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[1911.07023] Effectively Unbiased FID and Inception Score and where to find them

arxiv.org | 1 min read | listed 8 hours ago in reddit/MachineLearning/[P] Frechet Inception Distance

This paper shows that two commonly used evaluation metrics for generative models, the Fréchet Inception Distance (FID) and the Inception Score (IS), are biased -- the expected value of the score computed for a finite sample set is not the true value of the score. Worse, the paper shows that the bias term depends on the particular model being evaluated, so model A may get a better score than model B simply because model A's bias term is smaller. This effect cannot be fixed by evaluating at a fixed number of samples. This means all comparisons using FID or IS as currently computed are unreliable. We then show how to extrapolate the score to o...

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[2103.09396] Pros and Cons of GAN Evaluation Measures: New Developments

arxiv.org | 1 min read | listed 8 hours ago in reddit/MachineLearning/[P] Frechet Inception Distance

This work is an update of a previous paper on the same topic published a few years ago. With the dramatic progress in generative modeling, a suite of new quantitative and qualitative techniques to evaluate models has emerged. Although some measures such as Inception Score, Frechet Inception Distance, Precision-Recall, and Perceptual Path Length are relatively more popular, GAN evaluation is not a settled issue and there is still room for improvement. Here, I describe new dimensions that are becoming important in assessing models (e.g. bias and fairness) and discuss the connection between GAN evaluation and deepfakes. These are important areas...

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

www.nature.com | 19 min read | listed 10 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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[P] Real-life implementation of the Traveling Salesman Problem🍻 : MachineLearning

www.reddit.com | 1 min read | listed 12 hours ago in reddit/MachineLearning/[P] Real-life implementation of the Traveling Salesman Problem🍻

0 votes and 2 comments so far on Reddit

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[1903.12141] IMAE for Noise-Robust Learning: Mean Absolute Error Does Not Treat Examples Equally and Gradient Magnitude's Variance Matters

arxiv.org | 1 min read | listed 15 hours ago in reddit/MachineLearning/[Research] Not all our papers get published, therefore it is enjoyable to see our released papers become a true foundation for other works

In this work, we study robust deep learning against abnormal training data from the perspective of example weighting built in empirical loss functions, i.e., gradient magnitude with respect to logits, an angle that is not thoroughly studied so far. Consequently, we have two key findings: (1) Mean Absolute Error (MAE) Does Not Treat Examples Equally. We present new observations and insightful analysis about MAE, which is theoretically proved to be noise-robust. First, we reveal its underfitting problem in practice. Second, we analyse that MAE's noise-robustness is from emphasising on uncertain examples instead of treating training samples equa...

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Asymmetric Loss Functions for Learning with Noisy Labels

proceedings.mlr.press | 3 min read | listed 15 hours ago in reddit/MachineLearning/[Research] Not all our papers get published, therefore it is enjoyable to see our released papers become a true foundation for other works

Robust loss functions are essential for training deep neural networks with better generalization power in the presence of noisy labels. Symmetric loss functions are confirmed to be robust to label ...

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[1905.11233] Derivative Manipulation for General Example Weighting

arxiv.org | 1 min read | listed 15 hours ago in reddit/MachineLearning/[Research] Not all our papers get published, therefore it is enjoyable to see our released papers become a true foundation for other works

Real-world large-scale datasets usually contain noisy labels and are imbalanced. Therefore, we propose derivative manipulation (DM), a novel and general example weighting approach for training robust deep models under these adverse conditions. DM has two main merits. First, loss function and example weighting are common techniques in the literature. DM reveals their connection (a loss function does example weighting) and is a replacement of both. Second, despite that a loss defines an example weighting scheme by its derivative, in the loss design, we need to consider whether it is differentiable. Instead, DM is more flexible by directly mod...

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Sleep Staging Using End-to-End Deep Learning Model | NSS

www.dovepress.com | 25 min read | listed 1 day ago in Google News

to introduce an end-to-end (sound-to-sleep stages) deep learning model for sound-based sleep staging designed to work with audio from microphone chips.

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Characterizing seismic facies in a carbonate reservoir, using machine learning offshore Brazil

worldoil.com | 12 min read | listed 1 day ago in Google News

Seismic data can provide useful information for prospect identification and reservoir characterization. Combining seismic attributes helps identify different patterns, thus improving geological characterization. Machine learning applied to seismic interpretation is very useful in assisting with data classification limitations.

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A Phenotype Ontology for Autism Spectrum Disorder Was Created By Using Natural Language Processing on Electronic Health Records

www.physiciansweekly.com | 2 min read | listed 1 day ago in Google News

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder characterized by restricted, repetitive behavior and impaired social communication and...

