Dynamic Inference with Neural Interpreters (w/ author interview‪)‬ Yannic Kilcher Videos (Audio Only)

#deeplearning #neuralinterpreter #ai



This video includes an interview with the paper's authors!

What if we treated deep networks like modular programs? Neural Interpreters divide computation into small modules and route data to them via a dynamic type inference system. The resulting model combines recurrent elements, weight sharing, attention, and more to tackle both abstract reasoning, as well as computer vision tasks.



OUTLINE:

0:00 - Intro & Overview

3:00 - Model Overview

7:00 - Interpreter weights and function code

9:40 - Routing data to functions via neural type inference

14:55 - ModLin layers

18:25 - Experiments

21:35 - Interview Start

24:50 - General Model Structure

30:10 - Function code and signature

40:30 - Explaining Modulated Layers

49:50 - A closer look at weight sharing

58:30 - Experimental Results



Paper: https://arxiv.org/abs/2110.06399



Guests:

Nasim Rahaman: https://twitter.com/nasim_rahaman

Francesco Locatello: https://twitter.com/FrancescoLocat8

Waleed Gondal: https://twitter.com/Wallii_gondal



Abstract:

Modern neural network architectures can leverage large amounts of data to generalize well within the training distribution. However, they are less capable of systematic generalization to data drawn from unseen but related distributions, a feat that is hypothesized to require compositional reasoning and reuse of knowledge. In this work, we present Neural Interpreters, an architecture that factorizes inference in a self-attention network as a system of modules, which we call \emph{functions}. Inputs to the model are routed through a sequence of functions in a way that is end-to-end learned. The proposed architecture can flexibly compose computation along width and depth, and lends itself well to capacity extension after training. To demonstrate the versatility of Neural Interpreters, we evaluate it in two distinct settings: image classification and visual abstract reasoning on Raven Progressive Matrices. In the former, we show that Neural Interpreters perform on par with the vision transformer using fewer parameters, while being transferrable to a new task in a sample efficient manner. In the latter, we find that Neural Interpreters are competitive with respect to the state-of-the-art in terms of systematic generalization



Authors: Nasim Rahaman, Muhammad Waleed Gondal, Shruti Joshi, Peter Gehler, Yoshua Bengio, Francesco Locatello, Bernhard Schölkopf



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