17 citations · 24 across the 5 of their papers we have counts for
6 papers
Bayesian adaptive design for neural recruitment curves
Vishweshwar Tyagi, Saihari S. Dukkipati, Daniel Twili +4
Nonlinear response models remain ubiquitous across disciplines with recruitment curves describing muscle response to neural stimulation. Conventional acquisition methods typically…
The Sequential Nature of Science: Quantifying Learning from a Sequence of Studies
Jonas M. Mikhaeil, Donald P. Green, David Blei
Scientific progress is inherently sequential: collective knowledge is updated as new studies enter the literature. We propose the sequential meta-analysis research trace (SMART), w…
Quantifying How Much Has Been Learned from a Research Study
Jonas M. Mikhaeil, Donald P. Green
How much does a research study contribute to a scientific literature? We propose a learning metric to quantify how much a research community learns from a given study. To do so, we…
Sharp Bounds on the Variance of General Regression Adjustment in Randomized Experiments
Jonas M. Mikhaeil, Donald P. Green
A growing statistical literature focuses on causal inference in the context of experiments where the target of inference is the average treatment effect in a finite population and…
Tractable Dendritic RNNs for Reconstructing Nonlinear Dynamical Systems
Manuel Brenner, Florian Hess, Jonas M. Mikhaeil +4
In many scientific disciplines, we are interested in inferring the nonlinear dynamical system underlying a set of observed time series, a challenging task in the face of chaotic be…
On the difficulty of learning chaotic dynamics with RNNs
Jonas M. Mikhaeil, Zahra Monfared, Daniel Durstewitz
Recurrent neural networks (RNNs) are wide-spread machine learning tools for modeling sequential and time series data. They are notoriously hard to train because their loss gradient…