Search Results for author: Srinivasan Parthasarathy

Found 55 papers, 20 papers with code

Evaluating Biomedical Word Embeddings for Vocabulary Alignment at Scale in the UMLS Metathesaurus Using Siamese Networks

no code implementations insights (ACL) 2022 Goonmeet Bajaj, Vinh Nguyen, Thilini Wijesiriwardene, Hong Yung Yip, Vishesh Javangula, Amit Sheth, Srinivasan Parthasarathy, Olivier Bodenreider

Recent work uses a Siamese Network, initialized with BioWordVec embeddings (distributed word embeddings), for predicting synonymy among biomedical terms to automate a part of the UMLS (Unified Medical Language System) Metathesaurus construction process.

Word Embeddings

How Effectively Can Machines Defend Against Machine-Generated Fake News? An Empirical Study

no code implementations EMNLP (insights) 2020 Meghana Moorthy Bhat, Srinivasan Parthasarathy

We empirically study the effectiveness of machine-generated fake news detectors by understanding the model’s sensitivity to different synthetic perturbations during test time.

REASONS: A benchmark for REtrieval and Automated citationS Of scieNtific Sentences using Public and Proprietary LLMs

no code implementations3 May 2024 Deepa Tilwani, Yash Saxena, Ali Mohammadi, Edward Raff, Amit Sheth, Srinivasan Parthasarathy, Manas Gaur

Automatic citation generation for sentences in a document or report is paramount for intelligence analysts, cybersecurity, news agencies, and education personnel.

Hallucination Retrieval +1

HeteroMILE: a Multi-Level Graph Representation Learning Framework for Heterogeneous Graphs

no code implementations31 Mar 2024 Yue Zhang, Yuntian He, Saket Gurukar, Srinivasan Parthasarathy

To address this issue, we propose a Multi-Level Embedding framework of nodes on a heterogeneous graph (HeteroMILE) - a generic methodology that allows contemporary graph embedding methods to scale to large graphs.

Graph Embedding Graph Representation Learning +2

Grounding from an AI and Cognitive Science Lens

no code implementations19 Feb 2024 Goonmeet Bajaj, Srinivasan Parthasarathy, Valerie L. Shalin, Amit Sheth

Grounding is a challenging problem, requiring a formal definition and different levels of abstraction.

Masked LoGoNet: Fast and Accurate 3D Image Analysis for Medical Domain

no code implementations9 Feb 2024 Amin Karimi Monsefi, Payam Karisani, Mengxi Zhou, Stacey Choi, Nathan Doble, Heng Ji, Srinivasan Parthasarathy, Rajiv Ramnath

In this paper, we introduce a new neural network architecture, termed LoGoNet, with a tailored self-supervised learning (SSL) method to mitigate such challenges.

Contrastive Learning Image Segmentation +4

Modeling Sequences as Star Graphs to Address Over-smoothing in Self-attentive Sequential Recommendation

no code implementations13 Nov 2023 Bo Peng, Ziqi Chen, Srinivasan Parthasarathy, Xia Ning

As widely demonstrated in the literature, this issue could lead to a loss of information in individual items, and significantly degrade models' scalability and performance.

Sequential Recommendation

Towards Efficient and Effective Adaptation of Large Language Models for Sequential Recommendation

no code implementations2 Oct 2023 Bo Peng, Ben Burns, Ziqi Chen, Srinivasan Parthasarathy, Xia Ning

In addition, SSNA adapts the top-a layers of LLMs jointly, and integrates adapters sequentially for enhanced effectiveness (i. e., recommendation performance).

Sequential Recommendation

Multi-modality Meets Re-learning: Mitigating Negative Transfer in Sequential Recommendation

no code implementations18 Sep 2023 Bo Peng, Srinivasan Parthasarathy, Xia Ning

Our experimental results demonstrate that ANT does not suffer from the negative transfer issue on any of the target tasks.

Sequential Recommendation

Shape-conditioned 3D Molecule Generation via Equivariant Diffusion Models

no code implementations23 Aug 2023 Ziqi Chen, Bo Peng, Srinivasan Parthasarathy, Xia Ning

Ligand-based drug design aims to identify novel drug candidates of similar shapes with known active molecules.

3D Molecule Generation

PolicyClusterGCN: Identifying Efficient Clusters for Training Graph Convolutional Networks

no code implementations25 Jun 2023 Saket Gurukar, Shaileshh Bojja Venkatakrishnan, Balaraman Ravindran, Srinivasan Parthasarathy

Specifically, the subgraph-based sampling approaches such as ClusterGCN and GraphSAINT have achieved state-of-the-art performance on the node classification tasks.

graph partitioning Node Classification +1

FairMILE: Towards an Efficient Framework for Fair Graph Representation Learning

1 code implementation17 Nov 2022 Yuntian He, Saket Gurukar, Srinivasan Parthasarathy

FairMILE is a multi-level paradigm that can efficiently learn graph representations while enforcing fairness and preserving utility.

