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Concept

Google DeepMind

Publications associées (26)

HotelRec: a Novel Very Large-Scale Hotel Recommendation Dataset

Boi Faltings, Diego Matteo Antognini

Today, recommender systems are an inevitable part of everyone's daily digital routine and are present on most internet platforms. State-of-the-art deep learning-based models require a large number of data to achieve their best performance. Many datasets fu ...

EUROPEAN LANGUAGE RESOURCES ASSOC-ELRA2020

Optimism in the Face of Adversity: Understanding and Improving Deep Learning Through Adversarial Robustness

Pascal Frossard, Seyed Mohsen Moosavi Dezfooli, Apostolos Modas, Guillermo Ortiz Jimenez

Driven by massive amounts of data and important advances in computational resources, new deep learning systems have achieved outstanding results in a large spectrum of applications. Nevertheless, our current theoretical understanding on the mathematical fo ...

2020

Robust Reinforcement Learning via Adversarial training with Langevin Dynamics

Volkan Cevher, Ya-Ping Hsieh, Paul Thierry Yves Rolland, Kamalaruban Parameswaran

We introduce a sampling perspective to tackle the challenging task of training robust Reinforcement Learning (RL) agents. Leveraging the powerful Stochastic Gradient Langevin Dynamics, we present a novel, scalable two-player RL algorithm, which is a sampli ...

34th Conference on Neural Information Processing Systems (NeurIPS 2020)2020

Evaluation of Deep Learning Strategies for Nucleus Segmentation in Fluorescence Images

Matthieu Jean-Michel Philippe Broisin

Identifying nuclei is often a critical first step in analyzing microscopy images of cells and classical image processing algorithms are most commonly used for this task. Recent developments in deep learning can yield superior accuracy, but typical evaluati ...

2019

Design of an Always-On Deep Neural Network-Based 1-mu W Voice Activity Detector Aided With a Customized Software Model for Analog Feature Extraction

Minhao Yang

This paper presents an ultra-low-power voice activity detector (VAD). It uses analog signal processing for acoustic feature extraction (AFE) directly on the microphone output, approximate event-driven analog-to-digital conversion (ED-ADC), and digital deep ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2019

Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learning

Alexandre Massoud Alahi, Sven Kreiss, Yuejiang Liu

Mobility in an effective and socially-compliant manner is an essential yet challenging task for robots operating in crowded spaces. Recent works have shown the power of deep reinforcement learning techniques to learn socially cooperative policies. However, ...

IEEE2019