13 citations · 23 across the 7 of their papers we have counts for
8 papers
REALISTA: Realistic Latent Adversarial Attacks that Elicit LLM Hallucinations
Buyun Liang, Jinqi Luo, Liangzu Peng +6
Large language models (LLMs) achieve strong performance across many tasks but remain vulnerable to hallucinations, making it important to systematically evaluate their reliability…
Transformers with Joint Tokens and Local-Global Attention for Efficient Human Pose Estimation
Kaleab A. Kinfu, René Vidal
Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) have led to significant progress in 2D body pose estimation. However, achieving a good balance between accuracy,…
Computerized Assessment of Motor Imitation for Distinguishing Autism in Video (CAMI-2DNet)
Kaleab A. Kinfu, Carolina Pacheco, Alice D. Sperry +4
Motor imitation impairments are commonly reported in individuals with autism spectrum conditions (ASCs), suggesting that motor imitation could be used as a phenotype for addressing…
Efficient Vision Transformer for Human Pose Estimation via Patch Selection
Kaleab A. Kinfu, Rene Vidal
While Convolutional Neural Networks (CNNs) have been widely successful in 2D human pose estimation, Vision Transformers (ViTs) have emerged as a promising alternative to CNNs, boos…
Hybrid Facial Expression Recognition (FER2013) Model for Real-Time Emotion Classification and Prediction
Ozioma Collins Oguine, Kanyifeechukwu Jane Oguine, Hashim Ibrahim Bisallah +1
Facial Expression Recognition is a vital research topic in most fields ranging from artificial intelligence and gaming to Human-Computer Interaction (HCI) and Psychology. This pape…
Analysis and Extensions of Adversarial Training for Video Classification
Kaleab A. Kinfu, René Vidal
Adversarial training (AT) is a simple yet effective defense against adversarial attacks to image classification systems, which is based on augmenting the training set with attacks…
Prospective Learning: Principled Extrapolation to the Future
Ashwin De Silva, Rahul Ramesh, Lyle Ungar +40
Learning is a process which can update decision rules, based on past experience, such that future performance improves. Traditionally, machine learning is often evaluated under the…
When are Deep Networks really better than Decision Forests at small sample sizes, and how?
Haoyin Xu, Kaleab A. Kinfu, Will LeVine +9
Deep networks and decision forests (such as random forests and gradient boosted trees) are the leading machine learning methods for structured and tabular data, respectively. Many…