Search Results for author: Suman Banerjee

Found 14 papers, 5 papers with code

One Prompt To Rule Them All: LLMs for Opinion Summary Evaluation

1 code implementation18 Feb 2024 Tejpalsingh Siledar, Swaroop Nath, Sankara Sri Raghava Ravindra Muddu, Rupasai Rangaraju, Swaprava Nath, Pushpak Bhattacharyya, Suman Banerjee, Amey Patil, Sudhanshu Shekhar Singh, Muthusamy Chelliah, Nikesh Garera

Evaluation of opinion summaries using conventional reference-based metrics rarely provides a holistic evaluation and has been shown to have a relatively low correlation with human judgments.

nlg evaluation Opinion Summarization +1

Minimizing Regret in Billboard Advertisement under Zonal Influence Constraint

no code implementations2 Feb 2024 Dildar Ali, Suman Banerjee, Yamuna Prasad

In the second one, we introduce randomness with the first one, where we perform the marginal gain computation for a sample of randomly chosen billboard slots.

Towards Regret Free Slot Allocation in Billboard Advertisement

no code implementations29 Jan 2024 Dildar Ali, Suman Banerjee, Yamuna Prasad

In the context of an influence provider, it is a loss for him if he offers more or less views.

Hierarchical Federated Learning with Privacy

no code implementations10 Jun 2022 Varun Chandrasekaran, Suman Banerjee, Diego Perino, Nicolas Kourtellis

Federated learning (FL), where data remains at the federated clients, and where only gradient updates are shared with a central aggregator, was assumed to be private.

Federated Learning

Fast and Sample-Efficient Domain Adaptation for Autoencoder-Based End-to-End Communication

no code implementations29 Sep 2021 Jayaram Raghuram, Yijing Zeng, Dolores Garcia, Somesh Jha, Suman Banerjee, Joerg Widmer, Rafael Ruiz

In this paper, we address the setting where the target domain has only limited labeled data from a distribution that is expected to change frequently.

Domain Adaptation

Few-Shot Domain Adaptation For End-to-End Communication

1 code implementation2 Aug 2021 Jayaram Raghuram, Yijing Zeng, Dolores García Martí, Rafael Ruiz Ortiz, Somesh Jha, Joerg Widmer, Suman Banerjee

The problem of end-to-end learning of a communication system using an autoencoder -- consisting of an encoder, channel, and decoder modeled using neural networks -- has recently been shown to be an effective approach.

Domain Adaptation Semi-supervised Domain Adaptation

A General Framework For Detecting Anomalous Inputs to DNN Classifiers

1 code implementation29 Jul 2020 Jayaram Raghuram, Varun Chandrasekaran, Somesh Jha, Suman Banerjee

We propose an unsupervised anomaly detection framework based on the internal DNN layer representations in the form of a meta-algorithm with configurable components.

Image Classification Unsupervised Anomaly Detection

Earned Benefit Maximization in Social Networks Under Budget Constraint

no code implementations8 Apr 2020 Suman Banerjee, Mamata Jenamani, Dilip Kumar Pratihar

In this paper, we study this problem with a variation, where a set of nodes are designated as target nodes, each of them is assigned with a benefit value, that can be earned by influencing them, and our goal is to maximize the earned benefit by initially activating a set of nodes within the budget.

Social and Information Networks Data Structures and Algorithms Multiagent Systems

Graph Convolutional Network with Sequential Attention For Goal-Oriented Dialogue Systems

no code implementations ICLR 2019 Suman Banerjee, Mitesh M. Khapra

Domain specific goal-oriented dialogue systems typically require modeling three types of inputs, viz., (i) the knowledge-base associated with the domain, (ii) the history of the conversation, which is a sequence of utterances and (iii) the current utterance for which the response needs to be generated.

Document Dating Goal-Oriented Dialogue Systems +2

Target Set Selection parameterized by vertex cover and more

no code implementations4 Dec 2018 Suman Banerjee, Rogers Mathew, Fahad Panolan

We have the following results on the TSS problem: -> It was shown by Nichterlein et al. [Social Network Analysis and Mining, 2013] that it is possible to compute an optimal-sized target set in $O(2^{(2^{t}+1)t}\cdot m)$ time, where $t$ denotes the cardinality of a minimum degree-$0$ modulator of $G$.

Computational Complexity Data Structures and Algorithms Social and Information Networks 68W25, 68Q17, 68R10,

Towards Exploiting Background Knowledge for Building Conversation Systems

1 code implementation EMNLP 2018 Nikita Moghe, Siddhartha Arora, Suman Banerjee, Mitesh M. Khapra

Existing dialog datasets contain a sequence of utterances and responses without any explicit background knowledge associated with them.

A Survey on Influence Maximization in a Social Network

no code implementations16 Aug 2018 Suman Banerjee, Mamata Jenamani, Dilip Kumar Pratihar

Given a social network with diffusion probabilities as edge weights and an integer k, which k nodes should be chosen for initial injection of information to maximize influence in the network?

Social and Information Networks

Cannot find the paper you are looking for? You can Submit a new open access paper.