Journal Club

Here we meet to discuss and talk about the latest ongoing research in this field. This is conducted weekly on Friday night of 2 hours long duration. Where interested students present a short talk on some latest articles which they found to be interesting to share. This helps significantly in developing and maintaining curiosity and in spreading the latest research among the campus.

Follow up below for the latest posts related to Journal Club Meeting.

Interested in delivering a presentation/ talk on next Journal Club meeting? Submit your proposal here.

Are ANNs capable of Few-Shot Learning?

January 9, 2021

This talk will involve Brendan Lake's paper, as well as other related literature. The speaker shall then discuss the studies he conducted with his mentor, Shashikant Gupta, which provides a completely deep-learning based approach to tackle the Omniglot dataset, and the progress they have achieved with the project.

Does The Brain Do Back Propagation?

December 20, 2020

Here we build on past and recent developments to argue whether an algorithm similar to backpropagation enables learning in the brain and modulates synaptic plasticity.

More Than More Data

November 15, 2020

A theoretical and empirical evidence that data augmentation alone is more effective than commonly used explicit regularisation techniques, and how it can be used to incorporate insights from computational neuroscience as new learning objectives that yield better performance and more robust visual representations.

Codistillation for Distributed Training

October 27, 2020

This work contributes to a better understanding of codistillation and how to best take advantage of it in a distributed computing environment.

Investigating Inductive Biases in Humans and Machines

October 3, 2020

Demonstration of how to reformulate recent algorithms for meta-learning as methods for statistical inference in a hierarchical Bayesian model.

InfoTabS - Inference on Tables as Semi-Structured Data

September 20, 2020

Understanding ubiquitous semi-structured tabulated data requires not only comprehending the meaning of text fragments, but also implicit relationships between them. We argue that such data can prove as a testing ground for understanding how we reason about information.

Neural Constraints on Learning

September 13, 2020

As some behaviours are easier to learn than others, we asked if some neural activity patterns are easier to generate than others

Deep Reinforcement Learning And Its Neuroscientific Implications

August 30, 2020

What if a machine could mimic the way a human learns? Sounds pretty ambitious right? Reinforcement Learning is one of the three paradigms of Machine Learning that can be used for this purpose.

A Network Model of the Emotional Brain

March 5, 2020

Where does emotion reside in the brain? The key question addressed here is as follows - how is emotion instantiated in the brain? Thinking about the brain basis of emotion has fluctuated between a focus on regions and a focus on circuits

Journal Club Meeting 1

March 1, 2020

Presentations on The Role of Reward Dimensions in Preference Reversal by Shobhit Jagga and IVSN - A Biologically Inspired Computational Model for Visual Search by Shashi Kant Gupta