no code implementations • FNP (LREC) 2022 • Anik Saha, Jian Ni, Oktie Hassanzadeh, Alex Gittens, Kavitha Srinivas, Bulent Yener
Causal information extraction is an important task in natural language processing, particularly in finance domain.
no code implementations • 8 Mar 2024 • Sudipta Paul, Bulent Yener, Amanda W. Lund
As C2P-GCN integrates the structural data of an entire WSI into a single graph, it allows our model to work with significantly fewer training data compared to the latest models for colorectal cancer.
1 code implementation • 29 Aug 2023 • Anik Saha, Oktie Hassanzadeh, Alex Gittens, Jian Ni, Kavitha Srinivas, Bulent Yener
Neural ranking methods based on large transformer models have recently gained significant attention in the information retrieval community, and have been adopted by major commercial solutions.
1 code implementation • 7 Aug 2023 • Anik Saha, Oktie Hassanzadeh, Alex Gittens, Jian Ni, Kavitha Srinivas, Bulent Yener
Causal knowledge extraction is the task of extracting relevant causes and effects from text by detecting the causal relation.
no code implementations • 20 Apr 2023 • Anik Saha, Alex Gittens, Bulent Yener
This paper proposes a two-stage method to distill multiple word senses from a pre-trained language model (BERT) by using attention over the senses of a word in a context and transferring this sense information to fit multi-sense embeddings in a skip-gram-like framework.
no code implementations • 28 Feb 2022 • Sujoy Sikdar, Sikai Ruan, Qishen Han, Paween Pitimanaaree, Jeremy Blackthorne, Bulent Yener, Lirong Xia
We develop a game theoretic model of malware protection using the state-of-the-art sandbox method, to characterize and compute optimal defense strategies for anti-malware.
no code implementations • 25 Feb 2019 • Aritra Chowdhury, Malik Magdin-Ismail, Bulent Yener
We show that algorithm selection and hyper-parameter optimization methods can be used to quantify the error contribution and that random search is able to quantify the contribution more accurately than Bayesian optimization.
1 code implementation • 21 Feb 2019 • Aritra Chowdhury, Malik Magdon-Ismail, Bulent Yener
The agnostic and naive methodologies quantify the error contribution and propagation respectively from the computational steps, algorithms and hyperparameters in the image classification pipeline.
1 code implementation • NeurIPS 2018 • Haidar Khan, Bulent Yener
Our results show that the WD layer can improve neural network based time series classifiers both in accuracy and interpretability by learning directly from the input signal.