Salih Acar resim sergisi

Taha Toros Arşivi, Dosya No: 6-Salih-Metin-İsmail-Kuzgun Acar. Not: Sergi, 3 - 16 Nisan 2001 tarihleri arasında düzenlenmiştir.Unutma İstanbul projesi İstanbul Kalkınma Ajansı'nın 2016 yılı "Yenilikçi ve Yaratıcı İstanbul Mali Destek Programı" kapsamında desteklenmiştir. Proje No: TR10/16/YNY/010

Explicit Tradeoffs between Adversarial and Natural Distributional Robustness

Several existing works study either adversarial or natural distributional robustness of deep neural networks separately. In practice, however, models need to enjoy both types of robustness to ensure reliability. In this work, we bridge this gap and show that in fact, explicit tradeoffs exist between adversarial and natural distributional robustness. We first consider a simple linear regression setting on Gaussian data with disjoint sets of core and spurious features. In this setting, through theoretical and empirical analysis, we show that (i) adversarial training with $\ell_1$ and $\ell_2$ norms increases the model reliance on spurious features; (ii) For $\ell_\infty$ adversarial training, spurious reliance only occurs when the scale of the spurious features is larger than that of the core features; (iii) adversarial training can have an unintended consequence in reducing distributional robustness, specifically when spurious correlations are changed in the new test domain. Next, we present extensive empirical evidence, using a test suite of twenty adversarially trained models evaluated on five benchmark datasets (ObjectNet, RIVAL10, Salient ImageNet-1M, ImageNet-9, Waterbirds), that adversarially trained classifiers rely on backgrounds more than their standardly trained counterparts, validating our theoretical results. We also show that spurious correlations in training data (when preserved in the test domain) can improve adversarial robustness, revealing that previous claims that adversarial vulnerability is rooted in spurious correlations are incomplete.Comment: Accepted to NeurIPS 202

Provide a model to identify the effect of the occurrence of business cycles on financial uncertainty in the stock market Developed countries

The main purpose of this study is to provide a model to identify the effect of the occurrence of business cycles on financial uncertainty in the stock market of developed countries. This research is an experimental research and panel data has been used to test the hypothesis. The statistical population of the study is developed countries. We grouped the countries based on high per capita income according to the UN Human Development Report. Data were also collected from the World Bank and 21 developed countries were selected as the sample size. According to the results of coefficients of independent variables (financial leverage, interest rate, inflation, time, recessionary business cycles and boom business cycles) which are all significant, ie their P-VALUE is less than 0.05, so we can conclude a significant effect. On financial uncertainty (variance of S&P index growth rate). Also, considering that the coefficient of determination of the regression model (R2) is equal to 0.984 and is close to the number one, that is, the Fisher test (F) is significant (its probability is less than 0.05), so the regression model is justifiable and acceptable. Hypothesis H0 is therefore rejected and Hypothesis H1 is accepted with 95% probability, or in other words, business cycles have a significant effect on financial uncertainty in the stock market of developed countries

Formulation of an Electrostatic Field with a Charge Density in the Presence of a Minimal Length Based on the Kempf Algebra

In a series of papers, Kempf and co-workers (J. Phys. A: Math. Gen. {\bf 30}, 2093, (1997); Phys. Rev. D {\bf52}, 1108, (1995); Phys. Rev. D {\bf55}, 7909, (1997)) introduced a D-dimensional $(\beta,\beta')$-two-parameter deformed Heisenberg algebra which leads to a nonzero minimal observable length. In this work, the Lagrangian formulation of an electrostatic field in three spatial dimensions described by Kempf algebra is studied in the case where $\beta'=2\beta$ up to first order over deformation parameter $\beta$. It is shown that there is a similarity between electrostatics in the presence of a minimal length (modified electrostatics) and higher derivative Podolsky's electrostatics. The important property of this modified electrostatics is that the classical self-energy of a point charge becomes a finite value. Two different upper bounds on the isotropic minimal length of this modified electrostatics are estimated. The first upper bound will be found by treating the modified electrostatics as a classical electromagnetic system, while the second one will be estimated by considering the modified electrostatics as a quantum field theoretic model. It should be noted that the quantum upper bound on the isotropic minimal length in this paper is near to the electroweak length scale $(\ell_{electroweak}\sim 10^{-18}\, m)$.Comment: 11 pages, no figur

Spuriosity Rankings: Sorting Data to Measure and Mitigate Biases

We present a simple but effective method to measure and mitigate model biases caused by reliance on spurious cues. Instead of requiring costly changes to one's data or model training, our method better utilizes the data one already has by sorting them. Specifically, we rank images within their classes based on spuriosity (the degree to which common spurious cues are present), proxied via deep neural features of an interpretable network. With spuriosity rankings, it is easy to identify minority subpopulations (i.e. low spuriosity images) and assess model bias as the gap in accuracy between high and low spuriosity images. One can even efficiently remove a model's bias at little cost to accuracy by finetuning its classification head on low spuriosity images, resulting in fairer treatment of samples regardless of spuriosity. We demonstrate our method on ImageNet, annotating $5000$ class-feature dependencies ($630$ of which we find to be spurious) and generating a dataset of $325k$ soft segmentations for these features along the way. Having computed spuriosity rankings via the identified spurious neural features, we assess biases for $89$ diverse models and find that class-wise biases are highly correlated across models. Our results suggest that model bias due to spurious feature reliance is influenced far more by what the model is trained on than how it is trained.Comment: Accepted to NeurIPS '23 (Spotlight). Camera ready versio