Search Results for author:
Found 9 papers, 5 papers with code
Evaluating the Effectiveness of Efficient Neural Architecture Search for Sentence-Pair Tasks
Neural Architecture Search (NAS) methods, which automatically learn entire neural model or individual neural cell architectures, have recently achieved competitive or state-of-the-art (SOTA) performance on variety of natural language processing and computer vision tasks, including language modeling, natural language inference, and image classification.
Out-of-the-box channel pruned networks
We then demonstrate that if we found the profiles using a mid-sized dataset such as Cifar10/100, we are able to transfer them to even a large dataset such as Imagenet.
$d$-SNE: Domain Adaptation using Stochastic Neighborhood Embedding
Deep neural networks often require copious amount of labeled-data to train their scads of parameters.
A Strategy for an Uncompromising Incremental Learner
Using an implementation based on deep neural networks, we demonstrate that phantom sampling dramatically avoids catastrophic forgetting.
Classification of Diabetic Retinopathy Images Using Multi-Class Multiple-Instance Learning Based on Color Correlogram Features
All people with diabetes have the risk of developing diabetic retinopathy (DR), a vision-threatening complication.
Neural Dataset Generality
It is curious that while the filters learned by these CNNs are related to the atomic structures of the images from which they are learnt, all datasets learn similar looking low-level filters.
Diving deeper into mentee networks
We studied various characteristics of such networks and found some interesting behaviors.
Simpler non-parametric methods provide as good or better results to multiple-instance learning.
Multiple-instance learning (MIL) is a unique learning problem in which training data labels are available only for collections of objects (called bags) instead of individual objects (called instances).
Simpler Non-Parametric Methods Provide as Good or Better Results to Multiple-Instance Learning
Multiple-instance learning (MIL) is a unique learning problem in which training data labels are available only for collections of objects (called bags) instead of individual objects (called instances).
