195,828 research outputs found
Learning, Arts, and the Brain: The Dana Consortium Report on Arts and Cognition
Reports findings from multiple neuroscientific studies on the impact of arts training on the enhancement of other cognitive capacities, such as reading acquisition, sequence learning, geometrical reasoning, and memory
Intrinsic Motivation and Mental Replay enable Efficient Online Adaptation in Stochastic Recurrent Networks
Autonomous robots need to interact with unknown, unstructured and changing
environments, constantly facing novel challenges. Therefore, continuous online
adaptation for lifelong-learning and the need of sample-efficient mechanisms to
adapt to changes in the environment, the constraints, the tasks, or the robot
itself are crucial. In this work, we propose a novel framework for
probabilistic online motion planning with online adaptation based on a
bio-inspired stochastic recurrent neural network. By using learning signals
which mimic the intrinsic motivation signalcognitive dissonance in addition
with a mental replay strategy to intensify experiences, the stochastic
recurrent network can learn from few physical interactions and adapts to novel
environments in seconds. We evaluate our online planning and adaptation
framework on an anthropomorphic KUKA LWR arm. The rapid online adaptation is
shown by learning unknown workspace constraints sample-efficiently from few
physical interactions while following given way points.Comment: accepted in Neural Network
Better Safe Than Sorry: An Adversarial Approach to Improve Social Bot Detection
The arm race between spambots and spambot-detectors is made of several cycles
(or generations): a new wave of spambots is created (and new spam is spread),
new spambot filters are derived and old spambots mutate (or evolve) to new
species. Recently, with the diffusion of the adversarial learning approach, a
new practice is emerging: to manipulate on purpose target samples in order to
make stronger detection models. Here, we manipulate generations of Twitter
social bots, to obtain - and study - their possible future evolutions, with the
aim of eventually deriving more effective detection techniques. In detail, we
propose and experiment with a novel genetic algorithm for the synthesis of
online accounts. The algorithm allows to create synthetic evolved versions of
current state-of-the-art social bots. Results demonstrate that synthetic bots
really escape current detection techniques. However, they give all the needed
elements to improve such techniques, making possible a proactive approach for
the design of social bot detection systems.Comment: This is the pre-final version of a paper accepted @ 11th ACM
Conference on Web Science, June 30-July 3, 2019, Boston, U
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The effect of multiple knowledge sources on learning and teaching
Current paradigms for machine-based learning and teaching tend to perform their task in isolation from a rich context of existing knowledge. In contrast, the research project presented here takes the view that bringing multiple sources of knowledge to bear is of central importance to learning in complex domains. As a consequence teaching must both take advantage of and beware of interactions between new and existing knowledge. The central process which connects learning to its context is reasoning by analogy, a primary concern of this research. In teaching, the connection is provided by the explicit use of a learning model to reason about the choice of teaching actions. In this learning paradigm, new concepts are incrementally refined and integrated into a body of expertise, rather than being evaluated against a static notion of correctness. The domain chosen for this experimentation is that of learning to solve "algebra story problems." A model of acquiring problem solving skills in this domain is described, including: representational structures for background knowledge, a problem solving architecture, learning mechanisms, and the role of analogies in applying existing problem solving abilities to novel problems. Examples of learning are given for representative instances of algebra story problems. After relating our views to the psychological literature, we outline the design of a teaching system. Finally, we insist on the interdependence of learning and teaching and on the synergistic effects of conducting both research efforts in parallel
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