Fairness Graph Embedding +1

Recursive Attentive Methods with Reused Item Representations for Sequential Recommendation

no code implementations16 Sep 2022 Bo Peng, Srinivasan Parthasarathy, Xia Ning

Our run-time performance comparison signifies that RAM could also be more efficient on benchmark datasets.

Sequential Recommendation

Prospective Preference Enhanced Mixed Attentive Model for Session-based Recommendation

no code implementations4 Jun 2022 Bo Peng, Chang-Yu Tai, Srinivasan Parthasarathy, Xia Ning

In this manuscript, we develop prospective preference enhanced mixed attentive model (P2MAM) to generate session-based recommendations using two important factors: temporal patterns and estimates of users' prospective preferences.

Position Session-Based Recommendations

FairEGM: Fair Link Prediction and Recommendation via Emulated Graph Modification

no code implementations27 Jan 2022 Sean Current, Yuntian He, Saket Gurukar, Srinivasan Parthasarathy

As machine learning becomes more widely adopted across domains, it is critical that researchers and ML engineers think about the inherent biases in the data that may be perpetuated by the model.

Fairness Graph Neural Network +1

Semi-Supervised Deep Learning for Multiplex Networks

1 code implementation5 Oct 2021 Anasua Mitra, Priyesh Vijayan, Ranbir Sanasam, Diganta Goswami, Srinivasan Parthasarathy, Balaraman Ravindran

Multiplex networks are complex graph structures in which a set of entities are connected to each other via multiple types of relations, each relation representing a distinct layer.

Representation Learning

Privacy Policy Question Answering Assistant: A Query-Guided Extractive Summarization Approach

no code implementations29 Sep 2021 Moniba Keymanesh, Micha Elsner, Srinivasan Parthasarathy

We address these problems by paraphrasing to bring the style and language of the user's question closer to the language of privacy policies.

Extractive Summarization Question Answering

Evaluating Biomedical BERT Models for Vocabulary Alignment at Scale in the UMLS Metathesaurus

no code implementations14 Sep 2021 Goonmeet Bajaj, Vinh Nguyen, Thilini Wijesiriwardene, Hong Yung Yip, Vishesh Javangula, Srinivasan Parthasarathy, Amit Sheth, Olivier Bodenreider

Given the SOTA performance of these BERT models for other downstream tasks, our experiments yield surprisingly interesting results: (1) in both model architectures, the approaches employing these biomedical BERT-based models do not outperform the existing approaches using Siamese Network with BioWordVec embeddings for the UMLS synonymy prediction task, (2) the original BioBERT large model that has not been pre-trained with the UMLS outperforms the SapBERT models that have been pre-trained with the UMLS, and (3) using the Siamese Networks yields better performance for synonymy prediction when compared to using the biomedical BERT models.

Task 2 Word Embeddings

Fairness-aware Summarization for Justified Decision-Making

no code implementations13 Jul 2021 Moniba Keymanesh, Tanya Berger-Wolf, Micha Elsner, Srinivasan Parthasarathy

In other words, decision-relevant features should provide sufficient information for the predicted outcome and should be independent of the membership of individuals in protected groups such as race and gender.

Data Poisoning Decision Making +1

A Machine Learning Model for Nowcasting Epidemic Incidence

1 code implementation5 Apr 2021 Saumya Yashmohini Sahai, Saket Gurukar, Wasiur R. KhudaBukhsh, Srinivasan Parthasarathy, Grzegorz A. Rempala

Due to delay in reporting, the daily national and statewide COVID-19 incidence counts are often unreliable and need to be estimated from recent data.

BIG-bench Machine Learning

A Tight Bound for Stochastic Submodular Cover

no code implementations1 Feb 2021 Lisa Hellerstein, Devorah Kletenik, Srinivasan Parthasarathy

We show that the Adaptive Greedy algorithm of Golovin and Krause (2011) achieves an approximation bound of $(\ln (Q/\eta)+1)$ for Stochastic Submodular Cover: here $Q$ is the "goal value" and $\eta$ is the smallest non-zero marginal increase in utility deliverable by an item.

A Deep Generative Model for Molecule Optimization via One Fragment Modification

2 code implementations8 Dec 2020 Ziqi Chen, Martin Renqiang Min, Srinivasan Parthasarathy, Xia Ning

A pipeline of multiple, identical Modof models is implemented into Modof-pipe to modify an input molecule at multiple disconnection sites.

Drug Discovery

M2: Mixed Models with Preferences, Popularities and Transitions for Next-Basket Recommendation

3 code implementations3 Apr 2020 Bo Peng, Zhiyun Ren, Srinivasan Parthasarathy, Xia Ning

We compared M2 with different combinations of the factors with 5 state-of-the-art next-basket recommendation methods on 4 public benchmark datasets in recommending the first, second and third next basket.

