Search Results for author:
Found 10 papers, 2 papers with code
MMASD: A Multimodal Dataset for Autism Intervention Analysis
This work presents a novel privacy-preserving open-source dataset, MMASD as a MultiModal ASD benchmark dataset, collected from play therapy interventions of children with Autism.
A Probabilistic Representation of Deep Learning for Improving The Information Theoretic Interpretability
Based on the probabilistic explanations for MLPs, we improve the information-theoretic interpretability of MLPs in three aspects: (i) the random variable of f is discrete and the corresponding entropy is finite; (ii) the information bottleneck theory cannot correctly explain the information flow in MLPs if we take into account the back-propagation; and (iii) we propose novel information-theoretic explanations for the generalization of MLPs.
PAC-Bayesian Generalization Bounds for MultiLayer Perceptrons
We study PAC-Bayesian generalization bounds for Multilayer Perceptrons (MLPs) with the cross entropy loss.
Audio-video Emotion Recognition in the Wild using Deep Hybrid Networks
This paper presents an audiovisual-based emotion recognition hybrid network.
Probabilistic modeling the hidden layers of deep neural networks
Based on the probabilistic representation, we demonstrate that the entire architecture of DNNs can be explained as a Bayesian hierarchical model.
Graph Neural Networks for Image Understanding Based on Multiple Cues: Group Emotion Recognition and Event Recognition as Use Cases
A graph neural network (GNN) for image understanding based on multiple cues is proposed in this paper.
A Probabilistic Representation of Deep Learning
In this work, we introduce a novel probabilistic representation of deep learning, which provides an explicit explanation for the Deep Neural Networks (DNNs) in three aspects: (i) neurons define the energy of a Gibbs distribution; (ii) the hidden layers of DNNs formulate Gibbs distributions; and (iii) the whole architecture of DNNs can be interpreted as a Bayesian neural network.
Smile detection in the wild based on transfer learning
Compared to the size of databases for face recognition, far less labeled data is available for training smile detection systems.
Exploiting Restricted Boltzmann Machines and Deep Belief Networks in Compressed Sensing
This paper proposes a CS scheme that exploits the representational power of restricted Boltzmann machines and deep learning architectures to model the prior distribution of the sparsity pattern of signals belonging to the same class.
