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Found 16 papers, 2 papers with code
Diffusion Posterior Sampling is Computationally Intractable
Diffusion models are a remarkably effective way of learning and sampling from a distribution $p(x)$.
Sample-Efficient Training for Diffusion
Score-based diffusion models have become the most popular approach to deep generative modeling of images, largely due to their empirical performance and reliability.
Finite-Sample Symmetric Mean Estimation with Fisher Information Rate
The mean of an unknown variance-$\sigma^2$ distribution $f$ can be estimated from $n$ samples with variance $\frac{\sigma^2}{n}$ and nearly corresponding subgaussian rate.
High-dimensional Location Estimation via Norm Concentration for Subgamma Vectors
In location estimation, we are given $n$ samples from a known distribution $f$ shifted by an unknown translation $\lambda$, and want to estimate $\lambda$ as precisely as possible.
Sharp Constants in Uniformity Testing via the Huber Statistic
It is known that the optimal sample complexity to distinguish the uniform distribution on $m$ elements from any $\epsilon$-far distribution with $1-\delta$ probability is $n = \Theta\left(\frac{\sqrt{m \log (1/\delta)}}{\epsilon^2} + \frac{\log (1/\delta)}{\epsilon^2}\right)$, which is achieved by the empirical TV tester.
Finite-Sample Maximum Likelihood Estimation of Location
We consider 1-dimensional location estimation, where we estimate a parameter $\lambda$ from $n$ samples $\lambda + \eta_i$, with each $\eta_i$ drawn i. i. d.
An Empirical Analysis of AI Contributions to Sustainable Cities (SDG11)
Artificial Intelligence (AI) presents opportunities to develop tools and techniques for addressing some of the major global challenges and deliver solutions with significant social and economic impacts.
Recent Trends in Artificial Intelligence-inspired Electronic Thermal Management
The rise of computation-based methods in thermal management has gained immense attention in recent years due to the ability of deep learning to solve complex 'physics' problems, which are otherwise difficult to be approached using conventional techniques.
Neural Network Based Epileptic EEG Detection and Classification
It is used before surgery for locating affected region.
Pathological Analysis of Blood Cells Using Deep Learning Techniques
Pathology deals with the practice of discovering the reasons for disease by analyzing the body samples.
EpilNet: A Novel Approach to IoT based Epileptic Seizure Prediction and Diagnosis System using Artificial Intelligence
The main characteristic of this disease is a frequent seizure, which is an electrical imbalance in the brain.
Automated Human Mind Reading Using EEG Signals for Seizure Detection
In the present paper, a model is proposed to give an accuracy of 98. 33% which can be used for development of automated systems.
Noisy Text Data: Achilles' Heel of popular transformer based NLP models
However, for most datasets that are used by practitioners to build industrial NLP applications, it is hard to guarantee the presence of any noise in the data.
Outlier-Robust Sparse Estimation via Non-Convex Optimization
We explore the connection between outlier-robust high-dimensional statistics and non-convex optimization in the presence of sparsity constraints, with a focus on the fundamental tasks of robust sparse mean estimation and robust sparse PCA.
Efficient Algorithms For Fair Clustering with a New Fairness Notion
We revisit the problem of fair clustering, first introduced by Chierichetti et al., that requires each protected attribute to have approximately equal representation in every cluster; i. e., a balance property.
AI-based Monitoring and Response System for Hospital Preparedness towards COVID-19 in Southeast Asia
This research paper proposes a COVID-19 monitoring and response system to identify the surge in the volume of patients at hospitals and shortage of critical equipment like ventilators in South-east Asian countries, to understand the burden on health facilities.