Decoder Next-basket recommendation

HAM: Hybrid Associations Models for Sequential Recommendation

2 code implementations27 Feb 2020 Bo Peng, Zhiyun Ren, Srinivasan Parthasarathy, Xia Ning

We compared HAM models with the most recent, state-of-the-art methods on six public benchmark datasets in three different experimental settings.

Sequential Recommendation

Towards Quantifying the Distance between Opinions

no code implementations27 Jan 2020 Saket Gurukar, Deepak Ajwani, Sourav Dutta, Juho Lauri, Srinivasan Parthasarathy, Alessandra Sala

Similarly, in a supervised setting, our opinion distance measure achieves considerably better accuracy (up to 20% increase) compared to extant approaches that rely on text similarity, stance similarity, and sentiment similarity

Navigate text similarity

An End-to-End Framework for Cold Question Routing in Community Question Answering Services

no code implementations22 Nov 2019 Jiankai Sun, Jie Zhao, Huan Sun, Srinivasan Parthasarathy

Routing newly posted questions (a. k. a cold questions) to potential answerers with the suitable expertise in Community Question Answering sites (CQAs) is an important and challenging task.

Community Question Answering Graph Embedding

Automatic Table completion using Knowledge Base

no code implementations20 Sep 2019 Bortik Bandyopadhyay, Xiang Deng, Goonmeet Bajaj, Huan Sun, Srinivasan Parthasarathy

In this work, we propose to resolve a new type of heterogeneous query viz: tabular query, which contains a natural language query description, column names of the desired table, and an example row.

Decision Making

Accident Risk Prediction based on Heterogeneous Sparse Data: New Dataset and Insights

11 code implementations19 Sep 2019 Sobhan Moosavi, Mohammad Hossein Samavatian, Srinivasan Parthasarathy, Radu Teodorescu, Rajiv Ramnath

Further, we have shown the impact of traffic information, time, and points-of-interest data for real-time accident prediction.

A Countrywide Traffic Accident Dataset

9 code implementations12 Jun 2019 Sobhan Moosavi, Mohammad Hossein Samavatian, Srinivasan Parthasarathy, Rajiv Ramnath

Reducing traffic accidents is an important public safety challenge.

Databases Computers and Society

Graph Embedding on Biomedical Networks: Methods, Applications, and Evaluations

4 code implementations12 Jun 2019 Xiang Yue, Zhen Wang, Jingong Huang, Srinivasan Parthasarathy, Soheil Moosavinasab, Yungui Huang, Simon M. Lin, Wen Zhang, Ping Zhang, Huan Sun

Our experimental results demonstrate that the recent graph embedding methods achieve promising results and deserve more attention in the future biomedical graph analysis.

Graph Embedding Link Prediction +2

Optimal Exploitation of Clustering and History Information in Multi-Armed Bandit

no code implementations31 May 2019 Djallel Bouneffouf, Srinivasan Parthasarathy, Horst Samulowitz, Martin Wistub

We consider the stochastic multi-armed bandit problem and the contextual bandit problem with historical observations and pre-clustered arms.


Network Representation Learning: Consolidation and Renewed Bearing

1 code implementation2 May 2019 Saket Gurukar, Priyesh Vijayan, Aakash Srinivasan, Goonmeet Bajaj, Chen Cai, Moniba Keymanesh, Saravana Kumar, Pranav Maneriker, Anasua Mitra, Vedang Patel, Balaraman Ravindran, Srinivasan Parthasarathy

An important area of research that has emerged over the last decade is the use of graphs as a vehicle for non-linear dimensionality reduction in a manner akin to previous efforts based on manifold learning with uses for downstream database processing, machine learning and visualization.

Dimensionality Reduction General Classification +3

Towards Open Intent Discovery for Conversational Text

no code implementations17 Apr 2019 Nikhita Vedula, Nedim Lipka, Pranav Maneriker, Srinivasan Parthasarathy

Existing research for intent discovery model it as a classification task with a predefined set of known categories.

Intent Discovery Open Intent Discovery

PL-NMF: Parallel Locality-Optimized Non-negative Matrix Factorization

no code implementations16 Apr 2019 Gordon E. Moon, Aravind Sukumaran-Rajam, Srinivasan Parthasarathy, P. Sadayappan

Non-negative Matrix Factorization (NMF) is a key kernel for unsupervised dimension reduction used in a wide range of applications, including topic modeling, recommender systems and bioinformatics.

Dimensionality Reduction Recommendation Systems

Hypergraph Clustering: A Modularity Maximization Approach

no code implementations28 Dec 2018 Tarun Kumar, Sankaran Vaidyanathan, Harini Ananthapadmanabhan, Srinivasan Parthasarathy, Balaraman Ravindran

Clustering on hypergraphs has been garnering increased attention with potential applications in network analysis, VLSI design and computer vision, among others.


