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Nima Tajbakhsh

4 accepted papers

2026

Star Elastic: Many-in-One Reasoning LLMs with Efficient Budget Control

Ali Taghibakhshi, Ruisi Cai, Saurav Muralidharan, Sharath Turuvekere Sreenivas, Ameya Mahabaleshwarkar, Marcin Chochowski +10
ICML 2026poster

Training a family of large language models (LLMs), either from scratch or via iterative compression, is prohibitively expensive and inefficient, requiring separate training runs for each model in the family. In this paper, we introduce Star Elastic, a novel LLM post-training method that adds N neste…

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2025

Efficient Hybrid Language Model Compression through Group-Aware SSM Pruning

Ali Taghibakhshi, Sharath Turuvekere Sreenivas, Saurav Muralidharan, Marcin Chochowski, Yashaswi Karnati, Raviraj Bhuminand Joshi +12
NeurIPS 2025poster

Hybrid language models that combine Attention and State Space Models (SSMs) have been shown to achieve state-of-the-art accuracy and runtime performance. Recent work has also demonstrated that applying pruning and distillation to Attention-only models yields smaller, more accurate models at a fracti…

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2019

Learning Fixed Points in Generative Adversarial Networks: From Image-to-Image Translation to Disease Detection and Localization

Md Mahfuzur Rahman Siddiquee, Zongwei Zhou, Nima Tajbakhsh, Ruibin Feng, Michael B. Gotway, Yoshua Bengio +1
ICCV 2019poster

Generative adversarial networks (GANs) have ushered in a revolution in image-to-image translation. The development and proliferation of GANs raises an interesting question: can we train a GAN to remove an object, if present, from an image while otherwise preserving the image? Specifically, can a GAN…

Cited by 120PDF ↗code ↗Scholar ↗
2016

Automating Carotid Intima-Media Thickness Video Interpretation With Convolutional Neural Networks

Jae Shin, Nima Tajbakhsh, R. Todd Hurst, Christopher B. Kendall, Jianming Liang
CVPR 2016poster

Cardiovascular disease (CVD) is the leading cause of mortality yet largely preventable, but the key to prevention is to identify at risk individuals before adverse events. For predicting individual CVD risk, carotid intima-media thickness (CIMT), a noninvasive ultrasound method, has proven to be val…

Cited by 82PDF ↗Scholar ↗