A solvable connectionist model of immediate recall of ordered lists

By N. Burgess. 1994

Subfields: Neuroscience, Cognitive Science & Psychology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Neuroscience

1. Tim Preece
2. Neil Burgess
3. Jitendra Malik
4. Shiguo Lian
5. Pengfei Liu
6. Yang You
7. Charles Hulme
8. Gordon D. A. Brown

In Cognitive Science & Psychology

1. Samuel J. Gershman
2. Josh Tenenbaum
3. E. Vul
4. Tim Preece
5. Neil Burgess
6. Jitendra Malik
7. Shiguo Lian
8. Pengfei Liu

Citations in the corpus

• Taskonomy: Disentangling Task Transfer Learning (2018, methodology): “That is, we labeled our dataset using the output of state-of-the-art large fullysupervised network (ResNet-151) for semantic objects (using ImageNet, 100 applicable classes), scene categories (using MIT Places [51], 63 indoor workplace and home classes in MIT Places scene hierarchy), and semantic ob”
• Scaling SGD Batch Size to 32K for ImageNet Training (2017, methodology): “The goal of DNN training is to minimize the loss function in Equation (1).”
• Vision-based robotic grasping from object localization, object pose estimation to grasp estimation for parallel grippers: a review (2020, methodology): “With the raise of Capsule Network [Hinton et al., 2011; Sabour et al., 2017; Sabour et al., 2018], some capsule-based methods are proposed.”