5 research outputs found
Learning Group Structure and Disentangled Representations of Dynamical Environments
Discovering the underlying structure of a dynamical environment involves
learning representations that are interpretable and disentangled, which is a
challenging task. In physics, interpretable representations of our universe and
its underlying dynamics are formulated in terms of representations of groups of
symmetry transformations. We propose a physics-inspired method, built upon the
theory of group representation, that learns a representation of an environment
structured around the transformations that generate its evolution.
Experimentally, we learn the structure of explicitly symmetric environments
without supervision while ensuring the interpretability of the representations.
We show that the learned representations allow for accurate long-horizon
predictions and further demonstrate a correlation between the quality of
predictions and disentanglement in the latent space.Comment: 8 pages + 2 pages of reference
Multirate digital filters, filter banks, polyphase networks, and applications: a tutorial
Multirate digital filters and filter banks find application in communications, speech processing, image compression, antenna systems, analog voice privacy systems, and in the digital audio industry. During the last several years there has been substantial progress in multirate system research. This includes design of decimation and interpolation filters, analysis/synthesis filter banks (also called quadrature mirror filters, or QMFJ, and the development of new sampling theorems. First, the basic concepts and building blocks in multirate digital signal processing (DSPJ, including the digital polyphase representation, are reviewed. Next, recent progress as reported by several authors in this area is discussed. Several applications are described, including the following: subband coding of waveforms, voice privacy systems, integral and fractional sampling rate conversion (such as in digital audio), digital crossover networks, and multirate coding of narrow-band filter coefficients. The M-band QMF bank is discussed in considerable detail, including an analysis of various errors and imperfections. Recent techniques for perfect signal reconstruction in such systems are reviewed. The connection between QMF banks and other related topics, such as block digital filtering and periodically time-varying systems, based on a pseudo-circulant matrix framework, is covered. Unconventional applications of the polyphase concept are discussed
Constraint-Based Regularization of Neural Networks
We propose a method for efficiently incorporating constraints into a
stochastic gradient Langevin framework for the training of deep neural
networks. Constraints allow direct control of the parameter space of the model.
Appropriately designed, they reduce the vanishing/exploding gradient problem,
control weight magnitudes and stabilize deep neural networks and thus improve
the robustness of training algorithms and the generalization capabilities of
the trained neural network. We present examples of constrained training methods
motivated by orthogonality preservation for weight matrices and explicit weight
normalizations. We describe the methods in the overdamped formulation of
Langevin dynamics and the underdamped form, in which momenta help to improve
sampling efficiency. The methods are explored in test examples in image
classification and natural language processing.Comment: T. Vlaar won best student paper award at OPT202