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Related publications (159)

Accurate deep neural network inference using computational phase-change memory

Irem Boybat Kara, Evangelos Eleftheriou, Abu Sebastian

In-memory computing using resistive memory devices is a promising non-von Neumann approach for making energy-efficient deep learning inference hardware. However, due to device variability and noise, the network needs to be trained in a specific way so that ...

2020

Fast modeling of turbulent transport in fusion plasmas using neural networks

Federico Alberto Alfredo Felici, Yann Camenen, Simon Van Mulders

We present an ultrafast neural network model, QLKNN, which predicts core tokamak transport heat and particle fluxes. QLKNN is a surrogate model based on a database of 3 × 108 flux calculations of the quasilinear gyrokinetic transport model, QuaLiKiz. The d ...

2020

Competitive Neural Layer-based Method to Identify People with High Risk for Diabetic Foot

André Anjos

Background and objective: To automatically identify patients with diabetes mellitus (DM) who have high risk of developing diabetic foot, via an unsupervised machine learning technique. Methods: We collected a new database containing 54 known risk factors f ...

2020

AL2: Progressive Activation Loss for Learning General Representations in Classification Neural Networks

Sabine Süsstrunk, Majed El Helou, Frederike Dümbgen

The large capacity of neural networks enables them to learn complex functions. To avoid overfitting, networks however require a lot of training data that can be expensive and time-consuming to collect. A common practical approach to attenuate overfitting i ...

IEEE2020

Overcoming Multi-model Forgetting

Mathieu Salzmann, Anthony Christopher Davison, Martin Jaggi, Yassine Benyahia, Claudiu-Cristian Musat, Kaicheng Yu

We identify a phenomenon, which we refer to as multi-model forgetting, that occurs when sequentially training multiple deep networks with partially-shared parameters; the performance of previously-trained models degrades as one optimizes a subsequent one, ...

JMLR2019

Audio Feature Extraction with Convolutional Neural Autoencoders with Application to Voice Conversion

Golnooshsadat Elhami

Feature extraction is a key step in many machine learning and signal processing applications. For speech signals in particular, it is important to derive features that contain both the vocal characteristics of the speaker and the content of the speech. In ...

2019

Linear Impact Generator for Automated Dataset Acquisition of Elastic Waves in Haptic Surfaces

Yves Perriard, Paolo Germano, Adrien Jean-Michel Thabuis, Camilo Hernandez Mejia

Tactile surfaces are commonly found on a big number of devices nowadays. Generally, this kind of interfaces lacks haptic feedback, which lowers the efficiency and perceived quality of human interaction. Time reversal of elastic waves has been studied and i ...

IEEE2019

Learning agile and dynamic motor skills for legged robots

Marco Hutter

Legged robots pose one of the greatest challenges in robotics. Dynamic and agile maneuvers of animals cannot be imitated by existing methods that are crafted by humans. A compelling alternative is reinforcement learning, which requires minimal craftsmanshi ...

2019

Deep Semantic Segmentation Using Nir As Extra Physical Information

Sabine Süsstrunk, Siavash Arjomand Bigdeli

Deep neural networks for semantic segmentation are most often trained with RGB color images, which encode the radiation visible to the human eyes. In this paper, we study if additional physical scene information, specifically Near-Infrared (NIR) images, im ...

IEEE2019

Deep Micro-Dictionary Learning and Coding Network

Yan Yan, Wei Wang, Wei Xiao

In this paper, we propose a novel Deep Micro-Dictionary Learning and Coding Network (DDLCN). DDLCN has most of the standard deep learning layers (pooling, fully, connected, input/output, etc.) but the main difference is that the fundamental convolutional l ...

IEEE2019

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