Search Results for author:
Found 9 papers, 2 papers with code
Universum GANs: Improving GANs through contradictions
Limited availability of labeled-data makes any supervised learning problem challenging.
Pruning Algorithms to Accelerate Convolutional Neural Networks for Edge Applications: A Survey
With the general trend of increasing Convolutional Neural Network (CNN) model sizes, model compression and acceleration techniques have become critical for the deployment of these models on edge devices.
Auptimizer -- an Extensible, Open-Source Framework for Hyperparameter Tuning
Tuning machine learning models at scale, especially finding the right hyperparameter values, can be difficult and time-consuming.
On-Device Machine Learning: An Algorithms and Learning Theory Perspective
However, on-device learning is an expansive field with connections to a large number of related topics in AI and machine learning (including online learning, model adaptation, one/few-shot learning, etc.).
Improving Model Training by Periodic Sampling over Weight Distributions
In this paper, we explore techniques centered around periodic sampling of model weights that provide convergence improvements on gradient update methods (vanilla \acs{SGD}, Momentum, Adam) for a variety of vision problems (classification, detection, segmentation).
MULTI-MODAL EMOTION RECOGNITION ON IEMOCAP WITH NEURAL NETWORKS.
Emotion recognition has become an important field of re- search in Human Computer Interactions and there is a grow- ing need for automatic emotion recognition systems.
Make (Nearly) Every Neural Network Better: Generating Neural Network Ensembles by Weight Parameter Resampling
We perform a variety of analysis using the MNIST dataset and validate the approach with a number of DNN models using pre-trained models on the ImageNet dataset.
Towards Deeper Generative Architectures for GANs using Dense connections
In this paper, we present the result of adopting skip connections and dense layers, previously used in image classification tasks, in the Fisher GAN implementation.
Multi-Modal Emotion recognition on IEMOCAP Dataset using Deep Learning
Emotion recognition has become an important field of research in Human Computer Interactions as we improve upon the techniques for modelling the various aspects of behaviour.
