b'Misha Denil'

research

∙ 06/20/2023

RoboCat: A Self-Improving Foundation Agent for Robotic Manipulation

The ability to leverage heterogeneous robotic experience from different ...

0 Konstantinos Bousmalis, et al. ∙

research

∙ 06/16/2023

π2vec: Policy Representations with Successor Features

This paper describes π2vec, a method for representing behaviors of black...

0 Gianluca Scarpellini, et al. ∙

research

∙ 03/13/2023

Vision-Language Models as Success Detectors

Detecting successful behaviour is crucial for training intelligent agent...

7 Yuqing Du, et al. ∙

research

∙ 07/01/2021

Interactive decoding of words from visual speech recognition models

This work describes an interactive decoding method to improve the perfor...

3 Brendan Shillingford, et al. ∙

research

∙ 06/18/2021

Active Offline Policy Selection

This paper addresses the problem of policy selection in domains with abu...

12 Ksenia Konyushkova, et al. ∙

research

∙ 11/27/2020

Offline Learning from Demonstrations and Unlabeled Experience

Behavior cloning (BC) is often practical for robot learning because it a...

6 Konrad Zolna, et al. ∙

research

∙ 11/06/2020

Large-scale multilingual audio visual dubbing

We describe a system for large-scale audiovisual translation and dubbing...

3 Yi Yang, et al. ∙

research

∙ 11/01/2019

Positive-Unlabeled Reward Learning

Learning reward functions from data is a promising path towards achievin...

5 Danfei Xu, et al. ∙

research

∙ 10/02/2019

Task-Relevant Adversarial Imitation Learning

We show that a critical problem in adversarial imitation from high-dimen...

35 Konrad Zolna, et al. ∙

research

∙ 09/26/2019

A Framework for Data-Driven Robotics

We present a framework for data-driven robotics that makes use of a larg...

0 Serkan Cabi, et al. ∙

research

∙ 09/03/2019

Making Efficient Use of Demonstrations to Solve Hard Exploration Problems

This paper introduces R2D3, an agent that makes efficient use of demonst...

10 Tom Le Paine, et al. ∙

research

∙ 05/24/2018

Hyperbolic Attention Networks

We introduce hyperbolic attention networks to endow neural networks with...

0 Caglar Gulcehre, et al. ∙

research

∙ 04/17/2018

Learning Awareness Models

We consider the setting of an agent with a fixed body interacting with a...

0 Brandon Amos, et al. ∙

research

∙ 07/11/2017

The Intentional Unintentional Agent: Learning to Solve Many Continuous Control Tasks Simultaneously

This paper introduces the Intentional Unintentional (IU) agent. This age...

0 Serkan Cabi, et al. ∙

research

∙ 06/20/2017

Programmable Agents

We build deep RL agents that execute declarative programs expressed in f...

0 Misha Denil, et al. ∙

research

∙ 03/14/2017

Learned Optimizers that Scale and Generalize

Learning to learn has emerged as an important direction for achieving ar...

0 Olga Wichrowska, et al. ∙

research

∙ 11/11/2016

Learning to Learn without Gradient Descent by Gradient Descent

We learn recurrent neural network optimizers trained on simple synthetic...

0 Yutian Chen, et al. ∙

research

∙ 11/11/2016

Learning to Navigate in Complex Environments

Learning to navigate in complex environments with dynamic elements is an...

0 Piotr Mirowski, et al. ∙

research

∙ 11/06/2016

Learning to Perform Physics Experiments via Deep Reinforcement Learning

When encountering novel objects, humans are able to infer a wide range o...

0 Misha Denil, et al. ∙

research

∙ 06/14/2016

Learning to learn by gradient descent by gradient descent

The move from hand-designed features to learned features in machine lear...

0 Marcin Andrychowicz, et al. ∙

research

∙ 03/01/2016

Noisy Activation Functions

Common nonlinear activation functions used in neural networks can cause ...

0 Caglar Gulcehre, et al. ∙

research

∙ 11/18/2015

ACDC: A Structured Efficient Linear Layer

The linear layer is one of the most pervasive modules in deep learning r...

0 Marcin Moczulski, et al. ∙

research

∙ 12/22/2014

Deep Fried Convnets

The fully connected layers of a deep convolutional neural network typica...

0 Zichao Yang, et al. ∙

research

∙ 12/21/2014

Extraction of Salient Sentences from Labelled Documents

We present a hierarchical convolutional document model with an architect...

0 Misha Denil, et al. ∙

research

∙ 11/12/2014

Deep Multi-Instance Transfer Learning

We present a new approach for transferring knowledge from groups to indi...

0 Dimitrios Kotzias, et al. ∙

research

∙ 10/04/2013

Narrowing the Gap: Random Forests In Theory and In Practice

Despite widespread interest and practical use, the theoretical propertie...

0 Misha Denil, et al. ∙

research

∙ 08/29/2013

Linear and Parallel Learning of Markov Random Fields

We introduce a new embarrassingly parallel parameter learning algorithm ...

0 Yariv Dror Mizrahi, et al. ∙

research

∙ 06/03/2013

Predicting Parameters in Deep Learning

We demonstrate that there is significant redundancy in the parameterizat...

0 Misha Denil, et al. ∙

research

∙ 02/20/2013

Consistency of Online Random Forests

As a testament to their success, the theory of random forests has long b...

0 Misha Denil, et al. ∙

research

∙ 08/04/2012

Recklessly Approximate Sparse Coding

It has recently been observed that certain extremely simple feature enco...

0 Misha Denil, et al. ∙

research

∙ 09/16/2011

Learning where to Attend with Deep Architectures for Image Tracking

We discuss an attentional model for simultaneous object tracking and rec...

0 Misha Denil, et al. ∙

research

∙ 09/16/2011

A Characterization of the Combined Effects of Overlap and Imbalance on the SVM Classifier

In this paper we demonstrate that two common problems in Machine Learnin...

0 Misha Denil, et al. ∙

Misha Denil

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