KL-Divergence Guided Two-Beam Viterbi Algorithm on Factorial HMMs

This thesis addresses the problem of the high computation complexity issue that arises when decoding hidden Markov models (HMMs) with a large number of states. A novel approach, the two-beam Viterbi, with an extra forward beam, for decoding HMMs is implemented on a system that uses factorial HMM to simultaneously recognize a pair of isolated digits on one audio channel. The two-beam Viterbi algorithm uses KL-divergence and hierarchical clustering to reduce the overall decoding complexity. This novel approach achieves 60% less computation compared to the baseline algorithm, the Viterbi beam search, while maintaining 82.5% recognition accuracy.Ope

On relational homomorphisms of automata

This paper investigates the concepts of relational homomorphisms and their closely associated concepts of generalized congruence relations on automata which are in general incomplete, nondeterministic, and infinite. The concept of generalized isomorphism, which is a natural extension of the isomorphism concept in dealing with nondeterministic automata, is also studied

Stable and symmetric convolutional neural network

First we present a proof that convolutional neural networks (CNNs) with max-norm regularization, max-pooling, and Relu non-linearity are stable to additive noise. Second, we explore the use of symmetric and antisymmetric filters in a baseline CNN model on digit classification, which enjoys the stability to additive noise. Experimental results indicate that the symmetric CNN outperforms the baseline model for nearly all training sizes and matches the state-of-the-art deep-net in the cases of limited training examples

Inverting Adversarially Robust Networks for Image Synthesis

Recent research in adversarially robust classifiers suggests their representations tend to be aligned with human perception, which makes them attractive for image synthesis and restoration applications. Despite favorable empirical results on a few downstream tasks, their advantages are limited to slow and sensitive optimization-based techniques. Moreover, their use on generative models remains unexplored. This work proposes the use of robust representations as a perceptual primitive for feature inversion models, and show its benefits with respect to standard non-robust image features. We empirically show that adopting robust representations as an image prior significantly improves the reconstruction accuracy of CNN-based feature inversion models. Furthermore, it allows reconstructing images at multiple scales out-of-the-box. Following these findings, we propose an encoding-decoding network based on robust representations and show its advantages for applications such as anomaly detection, style transfer and image denoising