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Expo login req'd Sponsor Hall login req'd. FAQ Committee. Abstract: Learning-based methods, and in particular deep neural networks, have emerged as highly successful and universal tools for image and signal recovery and restoration.

Lenka Zdeborova's articles on arXiv

They achieve state-of-the-art results on tasks ranging from image denoising, image compression, and image reconstruction from few and noisy measurements. They are starting to be used in important imaging technologies, for example in GEs newest computational tomography scanners and in the newest generation of the iPhone.

The field has a range of theoretical and practical questions that remain unanswered. In particular, learning and neural network-based approaches often lack the guarantees of traditional physics-based methods. Further, while superior on average, learning-based methods can make drastic reconstruction errors, such as hallucinating a tumor in an MRI reconstruction or turning a pixelated picture of Obama into a white male. This virtual workshop aims at bringing together theoreticians and practitioners in order to chart out recent advances and discuss new directions in deep neural network-based approaches for solving inverse problems in the imaging sciences and beyond.

NeurIPS, with its visibility and attendance by experts in machine learning, offers the ideal frame for this exchange of ideas. We will use this virtual format to make this topic accessible to a broader audience than the in-person meeting is able to as described below.

Chat To ask questions please use rocketchat, available only upon registration and login. Schedule T - TWe study a lattice model of attractive colloids.

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It is exactly solvable on sparse random graphs. As the pressure and temperature are varied, it reproduces many characteristic phenomena of liquids, glasses, and colloidal systems such as ideal gel formation, liquid-glass phase coexistence, jamming, or the reentrance of the glass transition. COVID has impacted many institutions and organizations around the world, disrupting the progress of research. Through this difficult time APS and the Physical Review editorial office are fully equipped and actively working to support researchers by continuing to carry out all editorial and peer-review functions and publish research in the journals as well as minimizing disruption to journal access.

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To address this, we have been improving access via several different mechanisms. Learn about our response to COVIDincluding freely available research and expanded remote access support. Abstract We study a lattice model of attractive colloids. Issue Vol. Authorization Required. Log In. The inset shows the same plot on a logarithmic scale. Sign up to receive regular email alerts from Physical Review Letters.

lenka zdeborova cv

Journal: Phys. A Phys. B Phys. C Phys. D Phys. E Phys. Research Phys. Beams Phys. ST Accel. Applied Phys. Fluids Phys.

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Materials Phys. ST Phys. Physics Phys. Series I Physics Physique Fizika.Hello, I am a currently Ph. In my spare time, I practise skydiving, windsurfing, kitesurfing, surfing, hiking, biking and a lot of sport in general! Hello and welcome! I am currently in third year Ph. I am working on the statistical physics of disordered systems, information theory, statistical inference and message-passing algorithms and their applications to theoretical machine learning neural networks, phase retrieval, compressed sensing, matrix factorization, Download my resume View my research.

Latest News. December16 - Ph. D Defense. December- NeurIPS About Me Hello, I am a currently Ph. Research - Recent publications. Statistical physics of disordered systems, Constraints statisfaction problems, Machine Learning, Deep Learning. Statistical physics, Numerical physics, Bayesian inference, Statistical field theory, Condensed matter, Non-linear physics, Stochastic processes, Non-equlibirum physics.

Lenka Zdeborova: Algorithms in high-dimensional non-convex landscapes

Advanced quantum mechanics, Statistical physics, Computer science advanced programming and algorithmsProbability, Statistics, Massive data processing, Control dynamic models,Fluid mechanics, Continuum mechanics, Particle physics, Soft mmatter, Complex systems, Quantum optics.

Mathematics, Physics, Chemistry, Litterature, Foreign languages. Statistical physics, disordered systems, message passing algorithms. Python, Matlab, Julia. Machine learning, Deep learning. Pytorch, Keras, Scikit-Learn.

Bash, Git, Github, Latex. Get in Touch.See author identifier help for more information about arXiv author identifiers, please report any problems. We gratefully acknowledge support from the Simons Foundation and member institutions.

Lenka Zdeborova's articles on arXiv [1] arXiv Comments: The accompanying code for this paper is available at this https URL. Subjects: Machine Learning stat. Subjects: Information Theory cs. Journal-ref: Physical Review X, Vol.

Subjects: Machine Learning cs. LG ; Statistics Theory math. Subjects: Disordered Systems and Neural Networks cond-mat. DM ; Probability math. Comments: 12 pages main text and references26 pages of supplementary material. Subjects: Statistics Theory math. IT ; Machine Learning cs.