ATP: Directed Graph Embedding with Asymmetric Transitivity Preservation

1 code implementation2 Nov 2018 Jiankai Sun, Bortik Bandyopadhyay, Armin Bashizade, Jiongqian Liang, P. Sadayappan, Srinivasan Parthasarathy

Directed graphs have been widely used in Community Question Answering services (CQAs) to model asymmetric relationships among different types of nodes in CQA graphs, e. g., question, answer, user.

Community Question Answering Graph Embedding +1

ColdRoute: Effective Routing of Cold Questions in Stack Exchange Sites

1 code implementation2 Jul 2018 Jiankai Sun, Abhinav Vishnu, Aniket Chakrabarti, Charles Siegel, Srinivasan Parthasarathy

Using data from eight stack exchange sites, we are able to improve upon the routing metrics (Precision$@1$, Accuracy, MRR) over the state-of-the-art models such as semantic matching by $159. 5\%$,$31. 84\%$, and $40. 36\%$ for cold questions posted by existing askers, and $123. 1\%$, $27. 03\%$, and $34. 81\%$ for cold questions posted by new askers respectively.

Fusion Graph Convolutional Networks

1 code implementation31 May 2018 Priyesh Vijayan, Yash Chandak, Mitesh M. Khapra, Srinivasan Parthasarathy, Balaraman Ravindran

State-of-the-art models for node classification on such attributed graphs use differentiable recursive functions that enable aggregation and filtering of neighborhood information from multiple hops.

General Classification Node Classification

HOPF: Higher Order Propagation Framework for Deep Collective Classification

1 code implementation31 May 2018 Priyesh Vijayan, Yash Chandak, Mitesh M. Khapra, Srinivasan Parthasarathy, Balaraman Ravindran

Given a graph where every node has certain attributes associated with it and some nodes have labels associated with them, Collective Classification (CC) is the task of assigning labels to every unlabeled node using information from the node as well as its neighbors.

Attribute Classification +1

QDEE: Question Difficulty and Expertise Estimation in Community Question Answering Sites

1 code implementation31 Mar 2018 Jiankai Sun, Sobhan Moosavi, Rajiv Ramnath, Srinivasan Parthasarathy

We also propose a model to route newly posted questions to appropriate users based on the difficulty level of the question and the expertise of the user.

Community Question Answering

Adaptive Bayesian Sampling with Monte Carlo EM

no code implementations NeurIPS 2017 Anirban Roychowdhury, Srinivasan Parthasarathy

Our approach provides a simpler alternative, by using existing dynamics in the sampling step of a Monte Carlo EM framework, and learning the mass matrices in the M step with a novel online technique.

Multimodal Content Analysis for Effective Advertisements on YouTube

no code implementations12 Sep 2017 Nikhita Vedula, Wei Sun, Hyunhwan Lee, Harsh Gupta, Mitsunori Ogihara, Joseph Johnson, Gang Ren, Srinivasan Parthasarathy

The objective of this work is then to measure the effectiveness of an advertisement, and to recommend a useful set of features to advertisement designers to make it more successful and approachable to users.

Recommendation Systems

Fast Change Point Detection on Dynamic Social Networks

no code implementations20 May 2017 Yu Wang, Aniket Chakrabarti, David Sivakoff, Srinivasan Parthasarathy

In this work we devise an effective and efficient three-step-approach for detecting change points in dynamic networks under the snapshot model.

Change Point Detection Sociology

REMIX: Automated Exploration for Interactive Outlier Detection

no code implementations17 May 2017 Yanjie Fu, Charu Aggarwal, Srinivasan Parthasarathy, Deepak S. Turaga, Hui Xiong

This formulation incorporates multiple aspects such as (i) an upper limit on the total execution time of detectors (ii) diversity in the space of algorithms and features, and (iii) meta-learning for evaluating the cost and utility of detectors.

Meta-Learning Outlier Detection

Semi-supervised Embedding in Attributed Networks with Outliers

no code implementations23 Mar 2017 Jiongqian Liang, Peter Jacobs, Jiankai Sun, Srinivasan Parthasarathy

In this paper, we propose a novel framework, called Semi-supervised Embedding in Attributed Networks with Outliers (SEANO), to learn a low-dimensional vector representation that systematically captures the topological proximity, attribute affinity and label similarity of vertices in a partially labeled attributed network (PLAN).


Robust Contextual Outlier Detection: Where Context Meets Sparsity

no code implementations28 Jul 2016 Jiongqian Liang, Srinivasan Parthasarathy

To address these problems, here we propose a novel and robust approach alternative to the state-of-the-art called RObust Contextual Outlier Detection (ROCOD).

Outlier Detection

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