Home      Discussions      About
Join us
Latest Conferences Trending
ICLR
NIPS
ICML
CVPR
ICCV
ECCV
ICRA
MICCAI
IROS
IJRR
RLDM
ICLR 2019
Nov 10, 2014
By rkiros
Machine Learning
Computation and Language
Computer Vision

Unifying Visual-Semantic Embeddings with Multimodal Neural Language...

Inspired by recent advances in multimodal learning and machine translation, we introduce an encoder-decoder pipeline that learns (a): a multimodal ...
Sep 27, 2017
Computer Vision
Computation and Language

A Read-Write Memory Network for Movie Story Understanding

We propose a novel memory network model named Read-Write Memory Network (RWMN) to perform question and answering tasks for large-scale, multimodal ...
Dec 01, 2016
Artificial Intelligence
Machine Learning

While this paper is not the first to explore learning a physics simulator, here we take the opportunity to highlight the value of this paper’s contributions. We hope these contributions can seed further research that builds on the NPE framework th...

A Compositional Object-Based Approach to Learning Physical Dynamics

We present the Neural Physics Engine (NPE), a framework for learning simulators of intuitive physics that naturally generalize across variable obje...
Dec 08, 2016
By poole
Machine Learning

Improved generator objectives for GANs

We present a framework to understand GAN training as alternating density ratio estimation and approximate divergence minimization. This provides an...
Nov 23, 2016
Computer Vision

PoseTrack: Joint Multi-Person Pose Estimation and Tracking

In this work, we introduce the challenging problem of joint multi-person pose estimation and tracking of an unknown number of persons in unconstrai...
Dec 19, 2016
Computer Vision

Large-Scale Image Retrieval with Attentive Deep Local Features

We propose an attentive local feature descriptor suitable for large-scale image retrieval, referred to as DELF (DEep Local Feature). The new featur...
Jun 14, 2017
Machine Learning

Empirical Analysis of the Hessian of Over-Parametrized Neural Networks

We study the properties of common loss surfaces through their Hessian matrix. In particular, in the context of deep learning, we empirically show t...
Mar 22, 2018
Artificial Intelligence
Machine Learning

Deep Reinforcement Learning with Model Learning and Monte Carlo Tre...

Deep reinforcement learning has been successfully applied to several visual-input tasks using model-free methods. In this paper, we propose a model...
Jul 12, 2018
By vpihur
Conferences: test
Cryptography and Security
Machine Learning

Differentially-Private "Draw and Discard" Machine Learning

In this work, we propose a novel framework for privacy-preserving client-distributed machine learning. It is motivated by the desire to achieve dif...
Jul 26, 2017
Machine Learning

General Latent Feature Modeling for Data Exploration Tasks

This paper introduces a general Bayesian non- parametric latent feature model suitable to per- form automatic exploratory analysis of heterogeneous...
Jun 02, 2016
Machine Learning

Differentially Private Gaussian Processes

A major challenge for machine learning is increasing the availability of data while respecting the privacy of individuals. Here we combine the prov...
May 18, 2017
Computer Vision

Re3 : Real-Time Recurrent Regression Networks for Visual Tracking o...

Robust object tracking requires knowledge and understanding of the object being tracked: its appearance, its motion, and how it changes over time. ...
Mar 07, 2016
Computer Vision

Learning a Discriminative Null Space for Person Re-identification

Most existing person re-identification (re-id) methods focus on learning the optimal distance metrics across camera views. Typically a person's app...
Jan 08, 2016
By son
Computer Vision
Robotics

Learning to Remove Multipath Distortions in Time-of-Flight Range Im...

Range images captured by Time-of-Flight (ToF) cameras are corrupted with multipath distortions due to interaction between modulated light signals a...
Jun 22, 2015
Computation and Language
Machine Learning
Neural and Evolutionary Computation

A Deep Memory-based Architecture for Sequence-to-Sequence Learning

We propose DEEPMEMORY, a novel deep architecture for sequence-to-sequence learning, which performs the task through a series of nonlinear transform...
Aug 09, 2016
Computer Vision

Convolutional Oriented Boundaries

We present Convolutional Oriented Boundaries (COB), which produces multiscale oriented contours and region hierarchies starting from generic image ...
May 22, 2017
By mathieu
Machine Learning

Factorization machines and polynomial networks are supervised polynomial models based on an efficient low-rank decomposition. We extend these models to the multi-output setting, i.e., for learning vector-valued functions, with application to multi...

Multi-output Polynomial Networks and Factorization Machines

Factorization machines and polynomial networks are supervised polynomial models based on an efficient low-rank decomposition. We extend these model...
Apr 07, 2016
Computer Vision

GIFT: A Real-time and Scalable 3D Shape Search Engine

Projective analysis is an important solution for 3D shape retrieval, since human visual perceptions of 3D shapes rely on various 2D observations fr...
Oct 19, 2016
Machine Learning

Meta-learning consists in learning learning algorithms. We use a Long Short Term Memory (LSTM) based network to learn to compute on-line updates of the parameters of another neural network. These parameters are stored in the cell state of the LSTM...

Learning to Learn Neural Networks

Meta-learning consists in learning learning algorithms. We use a Long Short Term Memory (LSTM) based network to learn to compute on-line updates of...
Oct 07, 2016
Robotics

Safe and Robust Robot Maneuvers Based on Reach Control

In this paper, we investigate the synthesis of piecewise affine feedback controllers to address the problem of safe and robust controller design in...
There aren't more projects

Every arXiv paper needs to be discussed

Request Comment

or Invite via email
You are asking your first question!
How to quickly get a good answer:
  • Keep your question short and to the point
  • Check for grammar or spelling errors.
  • Phrase it like a question
Test
Test description