3 citations · 3 across the 4 of their papers we have counts for
4 papers
TabADM: Unsupervised Tabular Anomaly Detection with Diffusion Models
Guy Zamberg, Moshe Salhov, Ofir Lindenbaum +1
Tables are an abundant form of data with use cases across all scientific fields. Real-world datasets often contain anomalous samples that can negatively affect downstream analysis.…
Neuronal Cell Type Classification using Deep Learning
Ofek Ophir, Orit Shefi, Ofir Lindenbaum
The brain is likely the most complex organ, given the variety of functions it controls, the number of cells it comprises, and their corresponding diversity. Studying and identifyin…
Multi-modal Differentiable Unsupervised Feature Selection
Junchen Yang, Ofir Lindenbaum, Yuval Kluger +1
Multi-modal high throughput biological data presents a great scientific opportunity and a significant computational challenge. In multi-modal measurements, every sample is observed…
Revisiting the Noise Model of Stochastic Gradient Descent
Barak Battash, Ofir Lindenbaum
The stochastic gradient noise (SGN) is a significant factor in the success of stochastic gradient descent (SGD). Following the central limit theorem, SGN was initially modeled as G…