no code implementations • 30 Jan 2024 • Shengzhe Xu, Christo Kurisummoottil Thomas, Omar Hashash, Nikhil Muralidhar, Walid Saad, Naren Ramakrishnan
Diverging from NLP-based foundation models, the proposed framework promotes the design of large multi-modal models (LMMs) fostered by three key capabilities: 1) processing of multi-modal sensing data, 2) grounding of physical symbol representations in real-world wireless systems using causal reasoning and retrieval-augmented generation (RAG), and 3) enabling instructibility from the wireless environment feedback to facilitate dynamic network adaptation thanks to logical and mathematical reasoning facilitated by neuro-symbolic AI.
1 code implementation • 14 May 2023 • Mandar Sharma, Nikhil Muralidhar, Naren Ramakrishnan
The field of Math-NLP has witnessed significant growth in recent years, motivated by the desire to expand LLM performance to the learning of non-linguistic notions (numerals, and subsequently, arithmetic reasoning).
1 code implementation • 3 Nov 2022 • Mandar Sharma, Nikhil Muralidhar, Naren Ramakrishnan
Through their transfer learning abilities, highly-parameterized large pre-trained language models have dominated the NLP landscape for a multitude of downstream language tasks.
1 code implementation • 1 Oct 2022 • Gopikrishna Rathinavel, Nikhil Muralidhar, Timothy O'Shea, Naren Ramakrishnan
Specifically, CAAD employs contrastive learning in an adversarial setup to learn effective representations of normal and anomalous behavior in wireless networks.
1 code implementation • 4 Aug 2022 • Subhodip Biswas, Fanglan Chen, Zhiqian Chen, Chang-Tien Lu, Naren Ramakrishnan
However, the search operators employed by these population-based methods are mostly designed for real-parameter continuous optimization problems.
no code implementations • 31 Jul 2022 • Debanjan Datta, Sathappan Muthiah, John Simeone, Amelia Meadows, Naren Ramakrishnan
The task of finding such fraudulent activities using trade data, in the absence of ground truth, can be modelled as an unsupervised anomaly detection problem.
no code implementations • 25 Jul 2022 • Mandar Sharma, Ajay Gogineni, Naren Ramakrishnan
The neural boom that has sparked natural language processing (NLP) research through the last decade has similarly led to significant innovations in data-to-text generation (DTG).
1 code implementation • 8 Jul 2022 • Raquib Bin Yousuf, Subhodip Biswas, Kulendra Kumar Kaushal, James Dunham, Rebecca Gelles, Sathappan Muthiah, Nathan Self, Patrick Butler, Naren Ramakrishnan
We demonstrate how EneRex is able to extract key insights and trends from a large-scale dataset in the domain of computer science.
no code implementations • 29 Jun 2022 • Debanjan Datta, Feng Chen, Naren Ramakrishnan
We present an approach -- Context preserving Algorithmic Recourse for Anomalies in Tabular data (CARAT), that is effective, scalable, and agnostic to the underlying anomaly detection model.
1 code implementation • 8 Jun 2022 • Subhodip Biswas, Fanglan Chen, Zhiqian Chen, Chang-Tien Lu, Naren Ramakrishnan
Motivated by these recent developments, we develop a set of similar sampling techniques for designing school boundaries based on the flip proposal.
no code implementations • 6 Apr 2022 • Sneha Mehta, Huzefa Rangwala, Naren Ramakrishnan
We show how such context simplification can improve the performance of MRC-based event extraction by more than 5% for actor extraction and more than 10% for target extraction.
no code implementations • 4 Mar 2022 • Nikhil Muralidhar, Abdullah Zubair, Nathanael Weidler, Ryan Gerdes, Naren Ramakrishnan
The availability of wide-ranging third-party intellectual property (3PIP) cores enables integrated circuit (IC) designers to focus on designing high-level features in ASICs/SoCs.
