153 citations · 247 across the 10 of their papers we have counts for
10 papers
Building The Ph(ysical)AI Layer Of Machine Intelligence
Ulbert Jose Botero, Liam Smith, Brooks Olney +5
Foundation models achieve generalization through massive-scale training on diverse data, but have limitations with transfer to truly unseen domains without paired training data. We…
From Snow to Rain: Evaluating Robustness, Calibration, and Complexity of Model-Based Robust Training
Josué Martínez-Martínez, Olivia Brown, Giselle Zeno +2
Robustness to natural corruptions remains a critical challenge for reliable deep learning, particularly in safety-sensitive domains. We study a family of model-based training appro…
LoRAX: LoRA eXpandable Networks for Continual Synthetic Image Attribution
Danielle Sullivan-Pao, Nicole Tian, Pooya Khorrami
As generative AI image technologies become more widespread and advanced, there is a growing need for strong attribution models. These models are crucial for verifying the authentic…
Meta-Learning and Self-Supervised Pretraining for Real World Image Translation
Ileana Rugina, Rumen Dangovski, Mark Veillette +4
Recent advances in deep learning, in particular enabled by hardware advances and big data, have provided impressive results across a wide range of computational problems such as co…
Fast Generation for Convolutional Autoregressive Models
Prajit Ramachandran, Tom Le Paine, Pooya Khorrami +6
Convolutional autoregressive models have recently demonstrated state-of-the-art performance on a number of generation tasks. While fast, parallel training methods have been crucial…
Fast Wavenet Generation Algorithm
Tom Le Paine, Pooya Khorrami, Shiyu Chang +4
This paper presents an efficient implementation of the Wavenet generation process called Fast Wavenet. Compared to a naive implementation that has complexity O(2^L) (L denotes the…
Seq-NMS for Video Object Detection
Wei Han, Pooya Khorrami, Tom Le Paine +6
Video object detection is challenging because objects that are easily detected in one frame may be difficult to detect in another frame within the same clip. Recently, there have b…
How Deep Neural Networks Can Improve Emotion Recognition on Video Data
Pooya Khorrami, Tom Le Paine, Kevin Brady +2
We consider the task of dimensional emotion recognition on video data using deep learning. While several previous methods have shown the benefits of training temporal neural networ…
Do Deep Neural Networks Learn Facial Action Units When Doing Expression Recognition?
Pooya Khorrami, Tom Le Paine, Thomas S. Huang
Despite being the appearance-based classifier of choice in recent years, relatively few works have examined how much convolutional neural networks (CNNs) can improve performance on…
An Analysis of Unsupervised Pre-training in Light of Recent Advances
Tom Le Paine, Pooya Khorrami, Wei Han +1
Convolutional neural networks perform well on object recognition because of a number of recent advances: rectified linear units (ReLUs), data augmentation, dropout, and large label…