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Understanding the Role of Recurrent Connections in Assembly Calculus
(Center for Brains, Minds and Machines (CBMM), 2022-07-06)
In this note, we explore the role of recurrent connections in Assembly Calculus through a number of experiments conducted on models with and without recurrent connections. We observe that as- semblies can be formed even ...
Incorporating Rich Social Interactions Into MDPs
(Center for Brains, Minds and Machines (CBMM), International Conference on Robotics and Automation (ICRA), 2022-02-07)
Much of what we do as humans is engage socially with other agents, a skill that robots must also eventually possess. We demonstrate that a rich theory of social interactions originating from microso- ciology and economics ...
System identification of neural systems: If we got it right, would we know?
(Center for Brains, Minds and Machines (CBMM), 2022-07-02)
Various artificial neural networks developed by engineers have been evaluated as models of the brain, such as the ventral stream in the primate visual cortex. After being trained on large datasets, the network outputs are ...
PCA as a defense against some adversaries
(Center for Brains, Minds and Machines (CBMM), 2022-03-30)
Neural network classifiers are known to be highly vulnerable to adversarial perturbations in their inputs. Under the hypothesis that adversarial examples lie outside of the sub-manifold of natural images, previous work has ...
Trajectory Prediction with Linguistic Representations
(Center for Brains, Minds and Machines (CBMM), International Conference on Robotics and Automation (ICRA), 2022-03-09)
Language allows humans to build mental models that interpret what is happening around them resulting in more accurate long-term predictions. We present a novel trajectory prediction model that uses linguistic intermediate ...
Transformer Module Networks for Systematic Generalization in Visual Question Answering
(Center for Brains, Minds and Machines (CBMM), 2022-02-03)
Transformer-based models achieve great performance on Visual Question Answering (VQA). How- ever, when we evaluate them on systematic generalization, i.e., handling novel combinations of known concepts, their performance ...
Three approaches to facilitate DNN generalization to objects in out-of-distribution orientations and illuminations
(Center for Brains, Minds and Machines (CBMM), 2022-01-26)
The training data distribution is often biased towards objects in certain orientations and illumination conditions. While humans have a remarkable capability of recognizing objects in out-of-distribution (OoD) orientations ...
SGD Noise and Implicit Low-Rank Bias in Deep Neural Networks
(Center for Brains, Minds and Machines (CBMM), 2022-03-28)
We analyze deep ReLU neural networks trained with mini-batch stochastic gradient decent and weight decay. We prove that the source of the SGD noise is an implicit low rank constraint across all of the weight matrices within ...
Compositional Sparsity: a framework for ML
(Center for Brains, Minds and Machines (CBMM), 2022-10-10)
The main claim of this perspective is that compositional sparsity of the target function, which corre- sponds to the task to be learned, is the key principle underlying machine learning. I prove that under restrictions of ...