Search Results for author:
Found 6 papers, 4 papers with code
3D Face Tracking from 2D Video through Iterative Dense UV to Image Flow
Because such methods are expensive and due to the widespread availability of 2D videos, recent methods have focused on how to perform monocular 3D face tracking.
Learning to Follow Instructions in Text-Based Games
Text-based games present a unique class of sequential decision making problem in which agents interact with a partially observable, simulated environment via actions and observations conveyed through natural language.
Exploiting Explainable Metrics for Augmented SGD
In this paper, we address the following question: \textit{can we probe intermediate layers of a deep neural network to identify and quantify the learning quality of each layer?}
Towards Robust and Automatic Hyper-Parameter Tunning
In this work, we introduce a new class of HPO method and explore how the low-rank factorization of the convolutional weights of intermediate layers of a convolutional neural network can be used to define an analytical response surface for optimizing hyper-parameters, using only training data.
CONet: Channel Optimization for Convolutional Neural Networks
To solve this, we introduce an efficient dynamic scaling algorithm -- CONet -- that automatically optimizes channel sizes across network layers for a given CNN.
Response Modeling of Hyper-Parameters for Deep Convolution Neural Network
Hyper-parameter optimization (HPO) is critical in training high performing Deep Neural Networks (DNN).
