Search Results for author:
Found 16 papers, 2 papers with code
On Causal Inference for Data-free Structured Pruning
Neural networks (NNs) are making a large impact both on research and industry.
Deeplite Neutrino: An End-to-End Framework for Constrained Deep Learning Model Optimization
Designing deep learning-based solutions is becoming a race for training deeper models with a greater number of layers.
Reducing Overlearning through Disentangled Representations by Suppressing Unknown Tasks
Existing deep learning approaches for learning visual features tend to overlearn and extract more information than what is required for the task at hand.
"You might also like this model": Data Driven Approach for Recommending Deep Learning Models for Unknown Image Datasets
Further, we predict the performance accuracy of the recommended architecture on the given unknown dataset, without the need for training the model.
AuthorGAN: Improving GAN Reproducibility using a Modular GAN Framework
Five different GAN models are implemented as a part of this framework and the performance of the different GAN models are shown using the benchmark MNIST dataset.
Coverage Testing of Deep Learning Models using Dataset Characterization
Further, we conduct various experiments to demonstrate the effectiveness of systematic test case generation system for evaluating deep learning models.
A Visual Programming Paradigm for Abstract Deep Learning Model Development
Deep learning is one of the fastest growing technologies in computer science with a plethora of applications.
On Matching Faces with Alterations due to Plastic Surgery and Disguise
In this paper, a novel framework is proposed which transfers fundamental visual features learnt from a generic image dataset to supplement a supervised face recognition model.
Explaining Deep Learning Models using Causal Inference
Although deep learning models have been successfully applied to a variety of tasks, due to the millions of parameters, they are becoming increasingly opaque and complex.
Sanskrit Sandhi Splitting using seq2(seq)2
In Sanskrit, small words (morphemes) are combined to form compound words through a process known as Sandhi.
DeepTriage: Exploring the Effectiveness of Deep Learning for Bug Triaging
Using an attention mechanism enables the model to learn the context representation over a long word sequence, as in a bug report.
Sanskrit Sandhi Splitting using seq2(seq)^2
In Sanskrit, small words (morphemes) are combined to form compound words through a process known as Sandhi.
DLPaper2Code: Auto-generation of Code from Deep Learning Research Papers
To address these challenges, we propose a novel extensible approach, DLPaper2Code, to extract and understand deep learning design flow diagrams and tables available in a research paper and convert them to an abstract computational graph.
Hi, how can I help you?: Automating enterprise IT support help desks
Question answering is one of the primary challenges of natural language understanding.
"Let me convince you to buy my product ... ": A Case Study of an Automated Persuasive System for Fashion Products
Persuasivenes is a creative art aimed at making people believe in certain set of beliefs.
mAnI: Movie Amalgamation using Neural Imitation
Cross-modal data retrieval has been the basis of various creative tasks performed by Artificial Intelligence (AI).
