This paper introduces the first system performing automatic orchestration from a real-time piano input. We cast this problem as a case of projective orchestration, where the goal is to learn the underlying regularities existing between piano scores and their orchestrations by well-known composers, in order to later perform this task automatically on novel piano inputs. To that end, we investiga...

We present a faithful hardware implementation of the Boltzmann machine. The prototype performs 505 megasynapses (millionof additionsand multiplications)per second, using 16-bit fixed-point weights. It can emulate fully connected instances of the Boltzmann machine containing up to 1438 variables. Our specialized hardware only executes the simplest part of the Boltzmann machine algorithm, namely ...

Fully-observable high-order Boltzmann Machines are capable of identifying explicit highorder feature interactions theoretically. However, they have never been used in practice due to their prohibitively high computational cost for inference and learning. In this paper, we propose an efficient approach for learning a fully-observable high-order Boltzmann Machine based on sparse learning and cont...

In this paper, a new energy-based probabilistic model, called CAB (Cluster Adaptive restricted Boltzmann machine), is proposed for voice conversion (VC) that does not require parallel data during the training and requires only a small amount of speech data during the adaptation. Most of the existing VC methods require parallel data for training. Recently, VC methods that do not require parallel...

The Restricted Boltzmann Machine (RBM) has proved to be a powerful tool in machine learning, both on its own and as the building block for Deep Belief Networks (multi-layer generative graphical models). The RBM and Deep Belief Network have been shown to be universal approximators for probability distributions on binary vectors. In this paper we prove several similar universal approximation resu...

Recent research has seen the proposal of several new inductive principles designed specifically to avoid the problems associated with maximum likelihood learning in models with intractable partition functions. In this paper, we study learning methods for binary restricted Boltzmann machines (RBMs) based on ratio matching and generalized score matching. We compare these new RBM learning methods ...

This paper proposes a continuous stochastic generative model that offers an improved ability to model analogue data, with a simple and reliable learning algorithm. The architecture forms a continuous restricted Boltzmann Machine, with a novel learning algorithm. The capabilities of the model are demonstrated with both artificial and real data.

The Boltzmann–Gibbs distribution is currently widely used in economic modeling. One of the applications is integrated with the DSGE (Dynamic Stochastic General Equilibrium) model. However, a question that arises concerns whether the Boltzmann–Gibbs distribution can be directly applied, without considering the underlying social network structure more seriously, even though the social network str...

in this paper, we show how to obtain suitable differential charactristics for block ciphers with neural networks. we represent the operations of a block cipher, regarding their differential characteristics, through a directed weighted graph. in this way, the problem of finding the best differential characteristic for a block cipher reduces to the problem of finding the minimum-weight multi-path...