Search Results for author: Shirish Shevade

Found 21 papers, 9 papers with code

CodeQueries: A Dataset of Semantic Queries over Code

1 code implementation17 Sep 2022 Surya Prakash Sahu, Madhurima Mandal, Shikhar Bharadwaj, Aditya Kanade, Petros Maniatis, Shirish Shevade

Compared to the existing datasets, in CodeQueries, the queries are about code semantics, the context is file level and the answers are code spans.

Attribute Extractive Question-Answering +3

HyperTeNet: Hypergraph and Transformer-based Neural Network for Personalized List Continuation

2 code implementations4 Oct 2021 Vijaikumar M, Deepesh Hada, Shirish Shevade

The personalized list continuation (PLC) task is to curate the next items to user-generated lists (ordered sequence of items) in a personalized way.

Entity Embeddings

Stateful Detection of Model Extraction Attacks

1 code implementation12 Jul 2021 Soham Pal, Yash Gupta, Aditya Kanade, Shirish Shevade

Machine-Learning-as-a-Service providers expose machine learning (ML) models through application programming interfaces (APIs) to developers.

BIG-bench Machine Learning Model extraction

ACTIVETHIEF: Model Extraction Using Active Learning and Unannotated Public Data

1 code implementation7 Feb 2020 Soham Pal, Yash Gupta, Aditya Shukla, Aditya Kanade, Shirish Shevade, Vinod Ganapathy

We demonstrate that (1) it is possible to use ACTIVETHIEF to extract deep classifiers trained on a variety of datasets from image and text domains, while querying the model with as few as 10-30% of samples from public datasets, (2) the resulting model exhibits a higher transferability success rate of adversarial examples than prior work, and (3) the attack evades detection by the state-of-the-art model extraction detection method, PRADA.

Active Learning BIG-bench Machine Learning +1

Neural Attribution for Semantic Bug-Localization in Student Programs

1 code implementation NeurIPS 2019 Rahul Gupta, Aditya Kanade, Shirish Shevade

In this work, we present NeuralBugLocator, a deep learning based technique, that can localize the bugs in a faulty program with respect to a failing test, without even running the program.

Fault localization

Neural Cross-Domain Collaborative Filtering with Shared Entities

no code implementations19 Jul 2019 Vijaikumar M, Shirish Shevade, M. N. Murty

Cross-Domain Collaborative Filtering (CDCF) provides a way to alleviate data sparsity and cold-start problems present in recommendation systems by exploiting the knowledge from related domains.

Collaborative Filtering Recommendation Systems

Deep Learning for Bug-Localization in Student Programs

no code implementations28 May 2019 Rahul Gupta, Aditya Kanade, Shirish Shevade

To localize the bugs, we analyze the trained network using a state-of-the-art neural prediction attribution technique and see which lines of the programs make it predict the test outcomes.

Active Learning for Efficient Testing of Student Programs

no code implementations13 Apr 2018 Ishan Rastogi, Aditya Kanade, Shirish Shevade

In this work, we propose an automated method to identify semantic bugs in student programs, called ATAS, which builds upon the recent advances in both symbolic execution and active learning.

Active Learning

Latent Space Embedding for Retrieval in Question-Answer Archives

no code implementations EMNLP 2017 Deepak P, Dinesh Garg, Shirish Shevade

The idea is that such a space mirrors semantic similarity among questions as well as answers, thereby enabling high quality retrieval.

Question Answering Retrieval +4

DeepFix: Fixing Common C Language Errors by Deep Learning

1 code implementation4 Feb 2017 Rahul Gupta, Soham Pal, Aditya Kanade, Shirish Shevade

The problem of automatically fixing programming errors is a very active research topic in software engineering.

Program Repair

Learning Semantically Coherent and Reusable Kernels in Convolution Neural Nets for Sentence Classification

no code implementations1 Aug 2016 Madhusudan Lakshmana, Sundararajan Sellamanickam, Shirish Shevade, Keerthi Selvaraj

Motivated by this observation, we propose to learn kernels with semantic coherence using clustering scheme combined with Word2Vec representation and domain knowledge such as SentiWordNet.

Clustering General Classification +2

Topic Model Based Multi-Label Classification from the Crowd

no code implementations4 Apr 2016 Divya Padmanabhan, Satyanath Bhat, Shirish Shevade, Y. Narahari

Multi-label classification is a common supervised machine learning problem where each instance is associated with multiple classes.

Classification General Classification +1

A Robust UCB Scheme for Active Learning in Regression from Strategic Crowds

no code implementations25 Jan 2016 Divya Padmanabhan, Satyanath Bhat, Dinesh Garg, Shirish Shevade, Y. Narahari

We study the problem of training an accurate linear regression model by procuring labels from multiple noisy crowd annotators, under a budget constraint.

Active Learning regression +1

Gaussian Process Pseudo-Likelihood Models for Sequence Labeling

no code implementations25 Dec 2014 P. K. Srijith, P. Balamurugan, Shirish Shevade

We provide Gaussian process models based on pseudo-likelihood approximation to perform sequence labeling.

Gaussian Processes

Large Margin Semi-supervised Structured Output Learning

no code implementations9 Nov 2013 P. Balamurugan, Shirish Shevade, Sundararajan Sellamanickam

The optimization problem, which in general is not convex, contains the loss terms associated with the labelled and unlabelled examples along with the domain constraints.

Structured Prediction

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