196 citations · 394 across the 27 of their papers we have counts for
5 papers · 1 filter
Uncovering Drift in Textual Data: An Unsupervised Method for Detecting and Mitigating Drift in Machine Learning Models
Saeed Khaki, Akhouri Abhinav Aditya, Zohar Karnin +3
Drift in machine learning refers to the phenomenon where the statistical properties of data or context, in which the model operates, change over time leading to a decrease in its p…
Representation Projection Invariance Mitigates Representation Collapse
Anastasia Razdaibiedina, Ashish Khetan, Zohar Karnin +3
Fine-tuning contextualized representations learned by pre-trained language models remains a prevalent practice in NLP. However, fine-tuning can lead to representation degradation (…
Pyramid-BERT: Reducing Complexity via Successive Core-set based Token Selection
Xin Huang, Ashish Khetan, Rene Bidart +1
Transformer-based language models such as BERT have achieved the state-of-the-art performance on various NLP tasks, but are computationally prohibitive. A recent line of works use…
Improving Early Sepsis Prediction with Multi Modal Learning
Fred Qin, Vivek Madan, Ujjwal Ratan +4
Sepsis is a life-threatening disease with high morbidity, mortality and healthcare costs. The early prediction and administration of antibiotics and intravenous fluids is considere…
schuBERT: Optimizing Elements of BERT
Ashish Khetan, Zohar Karnin
Transformers \citep{vaswani2017attention} have gradually become a key component for many state-of-the-art natural language representation models. A recent Transformer based model-…