1 code implementation • 21 Feb 2022 • Alexander Rodríguez, Jiaming Cui, Naren Ramakrishnan, Bijaya Adhikari, B. Aditya Prakash
We introduce EINNs, a framework crafted for epidemic forecasting that builds upon the theoretical grounds provided by mechanistic models as well as the data-driven expressibility afforded by AI models, and their capabilities to ingest heterogeneous information.
1 code implementation • 9 Nov 2021 • Padmaksha Roy, Shailik Sarkar, Subhodip Biswas, Fanglan Chen, Zhiqian Chen, Naren Ramakrishnan, Chang-Tien Lu
The Gaussian Mixture Model layer is implemented to consider the multimodal nature of the real-time data while learning from multiple related time series.
1 code implementation • 11 Oct 2021 • Mandar Sharma, John S. Brownstein, Naren Ramakrishnan
We present TCube (Time-series-to-text), a domain-agnostic neural framework for time-series narration, that couples the representation of essential time-series elements in the form of a dense knowledge graph and the translation of said knowledge graph into rich and fluent narratives through the transfer-learning capabilities of PLMs (Pre-trained Language Models).
no code implementations • 30 Jun 2021 • Nikhil Muralidhar, Sathappah Muthiah, Patrick Butler, Manish Jain, Yu Yu, Katy Burne, Weipeng Li, David Jones, Prakash Arunachalam, Hays 'Skip' McCormick, Naren Ramakrishnan
We describe lessons learned from developing and deploying machine learning models at scale across the enterprise in a range of financial analytics applications.
no code implementations • 2 Apr 2021 • Debanjan Datta, Sathappan Muthiah, Naren Ramakrishnan
Among other challenges annotations are unavailable for our large-scale trade data with heterogeneous features (categorical and continuous), that can assist in building automated systems to detect fraudulent transactions.
no code implementations • 24 Dec 2020 • Alexander Rodríguez, Bijaya Adhikari, Naren Ramakrishnan, B. Aditya Prakash
Forecasting influenza like illnesses (ILI) has rapidly progressed in recent years from an art to a science with a plethora of data-driven methods.
1 code implementation • 11 Dec 2020 • Subhodip Biswas, Adam D Cobb, Andreea Sistrunk, Naren Ramakrishnan, Brian Jalaian
In this paper, we propose a surrogate-assisted evolutionary algorithm (EA) for hyperparameter optimization of machine learning (ML) models.
1 code implementation • 27 Sep 2020 • Shengzhe Xu, Manish Marwah, Martin Arlitt, Naren Ramakrishnan
We evaluate the performance of STAN in terms of the quality of data generated, by training it on both a simulated dataset and a real network traffic data set.
1 code implementation • 23 Sep 2020 • Alexander Rodríguez, Nikhil Muralidhar, Bijaya Adhikari, Anika Tabassum, Naren Ramakrishnan, B. Aditya Prakash
Our experiments demonstrate that our approach is successful in adapting a historical forecasting model to the current pandemic.
no code implementations • 6 Sep 2020 • Arjun Choudhry, Mandar Sharma, Pramod Chundury, Thomas Kapler, Derek W. S. Gray, Naren Ramakrishnan, Niklas Elmqvist
In this paper, we propose the use of textual narratives as a data-driven storytelling method to augment causality visualization.
1 code implementation • 25 May 2020 • Bing He, Caleb Ziems, Sandeep Soni, Naren Ramakrishnan, Diyi Yang, Srijan Kumar
The spread of COVID-19 has sparked racism and hate on social media targeted towards Asian communities.
1 code implementation • 26 Nov 2019 • Sneha Mehta, Huzefa Rangwala, Naren Ramakrishnan
Effective representation learning from text has been an active area of research in the fields of NLP and text mining.
1 code implementation • 6 Nov 2019 • Nikhil Muralidhar, Jie Bu, Ze Cao, Long He, Naren Ramakrishnan, Danesh Tafti, Anuj Karpatne
In such situations, it is often useful to rely on machine learning methods to fill in the gap by learning a model of the complex physical process directly from simulation data.
