For many fundamental scene understanding tasks, it is difficult or impossible to obtain per-pixel ground truth labels from real images. We…

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[1906.03548] Four Things Everyone Should Know to Improve Batch Normalization

arxiv.org | 1 min read | listed 2 days ago in reddit/MachineLearning/[P] Farewell, CUDA OOM: Automatic Gradient Accumulation

A key component of most neural network architectures is the use of normalization layers, such as Batch Normalization. Despite its common use and large utility in optimizing deep architectures, it has been challenging both to generically improve upon Batch Normalization and to understand the circumstances that lend themselves to other enhancements. In this paper, we identify four improvements to the generic form of Batch Normalization and the circumstances under which they work, yielding performance gains across all batch sizes while requiring no additional computation during training. These contributions include proposing a method for reasoni...

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Prime Video's work on 3-D scene reconstruction, image representation - Amazon Science

www.amazon.science | 6 min read | listed 3 days ago in Amazon Science Homepage

CVPR papers examine the recovery of 3-D information from camera movement and learning general representations from weakly annotated data.

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Augmented Dickey–Fuller test - Wikipedia

en.wikipedia.org | 5 min read | listed 2 days ago in reddit/MachineLearning/[R] 🌎 Sales prediction: how improve accuracy of Multivariate Time Series kernel from external features & data and get 2nd place in Kaggle competition
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[1812.06162] An Empirical Model of Large-Batch Training

arxiv.org | 1 min read | listed 2 days ago in reddit/MachineLearning/[P] Farewell, CUDA OOM: Automatic Gradient Accumulation

In an increasing number of domains it has been demonstrated that deep learning models can be trained using relatively large batch sizes without sacrificing data efficiency. However the limits of this massive data parallelism seem to differ from domain to domain, ranging from batches of tens of thousands in ImageNet to batches of millions in RL agents that play the game Dota 2. To our knowledge there is limited conceptual understanding of why these limits to batch size differ or how we might choose the correct batch size in a new domain. In this paper, we demonstrate that a simple and easy-to-measure statistic called the gradient noise scale p...

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Genome-wide mapping of somatic mutation rates uncovers drivers of cancer | Nature Biotechnology

www.nature.com | 16 min read | listed 5 days ago in Twitter — popular in #ai

Cancer driver mutations are identified by predicting neutral mutation rates across the entire genome.

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A calcium-based plasticity model for predicting long-term potentiation and depression in the neocortex | Nature Communications

www.nature.com | 32 min read | listed 5 days ago in reddit/singularity/Does one simple rule underlie learning in the brain?

The study of learning algorithms in the neocortex requires comprehensive knowledge of synaptic plasticity between its diverse cell types, which is currently lacking. Chindemi et al. describe a modeling approach to fill these gaps in experimental literature, and predict the features of synaptic plasticity in vivo.

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[2102.01350] Graph Coarsening with Neural Networks

arxiv.org | 1 min read | listed 6 days ago in reddit/MachineLearning/[D] Machine Learning - WAYR (What Are You Reading) - Week 140

As large-scale graphs become increasingly more prevalent, it poses significant computational challenges to process, extract and analyze large graph data. Graph coarsening is one popular technique to reduce the size of a graph while maintaining essential properties. Despite rich graph coarsening literature, there is only limited exploration of data-driven methods in the field. In this work, we leverage the recent progress of deep learning on graphs for graph coarsening. We first propose a framework for measuring the quality of coarsening algorithm and show that depending on the goal, we need to carefully choose the Laplace operator on the coar...

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Induction of mouse totipotent stem cells by a defined chemical cocktail | Nature

www.nature.com | 1 min read | listed 2 days ago in reddit/singularity/Chinese scientists have found a way to reprogram stem cells so they have potential to generate an entire organism. In a study on mice cells published in the journal Nature journal, Tsinghua University researchers said the stem cells could create life without the need for reproductive cells
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[2205.12615] Autoformalization with Large Language Models

arxiv.org | 1 min read | listed 5 days ago in Lobsters: ai - Artificial Intelligence, Machine Learning

Autoformalization is the process of automatically translating from natural language mathematics to formal specifications and proofs. A successful autoformalization system could advance the fields of formal verification, program synthesis, and artificial intelligence. While the long-term goal of autoformalization seemed elusive for a long time, we show large language models provide new prospects towards this goal. We make the surprising observation that LLMs can correctly translate a significant portion ($25.3\%$) of mathematical competition problems perfectly to formal specifications in Isabelle/HOL. We demonstrate the usefulness of this proc...

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[2001.06826] Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement

arxiv.org | 1 min read | listed 6 days ago in reddit/deeplearning/why are we dividing image gradients with these values?

The paper presents a novel method, Zero-Reference Deep Curve Estimation (Zero-DCE), which formulates light enhancement as a task of image-specific curve estimation with a deep network. Our method trains a lightweight deep network, DCE-Net, to estimate pixel-wise and high-order curves for dynamic range adjustment of a given image. The curve estimation is specially designed, considering pixel value range, monotonicity, and differentiability. Zero-DCE is appealing in its relaxed assumption on reference images, i.e., it does not require any paired or unpaired data during training. This is achieved through a set of carefully formulated non-referen...

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Machine learning predictive model for screening of LNP-based mRNA vaccines

www.news-medical.net | 2 min read | listed 6 days ago in Google News

In this new article publication from Acta Pharmaceutica Sinica B, authors Wei Wang, Shuo Feng, Zhuyifan Ye, Hanlu Gao, Jinzhong Lin and Defang Ouyang from University of Macau, Macau, China and Fudan University, Shanghai, China discuss the prediction of lipid nanoparticles for mRNA vaccines by machine learning algorithms.

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Deep-learning density functional theory Hamiltonian for efficient ab initio electronic-structure calculation | Nature Computational Science

www.nature.com | 1 min read | listed 1 day ago in Google Alerts — Daily Digest

A deep neural network method is developed to learn the mapping function from atomic structure to density functional theory (DFT) Hamiltonian, which helps address the accuracy–efficiency dilemma of DFT and is useful for studying large-scale materials.

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Research Highlights: Emergent Abilities of Large Language Models - insideBIGDATA

insidebigdata.com | 1 min read | listed 6 days ago in insideBIGDATA - insideBIGDATA: Clear, Concise Insights on Big Data Strategies

In this regular column, we take a look at highlights for important research topics of the day for big data, data science, machine learning, AI and deep learning. It’s important to keep connected with the research arm of the field in order to see where we’re headed. We drill down into topical areas: data science, machine learning, AI, and deep learning. Enjoy!

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Field Oriented Control (FOC) - A Deep Dive

www.pmdcorp.com | 7 min read | listed 5 days ago in reddit/robotics/Run BLDCs without Encoders

Explore multi-phase motor control, including field-oriented control (FOC), and see what works best for positioning and high-speed spinning applications.

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