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Deep Regression Forests for Age Estimation
(Center for Brains, Minds and Machines (CBMM), 2018-06-01)
Age estimation from facial images is typically cast as a nonlinear regression problem. The main challenge of this problem is the facial feature space w.r.t. ages is inhomogeneous, due to the large variation in facial ...
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. ...
Dreaming with ARC
(Center for Brains, Minds and Machines (CBMM), 2020-11-23)
Current machine learning algorithms are highly specialized to whatever it is they are meant to do –– e.g. playing chess, picking up objects, or object recognition. How can we extend this to a system that could solve a ...
An analysis of training and generalization errors in shallow and deep networks
(Center for Brains, Minds and Machines (CBMM), arXiv.org, 2019-05-30)
This paper is motivated by an open problem around deep networks, namely, the apparent absence of overfitting despite large over-parametrization which allows perfect fitting of the training data. In this paper, we analyze ...
Hippocampal Remapping as Hidden State Inference
(Center for Brains, Minds and Machines (CBMM), bioRxiv, 2019-08-22)
Cells in the hippocampus tuned to spatial location (place cells) typically change their tuning when an animal changes context, a phenomenon known as remapping. A fundamental challenge to understanding remapping is the fact ...
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 ...
Learning a natural-language to LTL executable semantic parser for grounded robotics
(Center for Brains, Minds and Machines (CBMM), Conference on Robot Learning (CoRL), 2020-11-16)
Children acquire their native language with apparent ease by observing how language is used in context and attempting to use it themselves. They do so without laborious annotations, negative examples, or even direct ...
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, ...
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 ...