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Spoken ObjectNet: A Bias-Controlled Spoken Caption Dataset
(Center for Brains, Minds and Machines (CBMM), The 22nd Annual Conference of the International Speech Communication Association (Interspeech), 2021-08-30)
Visually-grounded spoken language datasets can enable models to learn cross-modal correspon- dences with very weak supervision. However, modern audio-visual datasets contain biases that un- dermine the real-world performance ...
Social Interactions as Recursive MDPs
(Center for Brains, Minds and Machines (CBMM), Conference on Robot Learning (CoRL), 2021-11-08)
While machines and robots must interact with humans, providing them with social skills has been a largely overlooked topic. This is mostly a consequence of the fact that tasks such as navigation, command following, and ...
Compositional Networks Enable Systematic Generalization for Grounded Language Understanding
(Center for Brains, Minds and Machines (CBMM), Conference on Empirical Methods in Natural Language Processing (EMNLP), 2021-11-07)
Humans are remarkably flexible when under- standing new sentences that include combinations of concepts they have never encountered before. Recent work has shown that while deep networks can mimic some human language ...
Neural Regression, Representational Similarity, Model Zoology Neural Taskonomy at Scale in Rodent Visual Cortex
(Center for Brains, Minds and Machines (CBMM), The Thirty-fifth Annual Conference on Neural Information Processing Systems (NeurIPS), 2021-12-06)
How well do deep neural networks fare as models of mouse visual cortex? A majority of research to date suggests results far more mixed than those produced in the modeling of primate visual cortex. Here, we perform a ...
From Associative Memories to Deep Networks
(Center for Brains, Minds and Machines (CBMM), 2021-01-12)
About fifty years ago, holography was proposed as a model of associative memory. Associative memories with similar properties were soon after implemented as simple networks of threshold neurons by Willshaw and Longuet-Higgins. ...
Image interpretation by iterative bottom-up top-down processing
(Center for Brains, Minds and Machines (CBMM), 2021-11-01)
Scene understanding requires the extraction and representation of scene components, such as objects and their parts, people, and places, together with their individual properties, as well as relations and interactions ...
Measuring Social Biases in Grounded Vision and Language Embeddings
(Center for Brains, Minds and Machines (CBMM), Annual Conference of the North American Chapter of the Association for Computational Linguistics (HLT/NAACL), 2021-06-06)
We generalize the notion of measuring social biases in word embeddings to visually grounded word embeddings. Biases are present in grounded embeddings, and indeed seem to be equally or more significant than for ungrounded ...
Compositional RL Agents That Follow Language Commands in Temporal Logic
(Center for Brains, Minds and Machines (CBMM), Frontiers in Robotics and AI, 2021-07-19)
We demonstrate how a reinforcement learning agent can use compositional recurrent neural net- works to learn to carry out commands specified in linear temporal logic (LTL). Our approach takes as input an LTL formula, ...
From Marr’s Vision to the Problem of Human Intelligence
(Center for Brains, Minds and Machines (CBMM), 2021-09-01)
PHASE: PHysically-grounded Abstract Social Events for Machine Social Perception
(Center for Brains, Minds and Machines (CBMM), The Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI), 2021, 2021-03-19)
The ability to perceive and reason about social interactions in the context of physical environments
is core to human social intelligence and human-machine cooperation. However, no prior dataset or
benchmark has ...