5 citations · 5 across the 6 of their papers we have counts for
6 papers
Enabling Fine-Grained Operating Points for Black-Box LLMs
Ege Beyazit, KL Navaneet, Prashant Mathur +3
Black-box Large Language Models (LLMs) provide practical and accessible alternatives to other machine learning methods, as they require minimal labeled data and machine learning ex…
MoIN: Mixture of Introvert Experts to Upcycle an LLM
Ajinkya Tejankar, KL Navaneet, Ujjawal Panchal +2
The goal of this paper is to improve (upcycle) an existing large language model without the prohibitive requirements of continued pre-training of the full-model. The idea is to spl…
GeNIe: Generative Hard Negative Images Through Diffusion
Soroush Abbasi Koohpayegani, Anuj Singh, K L Navaneet +2
Data augmentation is crucial in training deep models, preventing them from overfitting to limited data. Recent advances in generative AI, e.g., diffusion models, have enabled more…
SlowFormer: Universal Adversarial Patch for Attack on Compute and Energy Efficiency of Inference Efficient Vision Transformers
KL Navaneet, Soroush Abbasi Koohpayegani, Essam Sleiman +1
Recently, there has been a lot of progress in reducing the computation of deep models at inference time. These methods can reduce both the computational needs and power usage of de…
SimReg: Regression as a Simple Yet Effective Tool for Self-supervised Knowledge Distillation
K L Navaneet, Soroush Abbasi Koohpayegani, Ajinkya Tejankar +1
Feature regression is a simple way to distill large neural network models to smaller ones. We show that with simple changes to the network architecture, regression can outperform m…
Constrained Mean Shift Using Distant Yet Related Neighbors for Representation Learning
KL Navaneet, Soroush Abbasi Koohpayegani, Ajinkya Tejankar +3
We are interested in representation learning in self-supervised, supervised, and semi-supervised settings. Some recent self-supervised learning methods like mean-shift (MSF) cluste…