Search Results for author:
Found 9 papers, 6 papers with code
A Geometric Approach to Archetypal Analysis via Sparse Projections
The work further presents the use of GAA algorithm for extended AA models such as robust and kernel AA.
On Characterizing the Evolution of Embedding Space of Neural Networks using Algebraic Topology
We study how the topology of feature embedding space changes as it passes through the layers of a well-trained deep neural network (DNN) through Betti numbers.
Towards on-Device Keyword Spotting using Low-Footprint Quaternion Neural Models
In this work, we explore Quaternion neural models as an alternative for effective acoustic modeling for the KWS task.
Ranked #1 on
Keyword Spotting
on Google Speech Commands
(Google Speech Commands V2 35 metric)
Data Encoding For Healthcare Data Democratisation and Information Leakage Prevention
Experimental evaluation highlights that models trained on encoded time-series data effectively uphold the information bottleneck principle and hence, exhibit lesser information leakage from trained models.
Coordinate descent on the orthogonal group for recurrent neural network training
We propose to use stochastic Riemannian coordinate descent on the orthogonal group for recurrent neural network training.
Activation function design for deep networks: linearity and effective initialisation
The activation function deployed in a deep neural network has great influence on the performance of the network at initialisation, which in turn has implications for training.
An Empirical Study of Derivative-Free-Optimization Algorithms for Targeted Black-Box Attacks in Deep Neural Networks
We perform a comprehensive study on the performance of derivative free optimization (DFO) algorithms for the generation of targeted black-box adversarial attacks on Deep Neural Network (DNN) classifiers assuming the perturbation energy is bounded by an $\ell_\infty$ constraint and the number of queries to the network is limited.
A Model-Based Derivative-Free Approach to Black-Box Adversarial Examples: BOBYQA
We demonstrate that model-based derivative free optimisation algorithms can generate adversarial targeted misclassification of deep networks using fewer network queries than non-model-based methods.
Conv-codes: Audio Hashing For Bird Species Classification
In this work, we propose a supervised, convex representation based audio hashing framework for bird species classification.