The Partition Function, Sampling and Equilibration in Physics

LG ; Probability math. Subjects: Populations and Evolution q-bio. PE ; Statistical Mechanics cond-mat. AI ; Machine Learning cs. Comments: v2: ICML camera-ready. ST ; Machine Learning cs. PR ; Machine Learning stat. LG ; Machine Learning stat.

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Authors: William H. Subjects: Social and Information Networks cs. LG ; Signal Processing eess.The affinity between statistical physics and machine learning has long history, this is reflected even in the machine learning terminology that is in part adopted from physics. Current theoretical challenges and open questions about deep learning and statistical learning call for unified account of the following three ingredients: a the dynamics of the learning algorithm, b the architecture of the neural networks, and c the structure of the data.

Most existing theories are not taking in account all of those three aspects in a satisfactory manner. In this talk I will describe some of the results stemming from statistical physics applied to machine learning and how it does include the three ingredients, although in a very simplified manner.

lenka zdeborova cv

Then I will focus on the current results improving our modelling in each of the three aspects covering recent articles []. Preprint arXiv November 15, am - pm. Abstract: The affinity between statistical physics and machine learning has long history, this is reflected even in the machine learning terminology that is in part adopted from physics. Details Date: November 15, Time: am - pm. Venue E United States.For instance, our giveaway posts tend to generate the most comments while user-generated landscape photos usually receive the most engagement.

To drive more engagement, we can continue to post landscape photos from our community. For example, if most of your followers are females aged 35-44 who like to design, can you create content that resonates with them more than the rest of your followers. You can also use your Instagram Stories data to improve your stories. Instagram Insights from the Instagram app provides data such as impressions, exits, and replies, which you can use to tell better stories.

For example, our story on creating a custom graphic within Canva received more impressions than other recent stories, and our Instagram Stories takeover by HubSpot on productivity tips received more replies than other recent stories.

We can also dive into a particular story: Most people exited our video marketing stats story at the first photo so perhaps we could work on the headline for the next story. What does your Instagram Stories data tell you. Is there a type of stories that tends to do better than the rest. Is there a common point when most of your followers exit your stories.

lenka zdeborova cv

Since Instagram allows only one link (in the bio), marketers would usually have a call-to-action (CTA) in their Instagram posts to direct followers to the link in their bio. You could change your CTA on a weekly basis and see if the number of website clicks increases. As a refresher, website clicks are the number of taps on the link in your bio. Alternatively, you could use Bitly or similar link shorteners with tracking capabilities for the link in your bio to track the number of clicks.

With the wealth of data available to us marketers through the numerous Instagram analytics tools, we can be more data-driven with our Instagram marketing than ever before. Combined with our understanding of our followers, these Instagram insights can enable us to create more valuable and engaging content for our audience.

How have you been measuring your Instagram metrics. How have you been using your Instagram insights to improve your Instagram marketing. I swim, cycle, and run a lot. It is incredibly exhaustive. Analytics is always a hard nut to crack, and you never know every details.

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Have you ever tried Metricool. Metricool is a social media analytics tool. Let me know if we can help tracking your Instagram performance or building your social media insights reports. Thank you for including Union Metrics free Instagram account checkup in this especially sharing your results. I just signed up for a few more analytics platforms based on your suggestions. There were times when I chanced upon an old Instagram post and the link in bio was no longer the one mentioned in that post (as it had been updated).

So this is great. Thanks for sharing this tool. Curious if a spreadsheet might work for this.This is the date and time in which the correlation was updated with microsecond precision.

See Correlation Results Object. Each entry includes the column number in original source, the name of the field, the type of the field, and the summary. Name of the correlation. A correlation result which is a dictionary between field ids and the result. The type of result object varies based on the name of the correlation.

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See Pearson's correlation coefficients for more information. Thus, the number of parameters grows with the amount of training data) measure of statistical dependence between two variables.

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See Spearman's correlation coefficients for more information. A measure of association between two nominal variables. Its value ranges between 0 (no association between the variables) and 1 (complete association), and can reach 1 only when the two variables are equal to each other.

It is based on Pearson's chi-squared statistic. Its value ranges ranges between 0 (no association between the variables) and 1 (complete association).

See Tschuprow's T for more information. In other words, the table summarizes the distribution of values in the sample. Its value ranges ranges between 0 and 1. A rule of thumb is: 0. See eta-squared for more information. The value of the F statistic, which is used to assess whether the expected values of a quantitative variable within several pre-defined groups differ from each other. It is the ratio of the variance calculated among the means to the variance within the samples.

This parameter specifies the number of samples to be used during the normality test. If not given, defaults to 1024.

Example: "MyADSeed" category optional The category that best describes the test.

Ad hoc analysis