Collaborating Authors

Building your First Neural Network on a Structured Dataset (using Keras)


Have you ever applied a neural network model on a structured dataset? If the answer is no, which of the following reasons are applicable for you? In this article, I will focus on the first three reasons and showcase how easily you can apply a neural network model on a structured dataset using a popular high-level library - "keras". We will work on the Black Friday dataset in this article. It is a regression challenge where we need to predict the purchase amount of a customer against various products.

Hybrid Neural Models For Sequence Modelling: The Best Of Three Worlds Machine Learning

We propose a neural architecture with the main characteristics of the most successful neural models of the last years: bidirectional RNNs, encoder-decoder, and the Transformer model. Evaluation on three sequence labelling tasks yields results that are close to the state-of-the-art for all tasks and better than it for some of them, showing the pertinence of this hybrid architecture for this kind of tasks.

Deep Neural Network Fingerprinting by Conferrable Adversarial Examples


In Machine Learning as a Service, a provider trains a deep neural network and provides many users access to it. However, the hosted (source) model is susceptible to model stealing attacks where an adversary derives a surrogate model from API access to the source model. For post hoc detection of such attacks, the provider needs a robust method to determine whether a suspect model is a surrogate of their model or not. We propose a fingerprinting method for deep neural networks that extracts a set of inputs from the source model so that only surrogates agree with the source model on the classification of such inputs. These inputs are a specifically crafted subclass of targeted transferable adversarial examples which we call conferrable adversarial examples that transfer exclusively from a source model to its surrogates.

On Multiplicative Integration with Recurrent Neural Networks

Neural Information Processing Systems

We introduce a general simple structural design called "Multiplicative Integration" (MI) to improve recurrent neural networks (RNNs). MI changes the way of how the information flow gets integrated in the computational building block of an RNN, while introducing almost no extra parameters. The new structure can be easily embedded into many popular RNN models, including LSTMs and GRUs. We empirically analyze its learning behaviour and conduct evaluations on several tasks using different RNN models. Our experimental results demonstrate that Multiplicative Integration can provide a substantial performance boost over many of the existing RNN models.