Neural Information Processing Systems http://nips.cc/

This work presents a user-centric recommendation framework, designed as a pipeline with four distinct, connected, and customizable phases. These phases are intended to improve explainability and boost user engagement. We have collected the historical Last.fm track playback records of a single user over approximately 15 years. The collected dataset includes more than 90,000 playbacks and approximately 14,000 unique tracks. From track playback records, we have created a dataset of user temporal contexts (each row is a specific moment when the user listened to certain music descriptors). As music descriptors, we have used community-contributed Last.fm tags and Spotify audio features. They represent the music that, throughout years, the user has been listening to. Next, given the most relevant Last.fm tags of a moment (e.g. the hour of the day), we predict the Spotify audio features that best fit the user preferences in that particular moment. Finally, we use the predicted audio features to find tracks similar to these features. The final aim is to recommend (and discover) tracks that the user may feel like listening to at a particular moment. For our initial study case, we have chosen to predict only a single audio feature target: danceability. The framework, however, allows to include more target variables. The ability to learn the musical habits from a single user can be quite powerful, and this framework could be extended to other users.

Solving real world problems with embedded neural networks requires both training algorithms that achieve high performance and compatible hardware that runs in real time while remaining energy efficient. For the former, deep learning using backpropagation has recently achieved a string of successes across many domains and datasets. For the latter, neuromorphic chips that run spiking neural networks have recently achieved unprecedented energy efficiency. To bring these two advances together, we must first resolve the incompatibility between backpropagation, which uses continuous-output neurons and synaptic weights, and neuromorphic designs, which employ spiking neurons and discrete synapses. Our approach is to treat spikes and discrete synapses as continuous probabilities, which allows training the network using standard backpropagation. The trained network naturally maps to neuromorphic hardware by sampling the probabilities to create one or more networks, which are merged using ensemble averaging. To demonstrate, we trained a sparsely connected network that runs on the TrueNorth chip using the MNIST dataset. With a high performance network (ensemble of 64), we achieve 99.42% accuracy at 108 µJ per image, and with a high efficiency network (ensemble of 1) we achieve 92.7% accuracy at 0.268 µJ per image.

We show that an interesting class of feed-forward neural networks can be understood as quantitative argumentation frameworks. This connection creates a bridge between research in Formal Argumentation and Machine Learning. We generalize the semantics of feed-forward neural networks to acyclic graphs and study the resulting computational and semantical properties in argumentation graphs. As it turns out, the semantics gives stronger guarantees than existing semantics that have been tailor-made for the argumentation setting. From a machine-learning perspective, the connection does not seem immediately helpful. While it gives intuitive meaning to some feed-forward-neural networks, they remain difficult to understand due to their size and density. However, the connection seems helpful for combining background knowledge in form of sparse argumentation networks with dense neural networks that have been trained for complementary purposes and for learning the parameters of quantitative argumentation frameworks in an end-to-end fashion from data.

This paper presents a novel method for information interpretability in an MC-SEFRON classifier. To develop a method to extract knowledge stored in a trained classifier, first, the binary-class SEFRON classifier developed earlier is extended to handle multi-class problems. MC-SEFRON uses the population encoding scheme to encode the real-valued input data into spike patterns. MC-SEFRON is trained using the same supervised learning rule used in the SEFRON. After training, the proposed method extracts the knowledge for a given class stored in the classifier by mapping the weighted postsynaptic potential in the time domain to the feature domain as Feature Strength Functions (FSFs). A set of FSFs corresponding to each output class represents the extracted knowledge from the classifier. This knowledge encoding method is derived to maintain consistency between the classification in the time domain and the feature domain. The correctness of the FSF is quantitatively measured by using FSF directly for classification tasks. For a given input, each FSF is sampled at the input value to obtain the corresponding feature strength value (FSV). Then the aggregated FSVs obtained for each class are used to determine the output class labels during classification. FSVs are also used to interpret the predictions during the classification task. Using ten UCI datasets and the MNIST dataset, the knowledge extraction method, interpretation and the reliability of the FSF are demonstrated. Based on the studies, it can be seen that on an average, the difference in the classification accuracies using the FSF directly and those obtained by MC-SEFRON is only around 0.9% & 0.1\% for the UCI datasets and the MNIST dataset respectively. This clearly shows that the knowledge represented by the FSFs has acceptable reliability and the interpretability of classification using the classifier's knowledge has been justified.

