Search Results for author:
Found 56 papers, 17 papers with code
Distantly Supervised Aspect Clustering And Naming For E-Commerce Reviews
While aspect phrases extraction and sentiment analysis have received a lot of attention, clustering of aspect phrases and assigning human readable names to clusters in e-commerce reviews is an extremely important and challenging problem due to the scale of the reviews that makes human review infeasible.
NLM at MEDIQA 2021: Transfer Learning-based Approaches for Consumer Question and Multi-Answer Summarization
One of the cardinal tasks in achieving robust consumer health question answering systems is the question summarization and multi-document answer summarization.
Overview of the MedVidQA 2022 Shared Task on Medical Video Question-Answering
The shared task addressed two of the challenges faced by medical video question answering: (I) a video classification task that explores new approaches to medical video understanding (labeling), and (ii) a visual answer localization task.
Overview of TREC 2024 Medical Video Question Answering (MedVidQA) Track
Earlier works on medical question answering primarily focused on textual and visual (image) modalities, which may be inefficient in answering questions requiring demonstration.
Overview of TREC 2024 Biomedical Generative Retrieval (BioGen) Track
Methods for grounding generated statements in reliable sources along with practical evaluation approaches are needed to overcome this barrier.
Enhancing Deep Learning based RMT Data Inversion using Gaussian Random Field
Synthetic experiments confirmed that the GRF dataset enhances generalization compared to a homogeneous background OOD dataset.
Bioimpedance a Diagnostic Tool for Tobacco Induced Oral Lesions: a Mixed Model cross-sectional study
Methodology: The study comprised 50 OSCC and OPMD tissue specimens for in-vitro study and 320 subjects for in vivo study.
Surgical Feature-Space Decomposition of LLMs: Why, When and How?
Low-rank approximations, of the weight and feature space can enhance the performance of deep learning models, whether in terms of improving generalization or reducing the latency of inference.
Interpretable User Satisfaction Estimation for Conversational Systems with Large Language Models
Accurate and interpretable user satisfaction estimation (USE) is critical for understanding, evaluating, and continuously improving conversational systems.
Beyond Uniform Scaling: Exploring Depth Heterogeneity in Neural Architectures
Our method is flexible towards skip connections a mainstay in modern vision transformers.
Faster and Lighter LLMs: A Survey on Current Challenges and Way Forward
Despite the impressive performance of LLMs, their widespread adoption faces challenges due to substantial computational and memory requirements during inference.
Rethinking Compression: Reduced Order Modelling of Latent Features in Large Language Models
Due to the substantial scale of Large Language Models (LLMs), the direct application of conventional compression methodologies proves impractical.
Efficient Expansion and Gradient Based Task Inference for Replay Free Incremental Learning
Our work proposes a simple filter and channel expansion based method that grows the model over the previous task parameters and not just over the global parameter.
Towards Answering Health-related Questions from Medical Videos: Datasets and Approaches
Toward this, this paper is focused on answering health-related questions asked by the public by providing visual answers from medical videos.
VERSE: Virtual-Gradient Aware Streaming Lifelong Learning with Anytime Inference
Streaming lifelong learning is a challenging setting of lifelong learning with the goal of continuous learning in a dynamic non-stationary environment without forgetting.
One-for-All: Generalized LoRA for Parameter-Efficient Fine-tuning
We present Generalized LoRA (GLoRA), an advanced approach for universal parameter-efficient fine-tuning tasks.
Large Scale Generative Multimodal Attribute Extraction for E-commerce Attributes
In a popular e-commerce store, we have deployed our models for 1000s of (product-type, attribute) pairs.
Empowering Language Model with Guided Knowledge Fusion for Biomedical Document Re-ranking
Pre-trained language models (PLMs) have proven to be effective for document re-ranking task.
Aux-Drop: Handling Haphazard Inputs in Online Learning Using Auxiliary Dropouts
Many real-world applications based on online learning produce streaming data that is haphazard in nature, i. e., contains missing features, features becoming obsolete in time, the appearance of new features at later points in time and a lack of clarity on the total number of input features.
Classifying text using machine learning models and determining conversation drift
Text classification helps analyse texts for semantic meaning and relevance, by mapping the words against this hierarchy.
Machine Learning enabled models for YouTube Ranking Mechanism and Views Prediction
Out of this, the application that is making sure that the world stays in touch with each other and with current affairs is social media.
Learning to Answer Multilingual and Code-Mixed Questions
In this dissertation, we focus on advancing QA techniques for handling end-user queries in multilingual environments.
Medical Image Retrieval via Nearest Neighbor Search on Pre-trained Image Features
We extensively tested the proposed NNS approach and compared the performance with state-of-the-art NNS approaches on benchmark datasets and our created medical image datasets.
CHQ-Summ: A Dataset for Consumer Healthcare Question Summarization
The quest for seeking health information has swamped the web with consumers' health-related questions.
A Dataset for Medical Instructional Video Classification and Question Answering
This paper introduces a new challenge and datasets to foster research toward designing systems that can understand medical videos and provide visual answers to natural language questions.