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A Chronicle of the Application of Differential Privacy to the 2020 Census · Harvard Data Science Review

hdsr.mitpress.mit.edu | 67 min read | listed 1 day ago in Harvard Data Science Review
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Private Numbers in Public Policy: Census, Differential Privacy, and Redistricting · Harvard Data Science Review

hdsr.mitpress.mit.edu | 55 min read | listed 1 day ago in Harvard Data Science Review
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The Effect of Differentially Private Noise Injection on Sampling Efficiency and Funding Allocations: Evidence From the 1940 Census · Harvard Data Science Review

hdsr.mitpress.mit.edu | 52 min read | listed 1 day ago in Harvard Data Science Review
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Assessing the Impact of Differential Privacy on Measures of Population and Racial Residential Segregation · Harvard Data Science Review

hdsr.mitpress.mit.edu | 66 min read | listed 1 day ago in Harvard Data Science Review
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Disclosure Protection in the Context of Statistical Agency Operations: Data Quality and Related Constraints · Harvard Data Science Review

hdsr.mitpress.mit.edu | 67 min read | listed 1 day ago in Harvard Data Science Review
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Robotic Manipulator Design

robohands.csail.mit.edu | 3 min read | listed 1 day ago in reddit/robotics/An Integrated Design Pipeline for Tactile Sensing Robotic Manipulators - MIT
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Case Study - Federated privacy preserving analytics for secure collaboration among Telco and partners to improve customer engagement

blog.openmined.org | 7 min read | listed 1 day ago in OpenMined Blog

MotivationWhile consumers expect better customer experience and personalization from businesses, they are increasingly sensitive to privacy and how businesses utilize and share customer data. As regulators step up consumer privacy requirements in response, leading businesses including Telcos that go beyond simple regulatory compliance can build customer trust and band appeal

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Artificial intelligence shows promise for interpreting dental X-rays

medicaldialogues.in | 1 min read | listed 1 day ago in Bing News

Previous studies have examined the use of artificial intelligence to detect caries, root fractures and apical lesions but there is limited research in the field of periodontology.But, according to...

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[2206.07578] E2V-SDE: From Asynchronous Events to Fast and Continuous Video Reconstruction via Neural Stochastic Differential Equations

arxiv.org | 1 min read | listed 1 day ago in reddit/MachineLearning/[D] How to copy text from more than 10 previously published papers and get accepted to CVPR 2022

Event cameras respond to brightness changes in the scene asynchronously and independently for every pixel. Due to the properties, these cameras have distinct features: high dynamic range (HDR), high temporal resolution, and low power consumption. However, the results of event cameras should be processed into an alternative representation for computer vision tasks. Also, they are usually noisy and cause poor performance in areas with few events. In recent years, numerous researchers have attempted to reconstruct videos from events. However, they do not provide good quality videos due to a lack of temporal information from irregular and discont...

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Detailed mapping of behavior reveals the formation of prelimbic neural ensembles across operant learning - PubMed

pubmed.ncbi.nlm.nih.gov | 1 min read | listed 1 day ago in reddit/MachineLearning/[D]Anyone use self-supervised learning at work? I'm surprised at how effective it has been for me.

The prelimbic cortex (PrL) is involved in the organization of operant behaviors, but the relationship between longitudinal PrL neural activity and operant learning and performance is unknown. Here, we developed deep behavior mapping (DBM) to identify behavioral microstates in video recordings. We co …

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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 2 days 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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[2206.08119] Learning to Infer Structures of Network Games

arxiv.org | 1 min read | listed 2 days ago in DataScienceWeekly.org — Data Science Weekly - Issue 448

Strategic interactions between a group of individuals or organisations can be modelled as games played on networks, where a player's payoff depends not only on their actions but also on those of their neighbours. Inferring the network structure from observed game outcomes (equilibrium actions) is an important problem with numerous potential applications in economics and social sciences. Existing methods mostly require the knowledge of the utility function associated with the game, which is often unrealistic to obtain in real-world scenarios. We adopt a transformer-like architecture which correctly accounts for the symmetries of the problem an...

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[2206.04737] Outsider Oversight: Designing a Third Party Audit Ecosystem for AI Governance

arxiv.org | 1 min read | listed 2 days ago in DataScienceWeekly.org — Data Science Weekly - Issue 448

Much attention has focused on algorithmic audits and impact assessments to hold developers and users of algorithmic systems accountable. But existing algorithmic accountability policy approaches have neglected the lessons from non-algorithmic domains: notably, the importance of interventions that allow for the effective participation of third parties. Our paper synthesizes lessons from other fields on how to craft effective systems of external oversight for algorithmic deployments. First, we discuss the challenges of third party oversight in the current AI landscape. Second, we survey audit systems across domains - e.g., financial, environmen...

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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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[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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