Search Results for author:
Found 16 papers, 3 papers with code
Adversarial Black-Box Attacks On Text Classifiers Using Multi-Objective Genetic Optimization Guided By Deep Networks
On an average, we achieve an attack success rate of 65. 67% for SST and 36. 45% for IMDB across the three models showing an improvement of 49. 48% and 101% respectively.
Evaluation of Siamese Networks for Semantic Code Search
With the increase in the number of open repositories and discussion forums, the use of natural language for semantic code search has become increasingly common.
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.
Benchmarking Popular Classification Models' Robustness to Random and Targeted Corruptions
This emphasizes the need for a model agnostic test dataset, which consists of various corruptions that are natural to appear in the wild.
"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.
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.
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.
Adversarial Black-Box Attacks on Automatic Speech Recognition Systems using Multi-Objective Evolutionary Optimization
Fooling deep neural networks with adversarial input have exposed a significant vulnerability in the current state-of-the-art systems in multiple domains.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
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.
mAnI: Movie Amalgamation using Neural Imitation
Cross-modal data retrieval has been the basis of various creative tasks performed by Artificial Intelligence (AI).
Fault in your stars: An Analysis of Android App Reviews
Further, we developed three systems; two of which were based on traditional machine learning and one on deep learning to automatically identify reviews whose rating did not match with the opinion expressed in the review.
