20 citations · 45 across the 7 of their papers we have counts for
7 papers
Predicting Visual Attention in Graphic Design Documents
Souradeep Chakraborty, Zijun Wei, Conor Kelton +4
We present a model for predicting visual attention during the free viewing of graphic design documents. While existing works on this topic have aimed at predicting static saliency…
Analyzing DCTCP and Cubic Buffer Sharing under Diverse Router Configurations
Santiago Vargas, Aruna Balasubramanian, Srikanth Sundaresan
In this work, we look at the impact of router configurations on DCTCP and Cubic traffic when both algorithms share router buffers in the data center. Modern data centers host traff…
IrEne: Interpretable Energy Prediction for Transformers
Qingqing Cao, Yash Kumar Lal, Harsh Trivedi +2
Existing software-based energy measurements of NLP models are not accurate because they do not consider the complex interactions between energy consumption and model execution. We…
Bew: Towards Answering Business-Entity-Related Web Questions
Qingqing Cao, Oriana Riva, Aruna Balasubramanian +1
We present BewQA, a system specifically designed to answer a class of questions that we call Bew questions. Bew questions are related to businesses/services such as restaurants, ho…
Towards Accurate and Reliable Energy Measurement of NLP Models
Qingqing Cao, Aruna Balasubramanian, Niranjan Balasubramanian
Accurate and reliable measurement of energy consumption is critical for making well-informed design choices when choosing and training large scale NLP models. In this work, we show…
DeFormer: Decomposing Pre-trained Transformers for Faster Question Answering
Qingqing Cao, Harsh Trivedi, Aruna Balasubramanian +1
Transformer-based QA models use input-wide self-attention -- i.e. across both the question and the input passage -- at all layers, causing them to be slow and memory-intensive. It…
MobiRNN: Efficient Recurrent Neural Network Execution on Mobile GPU
Qingqing Cao, Niranjan Balasubramanian, Aruna Balasubramanian
In this paper, we explore optimizations to run Recurrent Neural Network (RNN) models locally on mobile devices. RNN models are widely used for Natural Language Processing, Machine…