31,835 research outputs found
Universal generalization and universal inter-item confusability
We argue that confusability between items should be distinguished from generalization between items. Shepard's data concern confusability, but the theories proposed by Shepard and by Tenenbaum & Griffiths concern generalization, indicating a gap between theory and data. We consider the empirical and theoretical work involved in bridging this gap
Learning Social Relation Traits from Face Images
Social relation defines the association, e.g, warm, friendliness, and
dominance, between two or more people. Motivated by psychological studies, we
investigate if such fine-grained and high-level relation traits can be
characterised and quantified from face images in the wild. To address this
challenging problem we propose a deep model that learns a rich face
representation to capture gender, expression, head pose, and age-related
attributes, and then performs pairwise-face reasoning for relation prediction.
To learn from heterogeneous attribute sources, we formulate a new network
architecture with a bridging layer to leverage the inherent correspondences
among these datasets. It can also cope with missing target attribute labels.
Extensive experiments show that our approach is effective for fine-grained
social relation learning in images and videos.Comment: To appear in International Conference on Computer Vision (ICCV) 201
MBMF: Model-Based Priors for Model-Free Reinforcement Learning
Reinforcement Learning is divided in two main paradigms: model-free and
model-based. Each of these two paradigms has strengths and limitations, and has
been successfully applied to real world domains that are appropriate to its
corresponding strengths. In this paper, we present a new approach aimed at
bridging the gap between these two paradigms. We aim to take the best of the
two paradigms and combine them in an approach that is at the same time
data-efficient and cost-savvy. We do so by learning a probabilistic dynamics
model and leveraging it as a prior for the intertwined model-free optimization.
As a result, our approach can exploit the generality and structure of the
dynamics model, but is also capable of ignoring its inevitable inaccuracies, by
directly incorporating the evidence provided by the direct observation of the
cost. Preliminary results demonstrate that our approach outperforms purely
model-based and model-free approaches, as well as the approach of simply
switching from a model-based to a model-free setting.Comment: After we submitted the paper for consideration in CoRL 2017 we found
a paper published in the recent past with a similar method (see related work
for a discussion). Considering the similarities between the two papers, we
have decided to retract our paper from CoRL 201
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