26 citations · 181 across the 49 of their papers we have counts for
23 papers · 1 filter
K-PERM: Personalized Response Generation Using Dynamic Knowledge Retrieval and Persona-Adaptive Queries
Kanak Raj, Kaushik Roy, Vamshi Bonagiri +3
Personalizing conversational agents can enhance the quality of conversations and increase user engagement. However, they often lack external knowledge to appropriately tend to a us…
L3 Ensembles: Lifelong Learning Approach for Ensemble of Foundational Language Models
Aidin Shiri, Kaushik Roy, Amit Sheth +1
Fine-tuning pre-trained foundational language models (FLM) for specific tasks is often impractical, especially for resource-constrained devices. This necessitates the development o…
Hi Model, generating 'nice' instead of 'good' is not as bad as generating 'rice'! Towards Context and Semantic Infused Dialogue Generation Loss Function and Evaluation Metric
Abhisek Tiwari, Muhammed Sinan, Kaushik Roy +3
Over the past two decades, dialogue modeling has made significant strides, moving from simple rule-based responses to personalized and persuasive response generation. However, desp…
L3DMC: Lifelong Learning using Distillation via Mixed-Curvature Space
Kaushik Roy, Peyman Moghadam, Mehrtash Harandi
The performance of a lifelong learning (L3) model degrades when it is trained on a series of tasks, as the geometrical formation of the embedding space changes while learning novel…
Subspace Distillation for Continual Learning
Kaushik Roy, Christian Simon, Peyman Moghadam +1
An ultimate objective in continual learning is to preserve knowledge learned in preceding tasks while learning new tasks. To mitigate forgetting prior knowledge, we propose a novel…
Memorization Through the Lens of Curvature of Loss Function Around Samples
Isha Garg, Deepak Ravikumar, Kaushik Roy
Deep neural networks are over-parameterized and easily overfit the datasets they train on. In the extreme case, it has been shown that these networks can memorize a training set wi…
IERL: Interpretable Ensemble Representation Learning -- Combining CrowdSourced Knowledge and Distributed Semantic Representations
Yuxin Zi, Kaushik Roy, Vignesh Narayanan +2
Large Language Models (LLMs) encode meanings of words in the form of distributed semantics. Distributed semantics capture common statistical patterns among language tokens (words,…
Knowledge-Infused Self Attention Transformers
Kaushik Roy, Yuxin Zi, Vignesh Narayanan +2
Transformer-based language models have achieved impressive success in various natural language processing tasks due to their ability to capture complex dependencies and contextual…
Social network modeling and applications, a tutorial
Lisette Espín-Noboa, Tiago Peixoto, Fariba Karimi
Social networks have been widely studied over the last century from multiple disciplines to understand societal issues such as inequality in employment rates, managerial performanc…
Process Knowledge-infused Learning for Clinician-friendly Explanations
Kaushik Roy, Yuxin Zi, Manas Gaur +4
Language models have the potential to assess mental health using social media data. By analyzing online posts and conversations, these models can detect patterns indicating mental…
S-TLLR: STDP-inspired Temporal Local Learning Rule for Spiking Neural Networks
Marco Paul E. Apolinario, Kaushik Roy
Spiking Neural Networks (SNNs) are biologically plausible models that have been identified as potentially apt for deploying energy-efficient intelligence at the edge, particularly…
Cook-Gen: Robust Generative Modeling of Cooking Actions from Recipes
Revathy Venkataramanan, Kaushik Roy, Kanak Raj +4
As people become more aware of their food choices, food computation models have become increasingly popular in assisting people in maintaining healthy eating habits. For example, f…
ProKnow: Process Knowledge for Safety Constrained and Explainable Question Generation for Mental Health Diagnostic Assistance
Kaushik Roy, Manas Gaur, Misagh Soltani +3
Current Virtual Mental Health Assistants (VMHAs) provide counseling and suggestive care. They refrain from patient diagnostic assistance because they lack training in safety-constr…
Knowledge Graph Guided Semantic Evaluation of Language Models For User Trust
Kaushik Roy, Tarun Garg, Vedant Palit +3
A fundamental question in natural language processing is - what kind of language structure and semantics is the language model capturing? Graph formats such as knowledge graphs are…
Global Update Tracking: A Decentralized Learning Algorithm for Heterogeneous Data
Sai Aparna Aketi, Abolfazl Hashemi, Kaushik Roy
Decentralized learning enables the training of deep learning models over large distributed datasets generated at different locations, without the need for a central server. However…
Segmented Recurrent Transformer: An Efficient Sequence-to-Sequence Model
Yinghan Long, Sayeed Shafayet Chowdhury, Kaushik Roy
Transformers have shown dominant performance across a range of domains including language and vision. However, their computational cost grows quadratically with the sequence length…
FEDORA: Flying Event Dataset fOr Reactive behAvior
Amogh Joshi, Adarsh Kosta, Wachirawit Ponghiran +2
The ability of resource-constrained biological systems such as fruitflies to perform complex and high-speed maneuvers in cluttered environments has been one of the prime sources of…
Neurosymbolic AI -- Why, What, and How
Amit Sheth, Kaushik Roy, Manas Gaur
Humans interact with the environment using a combination of perception - transforming sensory inputs from their environment into symbols, and cognition - mapping symbols to knowled…
Fairness in Visual Clustering: A Novel Transformer Clustering Approach
Xuan-Bac Nguyen, Chi Nhan Duong, Marios Savvides +3
Promoting fairness for deep clustering models in unsupervised clustering settings to reduce demographic bias is a challenging goal. This is because of the limitation of large-scale…
Homogenizing Non-IID datasets via In-Distribution Knowledge Distillation for Decentralized Learning
Deepak Ravikumar, Gobinda Saha, Sai Aparna Aketi +1
Decentralized learning enables serverless training of deep neural networks (DNNs) in a distributed manner on multiple nodes. This allows for the use of large datasets, as well as t…
Demo Alleviate: Demonstrating Artificial Intelligence Enabled Virtual Assistance for Telehealth: The Mental Health Case
Kaushik Roy, Vedant Khandelwal, Raxit Goswami +3
After the pandemic, artificial intelligence (AI) powered support for mental health care has become increasingly important. The breadth and complexity of significant challenges requ…
CoDeC: Communication-Efficient Decentralized Continual Learning
Sakshi Choudhary, Sai Aparna Aketi, Gobinda Saha +1
Training at the edge utilizes continuously evolving data generated at different locations. Privacy concerns prohibit the co-location of this spatially as well as temporally distrib…
Continual Learning with Scaled Gradient Projection
Gobinda Saha, Kaushik Roy
In neural networks, continual learning results in gradient interference among sequential tasks, leading to catastrophic forgetting of old tasks while learning new ones. This issue…