160 citations · 168 across the 14 of their papers we have counts for
15 papers
Beyond Isolated Tasks: A Framework for Evaluating Coding Agents on Sequential Software Evolution
KN Ajay Shastry, Ganesh Senrayan, Shrey Satapara +2
Existing datasets for coding agents evaluate performance on isolated, single pull request (PR) tasks in a stateless manner, failing to capture the reality of real-world software de…
EnterpriseLab: A Full-Stack Platform for developing and deploying agents in Enterprises
Ankush Agarwal, Harsh Vishwakarma, Suraj Nagaje +1
Deploying AI agents in enterprise environments requires balancing capability with data sovereignty and cost constraints. While small language models offer privacy-preserving altern…
Can LLMs Help You at Work? A Sandbox for Evaluating LLM Agents in Enterprise Environments
Harsh Vishwakarma, Ankush Agarwal, Ojas Patil +2
Enterprise systems are crucial for enhancing productivity and decision-making among employees and customers. Integrating LLM based systems into enterprise systems enables intellige…
Adaptive LLM Routing under Budget Constraints
Pranoy Panda, Raghav Magazine, Chaitanya Devaguptapu +2
Large Language Models (LLMs) have revolutionized natural language processing, but their varying capabilities and costs pose challenges in practical applications. LLM routing addres…
Finding Needles in Images: Can Multimodal LLMs Locate Fine Details?
Parth Thakkar, Ankush Agarwal, Prasad Kasu +2
While Multi-modal Large Language Models (MLLMs) have shown impressive capabilities in document understanding tasks, their ability to locate and reason about fine-grained details wi…
Hybrid Graphs for Table-and-Text based Question Answering using LLMs
Ankush Agarwal, Ganesh S, Chaitanya Devaguptapu
Answering questions that require reasoning and aggregation across both structured (tables) and unstructured (raw text) data sources presents significant challenges. Current methods…
Towards a Training Free Approach for 3D Scene Editing
Vivek Madhavaram, Shivangana Rawat, Chaitanya Devaguptapu +2
Text driven diffusion models have shown remarkable capabilities in editing images. However, when editing 3D scenes, existing works mostly rely on training a NeRF for 3D editing. Re…
Semantic Graph Consistency: Going Beyond Patches for Regularizing Self-Supervised Vision Transformers
Chaitanya Devaguptapu, Sumukh Aithal, Shrinivas Ramasubramanian +2
Self-supervised learning (SSL) with vision transformers (ViTs) has proven effective for representation learning as demonstrated by the impressive performance on various downstream…
HOLMES: Hyper-Relational Knowledge Graphs for Multi-hop Question Answering using LLMs
Pranoy Panda, Ankush Agarwal, Chaitanya Devaguptapu +2
Given unstructured text, Large Language Models (LLMs) are adept at answering simple (single-hop) questions. However, as the complexity of the questions increase, the performance of…
Synergizing Contrastive Learning and Optimal Transport for 3D Point Cloud Domain Adaptation
Siddharth Katageri, Arkadipta De, Chaitanya Devaguptapu +3
Recently, the fundamental problem of unsupervised domain adaptation (UDA) on 3D point clouds has been motivated by a wide variety of applications in robotics, virtual reality, and…
-Patching: A Framework for Rapid Adaptation of Pre-trained Convolutional Networks without Base Performance Loss
Chaitanya Devaguptapu, Samarth Sinha, K J Joseph +2
Models pre-trained on large-scale datasets are often fine-tuned to support newer tasks and datasets that arrive over time. This process necessitates storing copies of the model ove…
Learning Modular Structures That Generalize Out-of-Distribution
Arjun Ashok, Chaitanya Devaguptapu, Vineeth Balasubramanian
Out-of-distribution (O.O.D.) generalization remains to be a key challenge for real-world machine learning systems. We describe a method for O.O.D. generalization that, through trai…
On Initial Pools for Deep Active Learning
Akshay L Chandra, Sai Vikas Desai, Chaitanya Devaguptapu +1
Active Learning (AL) techniques aim to minimize the training data required to train a model for a given task. Pool-based AL techniques start with a small initial labeled pool and t…
On Adversarial Robustness: A Neural Architecture Search perspective
Chaitanya Devaguptapu, Devansh Agarwal, Gaurav Mittal +2
Adversarial robustness of deep learning models has gained much traction in the last few years. Various attacks and defenses are proposed to improve the adversarial robustness of mo…
Borrow from Anywhere: Pseudo Multi-modal Object Detection in Thermal Imagery
Chaitanya Devaguptapu, Ninad Akolekar, Manuj M Sharma +1
Can we improve detection in the thermal domain by borrowing features from rich domains like visual RGB? In this paper, we propose a pseudo-multimodal object detector trained on nat…