1 code implementation • NAACL 2019 • Xuchao Zhang, Fanglan Chen, Chang-Tien Lu, Naren Ramakrishnan
The uncertainty measurement of classifiers' predictions is especially important in applications such as medical diagnoses that need to ensure limited human resources can focus on the most uncertain predictions returned by machine learning models.
1 code implementation • 22 May 2019 • Taoran Ji, Zhiqian Chen, Nathan Self, Kaiqun Fu, Chang-Tien Lu, Naren Ramakrishnan
For the problem of patent citations, we observe that forecasting a patent's chain of citations benefits from not only the patent's history itself but also from the historical citations of assignees and inventors associated with that patent.
5 code implementations • 5 Dec 2018 • Tian Shi, Yaser Keneshloo, Naren Ramakrishnan, Chandan K. Reddy
As part of this survey, we also develop an open source library, namely, Neural Abstractive Text Summarizer (NATS) toolkit, for the abstractive text summarization.
1 code implementation • 15 Oct 2018 • Yaser Keneshloo, Naren Ramakrishnan, Chandan K. Reddy
Deep neural networks are data hungry models and thus face difficulties when attempting to train on small text datasets.
3 code implementations • 24 May 2018 • Yaser Keneshloo, Tian Shi, Naren Ramakrishnan, Chandan K. Reddy
In this survey, we consider seq2seq problems from the RL point of view and provide a formulation combining the power of RL methods in decision-making with sequence-to-sequence models that enable remembering long-term memories.
1 code implementation • 22 Feb 2017 • Saurav Ghosh, Prithwish Chakraborty, Bryan L. Lewis, Maimuna S. Majumder, Emily Cohn, John S. Brownstein, Madhav V. Marathe, Naren Ramakrishnan
Specifically, we focus on deriving epidemiological characteristics of an emerging disease and the affected population from reports of illness.
no code implementations • 22 Feb 2017 • Bijaya Adhikari, Yao Zhang, Naren Ramakrishnan, B. Aditya Prakash
Motivated by the recent successes of embeddings in natural language processing, researchers have tried to find network embeddings in order to exploit machine learning algorithms for mining tasks like node classification and edge prediction.
Ranked #4 on Malware Detection on Android Malware Dataset
no code implementations • 22 Feb 2017 • Mohammad Raihanul Islam, B. Aditya Prakash, Naren Ramakrishnan
Recent successes in word embedding and document embedding have motivated researchers to explore similar representations for networks and to use such representations for tasks such as edge prediction, node label prediction, and community detection.
no code implementations • 1 Jun 2016 • Saurav Ghosh, Prithwish Chakraborty, Elaine O. Nsoesie, Emily Cohn, Sumiko R. Mekaru, John S. Brownstein, Naren Ramakrishnan
In this study, we quantify the extent to which media interest during infectious disease outbreaks is indicative of trends of reported incidence.
no code implementations • 31 Mar 2016 • Prithwish Chakraborty, Sathappan Muthiah, Ravi Tandon, Naren Ramakrishnan
We propose hierarchical quickest change detection (HQCD), a framework that formalizes the process of incorporating additional correlated sources for early changepoint detection.
no code implementations • 20 Mar 2016 • Pejman Khadivi, Ravi Tandon, Naren Ramakrishnan
Feed-forward deep neural networks have been used extensively in various machine learning applications.
1 code implementation • 1 Mar 2016 • Saurav Ghosh, Prithwish Chakraborty, Emily Cohn, John S. Brownstein, Naren Ramakrishnan
Traditional disease surveillance can be augmented with a wide variety of real-time sources such as, news and social media.
no code implementations • 22 Feb 2016 • Hao Wu, Xinwei Deng, Naren Ramakrishnan
Modeling data with multivariate count responses is a challenging problem due to the discrete nature of the responses.
no code implementations • 21 Feb 2016 • Dipayan Maiti, Mohammad Raihanul Islam, Scotland Leman, Naren Ramakrishnan
Storytelling algorithms aim to 'connect the dots' between disparate documents by linking starting and ending documents through a series of intermediate documents.