7 citations · 16 across the 6 of their papers we have counts for
6 papers
Coverage-Driven KV Cache Eviction for Efficient and Improved Inference of LLM
Shuvendu Roy, Mengyao Zhai, Hossein Hajimirsadeghi +1
Large language models (LLMs) excel at complex tasks like question answering and summarization, thanks to their ability to handle long-context inputs. However, deploying LLMs is cos…
You Need Reasoning to Learn Reasoning: The Limitations of Label-Free RL in Weak Base Models
Shuvendu Roy, Hossein Hajimirsadeghi, Mengyao Zhai +1
Recent advances in large language models have demonstrated the promise of unsupervised reinforcement learning (RL) methods for enhancing reasoning capabilities without external sup…
ReLU Strikes Back: Exploiting Activation Sparsity in Large Language Models
Iman Mirzadeh, Keivan Alizadeh, Sachin Mehta +5
Large Language Models (LLMs) with billions of parameters have drastically transformed AI applications. However, their demanding computation during inference has raised significant…
NeuMan: Neural Human Radiance Field from a Single Video
Wei Jiang, Kwang Moo Yi, Golnoosh Samei +2
Photorealistic rendering and reposing of humans is important for enabling augmented reality experiences. We propose a novel framework to reconstruct the human and the scene that ca…
Automatic Segmentation of the Prostate on 3D Trans-rectal Ultrasound Images using Statistical Shape Models and Convolutional Neural Networks
Golnoosh Samei, Davood Karimi, Claudia Kesch +1
In this work we propose to segment the prostate on a challenging dataset of trans-rectal ultrasound (TRUS) images using convolutional neural networks (CNNs) and statistical shape m…
A deep learning-based method for prostate segmentation in T2-weighted magnetic resonance imaging
Davood Karimi, Golnoosh Samei, Yanan Shao +1
We propose a novel automatic method for accurate segmentation of the prostate in T2-weighted magnetic resonance imaging (MRI). Our method is based on convolutional neural networks…