Metadata
Title
Attention Mechanisms in Deep Neural Networks
Category
general
UUID
eb3b05a92e1e41c1a2b9fc2bbb3e108d
Source URL
https://wsai.iitm.ac.in/projects/attention-mechanisms-in-deep-neural-networks/
Parent URL
https://wsai.iitm.ac.in/projects/
Crawl Time
2026-03-23T19:03:45+00:00
Rendered Raw Markdown

Attention Mechanisms in Deep Neural Networks

Source: https://wsai.iitm.ac.in/projects/attention-mechanisms-in-deep-neural-networks/ Parent: https://wsai.iitm.ac.in/projects/

Attention Mechanisms in Deep Neural Networks

Investigators

Mitesh M Khapra

Tags

deep learning reinforcement learning transfer learning natural language processing

Recently the Deep Learning community has shown great interest in attention mechanisms to train neural networks – the network pays attention to only certain parts of the input or to certain parts of the network structure to learn at a given instant. However, to make this work well, we need to develop efficient algorithms for jointly learning the network parameters as well as the attention mechanism.

The main work in the proposal will be two-fold:

  1. Better algorithms for attention, possibly based on reinforcement learning; and
  2. Transfer learning using attention.

We will demonstrate the success of this approach in two domains – natural language generation; and transfer in reinforcement learning.