27 citations · 78 across the 12 of their papers we have counts for
16 papers
Beyond Black Box AI-Generated Plagiarism Detection: From Sentence to Document Level
Mujahid Ali Quidwai, Chunhui Li, Parijat Dube
The increasing reliance on large language models (LLMs) in academic writing has led to a rise in plagiarism. Existing AI-generated text classifiers have limited accuracy and often…
Accelerating Parallel Stochastic Gradient Descent via Non-blocking Mini-batches
Haoze He, Parijat Dube
SOTA decentralized SGD algorithms can overcome the bandwidth bottleneck at the parameter server by using communication collectives like Ring All-Reduce for synchronization. While t…
RCD-SGD: Resource-Constrained Distributed SGD in Heterogeneous Environment via Submodular Partitioning
Haoze He, Parijat Dube
The convergence of SGD based distributed training algorithms is tied to the data distribution across workers. Standard partitioning techniques try to achieve equal-sized partitions…
G2L: A Geometric Approach for Generating Pseudo-labels that Improve Transfer Learning
John R. Kender, Bishwaranjan Bhattacharjee, Parijat Dube +1
Transfer learning is a deep-learning technique that ameliorates the problem of learning when human-annotated labels are expensive and limited. In place of such labels, it uses inst…
Using sequential drift detection to test the API economy
Samuel Ackerman, Parijat Dube, Eitan Farchi
The API economy refers to the widespread integration of API (advanced programming interface) microservices, where software applications can communicate with each other, as a crucia…
Machine Learning Model Drift Detection Via Weak Data Slices
Samuel Ackerman, Parijat Dube, Eitan Farchi +2
Detecting drift in performance of Machine Learning (ML) models is an acknowledged challenge. For ML models to become an integral part of business applications it is essential to de…
Adversarial training in communication constrained federated learning
Devansh Shah, Parijat Dube, Supriyo Chakraborty +1
Federated learning enables model training over a distributed corpus of agent data. However, the trained model is vulnerable to adversarial examples, designed to elicit misclassific…
Detection of data drift and outliers affecting machine learning model performance over time
Samuel Ackerman, Eitan Farchi, Orna Raz +2
A trained ML model is deployed on another `test' dataset where target feature values (labels) are unknown. Drift is distribution change between the training and deployment data, wh…
Sequential Drift Detection in Deep Learning Classifiers
Samuel Ackerman, Parijat Dube, Eitan Farchi
We utilize neural network embeddings to detect data drift by formulating the drift detection within an appropriate sequential decision framework. This enables control of the false…
Improving the affordability of robustness training for DNNs
Sidharth Gupta, Parijat Dube, Ashish Verma
Projected Gradient Descent (PGD) based adversarial training has become one of the most prominent methods for building robust deep neural network models. However, the computational…
FfDL : A Flexible Multi-tenant Deep Learning Platform
K. R. Jayaram, Vinod Muthusamy, Parijat Dube +9
Deep learning (DL) is becoming increasingly popular in several application domains and has made several new application features involving computer vision, speech recognition and s…
P2L: Predicting Transfer Learning for Images and Semantic Relations
Bishwaranjan Bhattacharjee, John R. Kender, Matthew Hill +7
Transfer learning enhances learning across tasks, by leveraging previously learned representations -- if they are properly chosen. We describe an efficient method to accurately est…
Improving Transferability of Deep Neural Networks
Parijat Dube, Bishwaranjan Bhattacharjee, Elisabeth Petit-Bois +1
Learning from small amounts of labeled data is a challenge in the area of deep learning. This is currently addressed by Transfer Learning where one learns the small data set as a t…
Dependability in a Multi-tenant Multi-framework Deep Learning as-a-Service Platform
Scott Boag, Parijat Dube, Kaoutar El Maghraoui +7
Deep learning (DL), a form of machine learning, is becoming increasingly popular in several application domains. As a result, cloud-based Deep Learning as a Service (DLaaS) platfor…
Slow and Stale Gradients Can Win the Race: Error-Runtime Trade-offs in Distributed SGD
Sanghamitra Dutta, Gauri Joshi, Soumyadip Ghosh +2
Distributed Stochastic Gradient Descent (SGD) when run in a synchronous manner, suffers from delays in waiting for the slowest learners (stragglers). Asynchronous methods can allev…
IBM Deep Learning Service
Bishwaranjan Bhattacharjee, Scott Boag, Chandani Doshi +15
Deep learning driven by large neural network models is overtaking traditional machine learning methods for understanding unstructured and perceptual data domains such as speech, te…