466 research outputs found
Recent Developments in Algorithmic Teaching
Abstract. The present paper surveys recent developments in algorith-mic teaching. First, the traditional teaching dimension model is recalled. Starting from the observation that the teaching dimension model some-times leads to counterintuitive results, recently developed approaches are presented. Here, main emphasis is put on the following aspects derived from human teaching/learning behavior: the order in which examples are presented should matter; teaching should become harder when the memory size of the learners decreases; teaching should become easier if the learners provide feedback; and it should be possible to teach infinite concepts and/or finite and infinite concept classes. Recent developments in the algorithmic teaching achieving (some) of these aspects are presented and compared.
One Deep Music Representation to Rule Them All? : A comparative analysis of different representation learning strategies
Inspired by the success of deploying deep learning in the fields of Computer
Vision and Natural Language Processing, this learning paradigm has also found
its way into the field of Music Information Retrieval. In order to benefit from
deep learning in an effective, but also efficient manner, deep transfer
learning has become a common approach. In this approach, it is possible to
reuse the output of a pre-trained neural network as the basis for a new
learning task. The underlying hypothesis is that if the initial and new
learning tasks show commonalities and are applied to the same type of input
data (e.g. music audio), the generated deep representation of the data is also
informative for the new task. Since, however, most of the networks used to
generate deep representations are trained using a single initial learning
source, their representation is unlikely to be informative for all possible
future tasks. In this paper, we present the results of our investigation of
what are the most important factors to generate deep representations for the
data and learning tasks in the music domain. We conducted this investigation
via an extensive empirical study that involves multiple learning sources, as
well as multiple deep learning architectures with varying levels of information
sharing between sources, in order to learn music representations. We then
validate these representations considering multiple target datasets for
evaluation. The results of our experiments yield several insights on how to
approach the design of methods for learning widely deployable deep data
representations in the music domain.Comment: This work has been accepted to "Neural Computing and Applications:
Special Issue on Deep Learning for Music and Audio
Dynamic Cognition Applied to Value Learning in Artificial Intelligence
Experts in Artificial Intelligence (AI) development predict that advances in the dvelopment of intelligent systems and agents will reshape vital areas in our society. Nevertheless, if such an advance isn't done with prudence, it can result in negative outcomes for humanity. For this reason, several researchers in the area are trying to develop a robust, beneficial, and safe concept of artificial intelligence. Currently, several of the open problems in the field of AI research arise from the difficulty of avoiding unwanted behaviors of intelligent agents, and at the same time specifying what we want such systems to do. It is of utmost importance that artificial intelligent agents have their values aligned with human values, given the fact that we cannot expect an AI to develop our moral preferences simply because of its intelligence, as discussed in the Orthogonality Thesis. Perhaps this difficulty comes from the way we are addressing the problem of expressing objectives, values, and ends, using representational cognitive methods. A solution to this problem would be the dynamic cognitive approach proposed by Dreyfus, whose phenomenological philosophy defends that the human experience of being-in-the-world cannot be represented by the symbolic or connectionist cognitive methods. A possible approach to this problem would be to use theoretical models such as SED (situated embodied dynamics) to address the values learning problem in AI
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