Towards Developing a Multilingual and Code-Mixed Visual Question Answering System by Knowledge Distillation
We also create the large-scale multilingual and code-mixed VQA dataset in eleven different language setups considering the multiple Indian and European languages.
BloomNet: A Robust Transformer based model for Bloom's Learning Outcome Classification
For the optimization of educational programs, it is crucial to design course learning outcomes (CLOs) according to the different cognitive levels of Bloom Taxonomy.
Reinforcement Learning for Abstractive Question Summarization with Question-aware Semantic Rewards
The growth of online consumer health questions has led to the necessity for reliable and accurate question answering systems.
Question-aware Transformer Models for Consumer Health Question Summarization
In this paper, we study the task of abstractive summarization for real-world consumer health questions.
Effective Resource-Competition Model for Species Coexistence
Finally, we analytically compute the distribution of the population sizes of coexisting species.
CovidGAN: Data Augmentation Using Auxiliary Classifier GAN for Improved Covid-19 Detection
This has led to the introduction of a variety of deep learning systems and studies have shown that the accuracy of COVID-19 patient detection through the use of chest X-rays is strongly optimistic.
Domain Controlled Title Generation with Human Evaluation
We study automatic title generation and present a method for generating domain-controlled titles for scientific articles.
Can Taxonomy Help? Improving Semantic Question Matching using Question Taxonomy
In this paper, we propose a hybrid technique for semantic question matching.
Rescaling CNN through Learnable Repetition of Network Parameters
We show that small base networks when rescaled, can provide performance comparable to deeper networks with as low as 6% of optimization parameters of the deeper one.
Resetting with stochastic return through linear confining potential
In contrast to the usual setting where the particle is instantaneously reset to a preferred location (say, the origin), here we consider a finite time resetting process facilitated by an external linear potential $V(x)=\lambda|x|~ (\lambda>0)$.
Statistical Mechanics
A Unified Framework for Multilingual and Code-Mixed Visual Question Answering
We propose a robust tech- nique capable of handling the multilingual and code-mixed question to provide the answer against the visual information (image).
Siamese Tracking with Lingual Object Constraints
Classically, visual object tracking involves following a target object throughout a given video, and it provides us the motion trajectory of the object.
A Semi-supervised Approach to Generate the Code-Mixed Text using Pre-trained Encoder and Transfer Learning
Code-mixing, the interleaving of two or more languages within a sentence or discourse is ubiquitous in multilingual societies.
Hierarchical Deep Multi-modal Network for Medical Visual Question Answering
To address this issue, we propose a hierarchical deep multi-modal network that analyzes and classifies end-user questions/queries and then incorporates a query-specific approach for answer prediction.
A Three-Stage Algorithm for the Large Scale Dynamic Vehicle Routing Problem with an Industry 4.0 Approach
Companies are eager to have a smart supply chain especially when they have a dynamic system.
A Two-Stage Metaheuristic Algorithm for the Dynamic Vehicle Routing Problem in Industry 4.0 approach
This research works on DVRP.
Reinforced Multi-task Approach for Multi-hop Question Generation
Question generation (QG) attempts to solve the inverse of question answering (QA) problem by generating a natural language question given a document and an answer.
Diagnosis of Breast Cancer Based on Modern Mammography using Hybrid Transfer Learning
Modified VGG (MVGG), residual network, mobile network is proposed and implemented in this paper.
Hiding Data in Images Using Cryptography and Deep Neural Network
Previously, steganography has been combined with cryptography and neural networks separately.
Improving Neural Question Generation using World Knowledge
In this paper, we propose a method for incorporating world knowledge (linked entities and fine-grained entity types) into a neural question generation model.
Helping each Other: A Framework for Customer-to-Customer Suggestion Mining using a Semi-supervised Deep Neural Network
We evaluate the performance of our proposed model on a benchmark customer review dataset, comprising of the reviews of Hotel and Electronics domains.
Uncovering Code-Mixed Challenges: A Framework for Linguistically Driven Question Generation and Neural Based Question Answering
In this paper, we propose a linguistically motivated technique for code-mixed question generation (CMQG) and a neural network based architecture for code-mixed question answering (CMQA).
Combining Graph-based Dependency Features with Convolutional Neural Network for Answer Triggering
Answer triggering is the task of selecting the best-suited answer for a given question from a set of candidate answers if exists.
IITP at IJCNLP-2017 Task 4: Auto Analysis of Customer Feedback using CNN and GRU Network
Our empirical analysis shows that our models perform well in all the four languages on the setups of IJCNLP Shared Task on Customer Feedback Analysis.
Auto Analysis of Customer Feedback using CNN and GRU Network
Analyzing customer feedback is the best way to channelize the data into new marketing strategies that benefit entrepreneurs as well as customers.
IIT-UHH at SemEval-2017 Task 3: Exploring Multiple Features for Community Question Answering and Implicit Dialogue Identification
In this paper we present the system for Answer Selection and Ranking in Community Question Answering, which we build as part of our participation in SemEval-2017 Task 3.
SMPOST: Parts of Speech Tagger for Code-Mixed Indic Social Media Text
For the task of PoS tagging on Code-Mixed Indian Social Media Text, we develop a supervised system based on Conditional Random Field classifier.
