Search Results for author:
Found 6 papers, 3 papers with code
MM-GATBT: Enriching Multimodal Representation Using Graph Attention Network
While there have been advances in Natural Language Processing (NLP), their success is mainly gained by applying a self-attention mechanism into single or multi-modalities.
Neural Attention Memory
We propose a novel perspective of the attention mechanism by reinventing it as a memory architecture for neural networks, namely Neural Attention Memory (NAM).
Defensive ML: Defending Architectural Side-channels with Adversarial Obfuscation
To address this problem, this paper explores using Adversarial Machine Learning (AML) methods as a defense at the computer architecture layer to obfuscate side channels.
Neural Sequence-to-grid Module for Learning Symbolic Rules
Logical reasoning tasks over symbols, such as learning arithmetic operations and computer program evaluations, have become challenges to deep learning.
I-BERT: Inductive Generalization of Transformer to Arbitrary Context Lengths
The model inductively generalizes on a variety of algorithmic tasks where state-of-the-art Transformer models fail to do so.
Number Sequence Prediction Problems for Evaluating Computational Powers of Neural Networks
We define the complexity and difficulty of a number sequence prediction task with the structure of the smallest automaton that can generate the sequence.
