b'Dumitru Erhan'

research

∙ 08/22/2023

StoryBench: A Multifaceted Benchmark for Continuous Story Visualization

Generating video stories from text prompts is a complex task. In additio...

0 Emanuele Bugliarello, et al. ∙

research

∙ 10/05/2022

Phenaki: Variable Length Video Generation From Open Domain Textual Description

We present Phenaki, a model capable of realistic video synthesis, given ...

11 Ruben Villegas, et al. ∙

research

∙ 04/18/2022

INFOrmation Prioritization through EmPOWERment in Visual Model-Based RL

Model-based reinforcement learning (RL) algorithms designed for handling...

0 Homanga Bharadhwaj, et al. ∙

research

∙ 06/24/2021

FitVid: Overfitting in Pixel-Level Video Prediction

An agent that is capable of predicting what happens next can perform a v...

6 Mohammad Babaeizadeh, et al. ∙

research

∙ 12/08/2020

Models, Pixels, and Rewards: Evaluating Design Trade-offs in Visual Model-Based Reinforcement Learning

Model-based reinforcement learning (MBRL) methods have shown strong samp...

7 Mohammad Babaeizadeh, et al. ∙

research

∙ 05/08/2020

SurfelGAN: Synthesizing Realistic Sensor Data for Autonomous Driving

Autonomous driving system development is critically dependent on the abi...

3 Zhenpei Yang, et al. ∙

research

∙ 11/05/2019

High Fidelity Video Prediction with Large Stochastic Recurrent Neural Networks

Predicting future video frames is extremely challenging, as there are ma...

37 Ruben Villegas, et al. ∙

research

∙ 03/04/2019

VideoFlow: A Flow-Based Generative Model for Video

Generative models that can model and predict sequences of future events ...

6 Manoj Kumar, et al. ∙

research

∙ 03/01/2019

Model-Based Reinforcement Learning for Atari

Model-free reinforcement learning (RL) can be used to learn effective po...

20 Łukasz Kaiser, et al. ∙

research

∙ 06/28/2018

Evaluating Feature Importance Estimates

Estimating the influence of a given feature to a model prediction is cha...

4 Sara Hooker, et al. ∙

research

∙ 06/12/2018

Hierarchical Long-term Video Prediction without Supervision

Much of recent research has been devoted to video prediction and generat...

10 Nevan Wichers, et al. ∙

research

∙ 11/02/2017

The (Un)reliability of saliency methods

Saliency methods aim to explain the predictions of deep neural networks....

0 Pieter-Jan Kindermans, et al. ∙

research

∙ 10/30/2017

Stochastic Variational Video Prediction

Predicting the future in real-world settings, particularly from raw sens...

0 Mohammad Babaeizadeh, et al. ∙

research

∙ 05/16/2017

Learning how to explain neural networks: PatternNet and PatternAttribution

DeConvNet, Guided BackProp, LRP, were invented to better understand deep...

0 Pieter-Jan Kindermans, et al. ∙

research

∙ 12/16/2016

Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks

Collecting well-annotated image datasets to train modern machine learnin...

0 Konstantinos Bousmalis, et al. ∙

research

∙ 09/21/2016

Show and Tell: Lessons learned from the 2015 MSCOCO Image Captioning Challenge

Automatically describing the content of an image is a fundamental proble...

0 Oriol Vinyals, et al. ∙

research

∙ 08/22/2016

Domain Separation Networks

The cost of large scale data collection and annotation often makes the a...

0 Konstantinos Bousmalis, et al. ∙

research

∙ 05/09/2016

Theano: A Python framework for fast computation of mathematical expressions

Theano is a Python library that allows to define, optimize, and evaluate...

0 The Theano Development Team, et al. ∙

research

∙ 12/08/2015

SSD: Single Shot MultiBox Detector

We present a method for detecting objects in images using a single deep ...

0 Wei Liu, et al. ∙

research

∙ 03/10/2015

Doubly Robust Policy Evaluation and Optimization

We study sequential decision making in environments where rewards are on...

0 Miroslav Dudík, et al. ∙

research

∙ 12/20/2014

Training Deep Neural Networks on Noisy Labels with Bootstrapping

Current state-of-the-art deep learning systems for visual object recogni...

0 Scott Reed, et al. ∙

research

∙ 12/03/2014

Scalable, High-Quality Object Detection

Current high-quality object detection approaches use the scheme of salie...

0 Christian Szegedy, et al. ∙

research

∙ 11/17/2014

Show and Tell: A Neural Image Caption Generator

Automatically describing the content of an image is a fundamental proble...

0 Oriol Vinyals, et al. ∙

research

∙ 09/17/2014

Going Deeper with Convolutions

We propose a deep convolutional neural network architecture codenamed "I...

0 Christian Szegedy, et al. ∙

research

∙ 12/21/2013

Intriguing properties of neural networks

Deep neural networks are highly expressive models that have recently ach...

0 Christian Szegedy, et al. ∙

research

∙ 12/19/2013

Using Web Co-occurrence Statistics for Improving Image Categorization

Object recognition and localization are important tasks in computer visi...

0 Samy Bengio, et al. ∙

research

∙ 12/08/2013

Scalable Object Detection using Deep Neural Networks

Deep convolutional neural networks have recently achieved state-of-the-a...

0 Dumitru Erhan, et al. ∙

research

∙ 07/01/2013

Challenges in Representation Learning: A report on three machine learning contests

The ICML 2013 Workshop on Challenges in Representation Learning focused ...

0 Ian J. Goodfellow, et al. ∙

research

∙ 10/16/2012

Sample-efficient Nonstationary Policy Evaluation for Contextual Bandits

We present and prove properties of a new offline policy evaluator for an...

0 Miroslav Dudík, et al. ∙

research

∙ 09/18/2010

Deep Self-Taught Learning for Handwritten Character Recognition

Recent theoretical and empirical work in statistical machine learning ha...

0 Frédéric Bastien, et al. ∙

Dumitru Erhan

Featured Co-authors