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Found 23 papers, 8 papers with code
The neuroconnectionist research programme
Artificial Neural Networks (ANNs) inspired by biology are beginning to be widely used to model behavioral and neural data, an approach we call neuroconnectionism.
Explainable 3D Convolutional Neural Networks by Learning Temporal Transformations
Finally, we implicitly demonstrate that, in popular ConvNets, the 2DConv can be replaced with a 3TConv and that the weights can be transferred to yield pretrained 3TConvs.
GAIT-prop: A biologically plausible learning rule derived from backpropagation of error
An alternative called target propagation proposes to solve this implausibility by using a top-down model of neural activity to convert an error at the output of a neural network into layer-wise and plausible 'targets' for every unit.
Overcoming the Weight Transport Problem via Spike-Timing-Dependent Weight Inference
We propose a solution to the weight transport problem, which questions the biological plausibility of the backpropagation algorithm.
Background Hardly Matters: Understanding Personality Attribution in Deep Residual Networks
It is important that we understand the driving factors behind the predictions, in humans and in deep neural networks.
Temporal Factorization of 3D Convolutional Kernels
3D convolutional neural networks are difficult to train because they are parameter-expensive and data-hungry.
Bayesian nonparametric discontinuity design
Quasi-experimental research designs, such as regression discontinuity and interrupted time series, allow for causal inference in the absence of a randomized controlled trial, at the cost of additional assumptions.
Modulation of early visual processing alleviates capacity limits in solving multiple tasks
In daily life situations, we have to perform multiple tasks given a visual stimulus, which requires task-relevant information to be transmitted through our visual system.
Perturbative estimation of stochastic gradients
Furthermore, we introduce a family of variance reduction techniques that can be applied to other gradient estimators.
Wasserstein Variational Inference
This paper introduces Wasserstein variational inference, a new form of approximate Bayesian inference based on optimal transport theory.
Forward Amortized Inference for Likelihood-Free Variational Marginalization
In this paper, we introduce a new form of amortized variational inference by using the forward KL divergence in a joint-contrastive variational loss.
First Impressions: A Survey on Vision-Based Apparent Personality Trait Analysis
However, recently there has been an increasing interest from the computer vision community in analyzing personality from visual data.
Explaining First Impressions: Modeling, Recognizing, and Explaining Apparent Personality from Videos
Explainability and interpretability are two critical aspects of decision support systems.
Reconstructing perceived faces from brain activations with deep adversarial neural decoding
Here, we present a novel approach to solve the problem of reconstructing perceived stimuli from brain responses by combining probabilistic inference with deep learning.
The Kernel Mixture Network: A Nonparametric Method for Conditional Density Estimation of Continuous Random Variables
In the Bayesian filtering example, we show that the method can be used to filter complex nonlinear and non-Gaussian signals defined on manifolds.
End-to-end semantic face segmentation with conditional random fields as convolutional, recurrent and adversarial networks
Recent years have seen a sharp increase in the number of related yet distinct advances in semantic segmentation.
Deep Impression: Audiovisual Deep Residual Networks for Multimodal Apparent Personality Trait Recognition
Here, we develop an audiovisual deep residual network for multimodal apparent personality trait recognition.
Convolutional Sketch Inversion
In this paper, we use deep neural networks for inverting face sketches to synthesize photorealistic face images.
Modeling the dynamics of human brain activity with recurrent neural networks
Encoding models are used for predicting brain activity in response to sensory stimuli with the objective of elucidating how sensory information is represented in the brain.
Neurons and Cognition
Brains on Beats
We developed task-optimized deep neural networks (DNNs) that achieved state-of-the-art performance in different evaluation scenarios for automatic music tagging.
Neurons and Cognition
Dynamic Decomposition of Spatiotemporal Neural Signals
Neural signals are characterized by rich temporal and spatiotemporal dynamics that reflect the organization of cortical networks.
Regularizing Solutions to the MEG Inverse Problem Using Space-Time Separable Covariance Functions
We apply the method to both simulated and empirical data, and demonstrate the efficiency and generality of our Bayesian source reconstruction approach which subsumes various classical approaches in the literature.
Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Brain's Ventral Visual Pathway
Converging evidence suggests that the mammalian ventral visual pathway encodes increasingly complex stimulus features in downstream areas.
