Dynamic Inference with Neural Interpreters (w/ author interview)

#deeplearning #neuralinterpreter #ai

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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