135 citations · 164 across the 11 of their papers we have counts for
11 papers
SoK: Data Minimization in Machine Learning
Robin Staab, Nikola Jovanović, Kimberly Mai +4
Data minimization (DM) describes the principle of collecting only the data strictly necessary for a given task. It is a foundational principle across major data protection regulati…
The Data Minimization Principle in Machine Learning
Prakhar Ganesh, Cuong Tran, Reza Shokri +1
The principle of data minimization aims to reduce the amount of data collected, processed or retained to minimize the potential for misuse, unauthorized access, or data breaches. R…
An Empirical Investigation into Benchmarking Model Multiplicity for Trustworthy Machine Learning: A Case Study on Image Classification
Prakhar Ganesh
Deep learning models have proven to be highly successful. Yet, their over-parameterization gives rise to model multiplicity, a phenomenon in which multiple models achieve similar p…
On The Impact of Machine Learning Randomness on Group Fairness
Prakhar Ganesh, Hongyan Chang, Martin Strobel +1
Statistical measures for group fairness in machine learning reflect the gap in performance of algorithms across different groups. These measures, however, exhibit a high variance b…
HiKonv: High Throughput Quantized Convolution With Novel Bit-wise Management and Computation
Xinheng Liu, Yao Chen, Prakhar Ganesh +3
Quantization for Convolutional Neural Network (CNN) has shown significant progress with the intention of reducing the cost of computation and storage with low-bitwidth data inputs.…
YOLO-ReT: Towards High Accuracy Real-time Object Detection on Edge GPUs
Prakhar Ganesh, Yao Chen, Yin Yang +2
Performance of object detection models has been growing rapidly on two major fronts, model accuracy and efficiency. However, in order to map deep neural network (DNN) based object…
Free Lunch for Co-Saliency Detection: Context Adjustment
Lingdong Kong, Prakhar Ganesh, Tan Wang +3
We unveil a long-standing problem in the prevailing co-saliency detection systems: there is indeed inconsistency between training and testing. Constructing a high-quality co-salien…
Compressing Large-Scale Transformer-Based Models: A Case Study on BERT
Prakhar Ganesh, Yao Chen, Xin Lou +6
Pre-trained Transformer-based models have achieved state-of-the-art performance for various Natural Language Processing (NLP) tasks. However, these models often have billions of pa…
Literature Survey on Finding Influential Communities in Large Scale Networks
Prakhar Ganesh, Saket Dingliwal, Rahul Agarwal
Community or modular structure is considered to be a significant property of large scale real-world graphs such as social or information networks. Detecting influential clusters or…
Restructuring Conversations using Discourse Relations for Zero-shot Abstractive Dialogue Summarization
Prakhar Ganesh, Saket Dingliwal
Dialogue summarization is a challenging problem due to the informal and unstructured nature of conversational data. Recent advances in abstractive summarization have been focused o…
VLSTM: Very Long Short-Term Memory Networks for High-Frequency Trading
Prakhar Ganesh, Puneet Rakheja
Financial trading is at the forefront of time-series analysis, and has grown hand-in-hand with it. The advent of electronic trading has allowed complex machine learning solutions t…