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Found 6 papers, 3 papers with code
Adversarial Distribution Balancing for Counterfactual Reasoning
The development of causal prediction models is challenged by the fact that the outcome is only observable for the applied (factual) intervention and not for its alternatives (the so-called counterfactuals); in medicine we only know patients' survival for the administered drug and not for other therapeutic options.
Probabilistic Neural Transfer Function Estimation with Bayesian System Identification
While deep neural network models have demonstrated excellent power on neural prediction, they usually do not provide the uncertainty of the resulting neural representations and derived statistics, such as the stimuli driving neurons optimally, from in silico experiments.
A flow-based latent state generative model of neural population responses to natural images
We present a joint deep neural system identification model for two major sources of neural variability: stimulus-driven and stimulus-conditioned fluctuations.
Factorized Neural Processes for Neural Processes: K-Shot Prediction of Neural Responses
We overcome this limitation by formulating the problem as $K$-shot prediction to directly infer a neuron's tuning function from a small set of stimulus-response pairs using a Neural Process.
Stimulus domain transfer in recurrent models for large scale cortical population prediction on video
However, in many cases this approach requires that the model is able to generalize to stimulus statistics that it was not trained on, such as band-limited noise and other parameterized stimuli.
Least Informative Dimensions
We present a novel non-parametric method for finding a subspace of stimulus features that contains all information about the response of a system.