Weighted bipolar argumentation frameworks allow modeling decision problems and online discussions by defining arguments and their relationships. The strength of arguments can be computed based on an initial weight and the strength of attacking and supporting arguments. While previous approaches assumed an acyclic argumentation graph and successively set arguments' strength based on the strength of their parents, recently continuous dynamical systems have been proposed as an alternative. Continuous models update arguments' strength simultaneously and continuously. While there are currently no analytical guarantees for convergence in general graphs, experiments show that continuous models can converge quickly in large cyclic graphs with thousands of arguments. Here, we focus on the high-level ideas of this approach and explain key results and applications. We also introduce Attractor, a Java library that can be used to solve weighted bipolar argumentation problems. Attractor contains implementations of several discrete and continuous models and numerical algorithms to compute solutions. It also provides base classes that can be used to implement, to evaluate and to compare continuous models easily.

Weighted bipolar argumentation frameworks determine the strength of arguments based on an initial weight and the strength of their attackers and supporters. They find applications in decision support and social media analysis. Mossakowski and Neuhaus recently introduced a unification of different models and gave sufficient conditions for convergence and divergence in cyclic graphs. We build up on this work, incorporate additional models and extend results in several directions. In particular, we explain that the convergence guarantees can be seen as special cases of the contraction principle. We use this observation to unify and to generalize results and add runtime guarantees. Unfortunately, we find that guarantees obtained in this way are bought at the expense of open-mindedness, that is, the ability to move strength values away from the initial weights. However, we also demonstrate that divergence problems can be solved without giving up open-mindedness by continuizing the models. Finally, we integrate the Duality property that assures a symmetric impact of attack and support relations into the framework by Mossakowski and Neuhaus.

Causal learning is an inductive process and causal knowledge about the world is of paramount importance for intelligent systems, natural or artificial. Given an observation of events happening in the world, how does an intelligent system establish the causalities between them? The issue is further complicated by intervening noisy events. Psychologists have proposed a contingency model of causal induction but it does not incorporate computational means of addressing the issues of intervening noise to recover the causalities between events. In this paper we propose an inductive causal learning method that is able to establish causalities between events in the presence of intervening noisy events, and we apply the method to real-world data to investigate its viability. We demonstrate that the learning method works well in uncovering valid causalities, and relatively non-noisy, opportunistic situations provide the best confirmation of the causalities involved. Causal knowledge is the foundation of problem solving and the ability to learn causal knowledge enables the intelligent system to be maximally adaptive.

Solving real world problems with embedded neural networks requires both training algorithms that achieve high performance and compatible hardware that runs in real time while remaining energy efficient. For the former, deep learning using backpropagation has recently achieved a string of successes across many domains and datasets. For the latter, neuromorphic chips that run spiking neural networks have recently achieved unprecedented energy efficiency. To bring these two advances together, we must first resolve the incompatibility between backpropagation, which uses continuous-output neurons and synaptic weights, and neuromorphic designs, which employ spiking neurons and discrete synapses. Our approach is to treat spikes and discrete synapses as continuous probabilities, which allows training the network using standard backpropagation. The trained network naturally maps to neuromorphic hardware by sampling the probabilities to create one or more networks, which are merged using ensemble averaging. To demonstrate, we trained a sparsely connected network that runs on the TrueNorth chip using the MNIST dataset. With a high performance network (ensemble of $64$), we achieve $99.42\%$ accuracy at $121 \mu$J per image, and with a high efficiency network (ensemble of $1$) we achieve $92.7\%$ accuracy at $0.408 \mu$J per image.

Smart decision making at the tactical level is important for Artificial Intelligence (AI) agents to perform well in the domain of real-time strategy (RTS) games. Winning battles is crucial in RTS games, and while humans can decide when and how to attack based on their experience, it is challenging for AI agents to estimate combat outcomes accurately. A few existing models address this problem in the game of StarCraft but present many restrictions, such as not modeling injured units, supporting only a small number of unit types, or being able to predict the winner of a fight but not the remaining army. Prediction using simulations is a popular method, but generally slow and requires extensive coding to model the game engine accurately. This paper introduces a model based on Lanchester's attrition laws which addresses the mentioned limitations while being faster than running simulations. Unit strength values are learned using maximum likelihood estimation from past recorded battles. We present experiments that use a StarCraft simulator for generating battles for both training and testing, and show that the model is capable of making accurate predictions. Furthermore, we implemented our method in a StarCraft bot that uses either this or traditional simulations to decide when to attack or to retreat. We present tournament results (against top bots from 2014 AIIDE competition) comparing the performances of the two versions, and show increased winning percentages for our method.