112 citations · 172 across the 9 of their papers we have counts for
11 papers
GVdoc: Graph-based Visual Document Classification
Fnu Mohbat, Mohammed J. Zaki, Catherine Finegan-Dollak +1
The robustness of a model for real-world deployment is decided by how well it performs on unseen data and distinguishes between in-domain and out-of-domain samples. Visual document…
Position Masking for Improved Layout-Aware Document Understanding
Anik Saha, Catherine Finegan-Dollak, Ashish Verma
Natural language processing for document scans and PDFs has the potential to enormously improve the efficiency of business processes. Layout-aware word embeddings such as LayoutLM…
Separation of Powers in Federated Learning
Pau-Chen Cheng, Kevin Eykholt, Zhongshu Gu +4
Federated Learning (FL) enables collaborative training among mutually distrusting parties. Model updates, rather than training data, are concentrated and fused in a central aggrega…
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…
IBM Federated Learning: an Enterprise Framework White Paper V0.1
Heiko Ludwig, Nathalie Baracaldo, Gegi Thomas +21
Federated Learning (FL) is an approach to conduct machine learning without centralizing training data in a single place, for reasons of privacy, confidentiality or data volume. How…
Effective Elastic Scaling of Deep Learning Workloads
Vaibhav Saxena, K. R. Jayaram, Saurav Basu +2
The increased use of deep learning (DL) in academia, government and industry has, in turn, led to the popularity of on-premise and cloud-hosted deep learning platforms, whose goals…
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…
PoWER-BERT: Accelerating BERT Inference via Progressive Word-vector Elimination
Saurabh Goyal, Anamitra R. Choudhury, Saurabh M. Raje +3
We develop a novel method, called PoWER-BERT, for improving the inference time of the popular BERT model, while maintaining the accuracy. It works by: a) exploiting redundancy pert…
A Simple Dynamic Learning Rate Tuning Algorithm For Automated Training of DNNs
Koyel Mukherjee, Alind Khare, Ashish Verma
Training neural networks on image datasets generally require extensive experimentation to find the optimal learning rate regime. Especially, for the cases of adversarial training o…
Efficient Training of Convolutional Neural Nets on Large Distributed Systems
Sameer Kumar, Dheeraj Sreedhar, Vaibhav Saxena +2
Deep Neural Networks (DNNs) have achieved im- pressive accuracy in many application domains including im- age classification. Training of DNNs is an extremely compute- intensive pr…
Efficient Inferencing of Compressed Deep Neural Networks
Dharma Teja Vooturi, Saurabh Goyal, Anamitra R. Choudhury +2
Large number of weights in deep neural networks makes the models difficult to be deployed in low memory environments such as, mobile phones, IOT edge devices as well as "inferencin…