66 citations · 163 across the 16 of their papers we have counts for
18 papers
Constant Memory Attention Block
Leo Feng, Frederick Tung, Hossein Hajimirsadeghi +2
Modern foundation model architectures rely on attention mechanisms to effectively capture context. However, these methods require linear or quadratic memory in terms of the number…
DynaShare: Task and Instance Conditioned Parameter Sharing for Multi-Task Learning
Elahe Rahimian, Golara Javadi, Frederick Tung +1
Multi-task networks rely on effective parameter sharing to achieve robust generalization across tasks. In this paper, we present a novel parameter sharing method for multi-task lea…
Memory Efficient Neural Processes via Constant Memory Attention Block
Leo Feng, Frederick Tung, Hossein Hajimirsadeghi +2
Neural Processes (NPs) are popular meta-learning methods for efficiently modelling predictive uncertainty. Recent state-of-the-art methods, however, leverage expensive attention me…
Ranking Regularization for Critical Rare Classes: Minimizing False Positives at a High True Positive Rate
Mohammadi Kiarash, Zhao He, Mengyao Zhai +1
In many real-world settings, the critical class is rare and a missed detection carries a disproportionately high cost. For example, tumors are rare and a false negative diagnosis c…
Meta Temporal Point Processes
Wonho Bae, Mohamed Osama Ahmed, Frederick Tung +1
A temporal point process (TPP) is a stochastic process where its realization is a sequence of discrete events in time. Recent work in TPPs model the process using a neural network…
Gumbel-Softmax Selective Networks
Mahmoud Salem, Mohamed Osama Ahmed, Frederick Tung +1
ML models often operate within the context of a larger system that can adapt its response when the ML model is uncertain, such as falling back on safe defaults or a human in the lo…