2 citations · 4 across the 5 of their papers we have counts for
5 papers
Neural Attention Memory
Hyoungwook Nam, Seung Byum Seo
We propose a novel perspective of the attention mechanism by reinventing it as a memory architecture for neural networks, namely Neural Attention Memory (NAM). NAM is a memory stru…
Defensive ML: Defending Architectural Side-channels with Adversarial Obfuscation
Hyoungwook Nam, Raghavendra Pradyumna Pothukuchi, Bo Li +2
Side-channel attacks that use machine learning (ML) for signal analysis have become prominent threats to computer security, as ML models easily find patterns in signals. To address…
Neural Sequence-to-grid Module for Learning Symbolic Rules
Segwang Kim, Hyoungwook Nam, Joonyoung Kim +1
Logical reasoning tasks over symbols, such as learning arithmetic operations and computer program evaluations, have become challenges to deep learning. In particular, even state-of…
I-BERT: Inductive Generalization of Transformer to Arbitrary Context Lengths
Hyoungwook Nam, Seung Byum Seo, Vikram Sharma Mailthody +2
Self-attention has emerged as a vital component of state-of-the-art sequence-to-sequence models for natural language processing in recent years, brought to the forefront by pre-tra…
Number Sequence Prediction Problems for Evaluating Computational Powers of Neural Networks
Hyoungwook Nam, Segwang Kim, Kyomin Jung
Inspired by number series tests to measure human intelligence, we suggest number sequence prediction tasks to assess neural network models' computational powers for solving algorit…