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InterpretCC: Intrinsic User-Centric Interpretability through Global Mixture of Experts

Related publications (40)

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Light Field Synthesis Using Inexpensive Surveillance Camera Systems

We present a light field synthesis technique that achieves accurate reconstruction given a low-cost, wide-baseline camera rig. Our system integrates optical flow with methods for rectification, disparity estimation, and feature extraction, which we then fe ...

2019

A Neural Model to Predict Parameters for a Generalized Command Response Model of Intonation

Philip Neil Garner, Bastian Schnell

The Generalised Command Response (GCR) model is a time-local model of intonation that has been shown to lend itself to (cross-language) transfer of emphasis. In order to generalise the model to longer prosodic sequences, we show that it can be driven by a ...

2018

Learning Continuous Muscle Control for a Multi-joint Arm by Extending Proximal Policy Optimization with a Liquid State Machine

Marc-Oliver Gewaltig

There have been many advances in the field of reinforcement learning in continuous control problems. Usually, these approaches use deep learning with artificial neural networks for approximation of policies and value functions. In addition, there have been ...

SPRINGER INTERNATIONAL PUBLISHING AG2018

Deep Neural Networks for Multiple Speaker Detection and Localization

Jean-Marc Odobez, Petr Motlicek, Weipeng He

We propose to use neural networks for simultaneous detection and localization of multiple sound sources in human-robot interaction. In contrast to conventional signal processing techniques, neural network-based sound source localization methods require few ...

2018

Hybrid Neural Networks for Learning the Trend in Time Series

Karl Aberer, Tao Lin, Tian Guo

The trend of time series characterizes the intermediate upward and downward behaviour of time series. Learning and forecasting the trend in time series data play an important role in many real applications, ranging from resource allocation in data centers, ...

2017

Formal Guarantees on the Robustness of a Classifier against Adversarial Manipulation

Maksym Andriushchenko

Recent work has shown that state-of-the-art classifiers are quite brittle, in the sense that a small adversarial change of an originally with high confidence correctly classified input leads to a wrong classification again with high confidence. This raises ...

2017

Theory of representation learning in cortical neural networks

Carlos Stein Naves de Brito

Our brain continuously self-organizes to construct and maintain an internal representation of the world based on the information arriving through sensory stimuli. Remarkably, cortical areas related to different sensory modalities appear to share the same f ...

EPFL2016

Word Sequence Modeling using Deep Learning

Joël Yvon Roland Legrand

For a long time, natural language processing (NLP) has relied on generative models with task specific and manually engineered features. Recently, there has been a resurgence of interest for neural networks in the machine learning community, obtaining state ...

EPFL2016

Phonological vocoding using artificial neural networks

Philip Neil Garner, Milos Cernak

We investigate a vocoder based on artificial neural networks using a phonological speech representation. Speech decomposition is based on the phonological encoders, realised as neural network classifiers, that are trained for a particular language. The spe ...

Idiap2015

Handbook of Neural Computation

Many scientists and engineers now use neural networks to tackle problems that are either intractable, or unrealistically time consuming to solve, through traditional computational strategies. To address the need for speedy dissemination of new ideas in thi ...

Institute of Physics and Oxford University Press1996