Search Results for author:
Found 22 papers, 6 papers with code
Efficient Deep Reinforcement Learning with Predictive Processing Proximal Policy Optimization
Predictive processing is a popular theoretical framework which maintains that the human brain is actively seeking to minimize surprise.
P4O: Efficient Deep Reinforcement Learning with Predictive Processing Proximal Policy Optimization
Predictive processing is a popular theoretical framework which maintains that the human brain is actively seeking to minimize surprise.
The 3TConv: An Intrinsic Approach to Explainable 3D CNNs
In a 3TConv the 3D convolutional filter is obtained by learning a 2D filter and a set of temporal transformation parameters, resulting in a sparse filter requiring less parameters.
Hyperrealistic neural decoding: Reconstruction of face stimuli from fMRI measurements via the GAN latent space
We introduce a new framework for hyperrealistic reconstruction of perceived naturalistic stimuli from brain recordings.
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.
The Indian Chefs Process
This paper introduces the Indian Chefs Process (ICP), a Bayesian nonparametric prior on the joint space of infinite directed acyclic graphs (DAGs) and orders that generalizes Indian Buffet Processes.
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.
k-GANs: Ensemble of Generative Models with Semi-Discrete Optimal Transport
A possible way of dealing with this problem is to use an ensemble of GANs, where (ideally) each network models a single mode.
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.
Wasserstein Variational Inference
This paper introduces Wasserstein variational inference, a new form of approximate Bayesian inference based on optimal transport theory.
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.
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.
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.
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.
Estimating Nonlinear Dynamics with the ConvNet Smoother
Estimating the state of a dynamical system from a series of noise-corrupted observations is fundamental in many areas of science and engineering.
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
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.
