16 citations · 37 across the 25 of their papers we have counts for
26 papers
Learning Explainable Representations of Complex Game-playing Strategies
Abhijeet Krishnan, Colin M. Potts, Arnav Jhala +3
As part of learning to play complex games, human players develop develop abstractions for concepts and strategies of gameplay consistent with game rules to improve their performanc…
ArtMine: Discovering and Formalizing Artistic Processes
Kaustubh Kumar, Ashutosh Ranjan, Vivek Srivastava +2
Understanding how artworks are created requires reasoning about the iterative decisions, material operations, and contextual influences that shape artistic production. While recent…
Democratic ICAI: Debating Our Way to Steering Principles from Preferences
Kevin Kingslin, Anish Natekar, Ashutosh Ranjan +3
Preference-based alignment often struggles to capture the reasoning that underlies human judgments. Many evaluations rely on multiple interacting criteria, yet pairwise labels reve…
The Silent Brush: Evaluating Artistic Style Leakage in AI Art Generation
Ninad Joshi, Ashutosh Ranjan, Vivek Srivastava +1
Generative text-to-image models are typically trained on large-scale web-scraped datasets that include diverse visual content such as copyrighted and stylistically distinctive artw…
Polaris: A Gödel Agent Framework for Small Language Models through Experience-Abstracted Policy Repair
Aditya Kakade, Vivek Srivastava, Shirish Karande
Gödel agent realize recursive self-improvement: an agent inspects its own policy and traces and then modifies that policy in a tested loop. We introduce Polaris, Gödel agent for co…
Forgetting is Competition: Rethinking Unlearning as Representation Interference in Diffusion Models
Ashutosh Ranjan, Vivek Srivastava, Shirish Karande +1
Deployed text-to-image diffusion models increasingly require post-hoc concept unlearning for copyright claims, artist opt-outs, safety updates, and protected-content mitigation wit…
Quality Detection of Stored Potatoes via Transfer Learning: A CNN and Vision Transformer Approach
Shrikant Kapse, Priyankkumar Dhrangdhariya, Priya Kedia +5
Image-based deep learning provides a non-invasive, scalable solution for monitoring potato quality during storage, addressing key challenges such as sprout detection, weight loss e…
The Realignment Problem: When Right becomes Wrong in LLMs
Aakash Sen Sharma, Debdeep Sanyal, Manodeep Ray +3
Post-training alignment of large language models (LLMs) relies on large-scale human annotations guided by policy specifications that change over time. Cultural shifts, value reinte…
OrgAccess: A Benchmark for Role Based Access Control in Organization Scale LLMs
Debdeep Sanyal, Umakanta Maharana, Yash Sinha +4
Role-based access control (RBAC) and hierarchical structures are foundational to how information flows and decisions are made within virtually all organizations. As the potential o…
Nine Ways to Break Copyright Law and Why Our LLM Won't: A Fair Use Aligned Generation Framework
Aakash Sen Sharma, Debdeep Sanyal, Priyansh Srivastava +4
Large language models (LLMs) commonly risk copyright infringement by reproducing protected content verbatim or with insufficient transformative modifications, posing significant et…
Guardians of Generation: Dynamic Inference-Time Copyright Shielding with Adaptive Guidance for AI Image Generation
Soham Roy, Abhishek Mishra, Shirish Karande +1
Modern text-to-image generative models can inadvertently reproduce copyrighted content memorized in their training data, raising serious concerns about potential copyright infringe…
Advancing NAM-to-Speech Conversion with Novel Methods and the MultiNAM Dataset
Neil Shah, Shirish Karande, Vineet Gandhi
Current Non-Audible Murmur (NAM)-to-speech techniques rely on voice cloning to simulate ground-truth speech from paired whispers. However, the simulated speech often lacks intellig…
MRI2Speech: Speech Synthesis from Articulatory Movements Recorded by Real-time MRI
Neil Shah, Ayan Kashyap, Shirish Karande +1
Previous real-time MRI (rtMRI)-based speech synthesis models depend heavily on noisy ground-truth speech. Applying loss directly over ground truth mel-spectrograms entangles speech…
Persuasion Games using Large Language Models
Ganesh Prasath Ramani, Shirish Karande, Santhosh V +1
Large Language Models (LLMs) have emerged as formidable instruments capable of comprehending and producing human-like text. This paper explores the potential of LLMs, to shape user…
Towards Improving NAM-to-Speech Synthesis Intelligibility using Self-Supervised Speech Models
Neil Shah, Shirish Karande, Vineet Gandhi
We propose a novel approach to significantly improve the intelligibility in the Non-Audible Murmur (NAM)-to-speech conversion task, leveraging self-supervision and sequence-to-sequ…
Towards Mitigating Perceived Unfairness in Contracts from a Non-Legal Stakeholder's Perspective
Anmol Singhal, Preethu Rose Anish, Shirish Karande +1
Commercial contracts are known to be a valuable source for deriving project-specific requirements. However, contract negotiations mainly occur among the legal counsel of the partie…
Can Physics Informed Neural Operators Self Improve?
Ritam Majumdar, Amey Varhade, Shirish Karande +1
Self-training techniques have shown remarkable value across many deep learning models and tasks. However, such techniques remain largely unexplored when considered in the context o…
HyperLoRA for PDEs
Ritam Majumdar, Vishal Jadhav, Anirudh Deodhar +3
Physics-informed neural networks (PINNs) have been widely used to develop neural surrogates for solutions of Partial Differential Equations. A drawback of PINNs is that they have t…
Synthesis of Mathematical programs from Natural Language Specifications
Ganesh Prasath, Shirish Karande
Several decision problems that are encountered in various business domains can be modeled as mathematical programs, i.e. optimization problems. The process of conducting such model…
Symbolic Regression for PDEs using Pruned Differentiable Programs
Ritam Majumdar, Vishal Jadhav, Anirudh Deodhar +3
Physics-informed Neural Networks (PINNs) have been widely used to obtain accurate neural surrogates for a system of Partial Differential Equations (PDE). One of the major limitatio…
I Know Therefore I Score: Label-Free Crafting of Scoring Functions using Constraints Based on Domain Expertise
Ragja Palakkadavath, Sarath Sivaprasad, Shirish Karande +1
Several real-life applications require crafting concise, quantitative scoring functions (also called rating systems) from measured observations. For example, an effectiveness score…
Knowledge distillation using unlabeled mismatched images
Mandar Kulkarni, Kalpesh Patil, Shirish Karande
Current approaches for Knowledge Distillation (KD) either directly use training data or sample from the training data distribution. In this paper, we demonstrate effectiveness of '…
Layer-wise training of deep networks using kernel similarity
Mandar Kulkarni, Shirish Karande
Deep learning has shown promising results in many machine learning applications. The hierarchical feature representation built by deep networks enable compact and precise encoding…
Stamp processing with examplar features
Yash Bhalgat, Mandar Kulkarni, Shirish Karande +1
Document digitization is becoming increasingly crucial. In this work, we propose a shape based approach for automatic stamp verification/detection in document images using an unsup…