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Found 11 papers, 5 papers with code
Bayesian Sparsification of Deep C-valued Networks
With continual miniaturization ever more applications of deep learning can be found in embedded systems, where it is common to encounter data with natural representation in the complex domain.
Insights From the NeurIPS 2021 NetHack Challenge
In this report, we summarize the takeaways from the first NeurIPS 2021 NetHack Challenge.
Project Achoo: A Practical Model and Application for COVID-19 Detection from Recordings of Breath, Voice, and Cough
The COVID-19 pandemic created a significant interest and demand for infection detection and monitoring solutions.
Topology-based Clusterwise Regression for User Segmentation and Demand Forecasting
Topological Data Analysis (TDA) is a recent approach to analyze data sets from the perspective of their topological structure.
Bayesian Sparsification Methods for Deep Complex-valued Networks
With continual miniaturization ever more applications of deep learning can be found in embedded systems, where it is common to encounter data with natural complex domain representation.
CoLES: Contrastive Learning for Event Sequences with Self-Supervision
We address the problem of self-supervised learning on discrete event sequences generated by real-world users.
Demand forecasting techniques for build-to-order lean manufacturing supply chains
Due to their nature, they differ significantly from traditional supply chains.
Sparse Group Inductive Matrix Completion
We consider the problem of matrix completion with side information (\textit{inductive matrix completion}).
Conformal k-NN Anomaly Detector for Univariate Data Streams
Anomalies in time-series data give essential and often actionable information in many applications.
Steganographic Generative Adversarial Networks
Steganography is collection of methods to hide secret information ("payload") within non-secret information "container").
Conformalized Kernel Ridge Regression
General predictive models do not provide a measure of confidence in predictions without Bayesian assumptions.
