Chelsea Finn Github

All stories published by Towards Data Science on September 25, 2018

All stories published by Towards Data Science on September 25, 2018

PR-153: SNAIL: A Simple Neural Attentive Meta-Learner

PR-153: SNAIL: A Simple Neural Attentive Meta-Learner

Learning Reward Functions by Integrating Human Demonstrations and

Learning Reward Functions by Integrating Human Demonstrations and

Model-Ensemble Trust-Region Policy Optimization – arXiv Vanity

Model-Ensemble Trust-Region Policy Optimization – arXiv Vanity

Chelsea Finn on Twitter:

Chelsea Finn on Twitter: "We introduce PEARL, a new meta

Alexis Cook (@alexis_b_cook) | Twitter

Alexis Cook (@alexis_b_cook) | Twitter

abhiksingla (Abhik Singla) / Following · GitHub

abhiksingla (Abhik Singla) / Following · GitHub

Learning Visual Feature Spaces for Robotic Manipulation with Deep

Learning Visual Feature Spaces for Robotic Manipulation with Deep

DISCRIMINATOR-ACTOR-CRITIC: ADDRESSING SAMPLE INEFFICIENCY AND

DISCRIMINATOR-ACTOR-CRITIC: ADDRESSING SAMPLE INEFFICIENCY AND

DR 1 3: Sensing, mapping and low-level memory III

DR 1 3: Sensing, mapping and low-level memory III

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

CAML: Fast Context Adaptation via Meta-Learning

CAML: Fast Context Adaptation via Meta-Learning

Top 5 Things To Remember To Get That Elusive Job Offer - | Alldus

Top 5 Things To Remember To Get That Elusive Job Offer - | Alldus

Deep visual foresight for planning robot motion | Chelsea Finn

Deep visual foresight for planning robot motion | Chelsea Finn

Papers With Code : Probabilistic Model-Agnostic Meta-Learning

Papers With Code : Probabilistic Model-Agnostic Meta-Learning

Profillic: AI research & source code to supercharge your projects

Profillic: AI research & source code to supercharge your projects

PDF] Gaussian Prototypical Networks for Few-Shot Learning on

PDF] Gaussian Prototypical Networks for Few-Shot Learning on

D] Machine Learning - WAYR (What Are You Reading) - Week 63

D] Machine Learning - WAYR (What Are You Reading) - Week 63

Unsupervised Control Through Non-Parametric Discriminative Rewards

Unsupervised Control Through Non-Parametric Discriminative Rewards

An Atari Model Zoo for Analyzing, Visualizing, and Comparing Deep

An Atari Model Zoo for Analyzing, Visualizing, and Comparing Deep

Introduction to Neural Networks and Brief Tutorial with Caffe 10th

Introduction to Neural Networks and Brief Tutorial with Caffe 10th

mari-linhares (Marianne Linhares Monteiro) / Following · GitHub

mari-linhares (Marianne Linhares Monteiro) / Following · GitHub

Learning to Compare: Relation Network for Few-Shot Learning

Learning to Compare: Relation Network for Few-Shot Learning

ICRA 2019 Program | Monday May 20, 2019

ICRA 2019 Program | Monday May 20, 2019

Chelsea Finn on Twitter:

Chelsea Finn on Twitter: "We introduce PEARL, a new meta

Videos matching Ilya Sutskever - Meta Learning %26amp

Videos matching Ilya Sutskever - Meta Learning %26amp

Building Generalizable Agents with a Realistic and Rich 3D

Building Generalizable Agents with a Realistic and Rich 3D

Profillic: AI research & source code to supercharge your projects

Profillic: AI research & source code to supercharge your projects

Chelsea Finn on Twitter:

Chelsea Finn on Twitter: "Students at MILA released a clean pytorch

A General Method for Amortizing Variational Filtering

A General Method for Amortizing Variational Filtering

DISCRIMINATOR-ACTOR-CRITIC: ADDRESSING SAMPLE INEFFICIENCY AND

DISCRIMINATOR-ACTOR-CRITIC: ADDRESSING SAMPLE INEFFICIENCY AND

Deep visual foresight for planning robot motion | Chelsea Finn

Deep visual foresight for planning robot motion | Chelsea Finn

Papers With Code : Model-Based Reinforcement Learning for Atari

Papers With Code : Model-Based Reinforcement Learning for Atari

Supplementary material for Learning to Adapt for Stereo

Supplementary material for Learning to Adapt for Stereo

Skymind | A Beginner's Guide to Deep Reinforcement Learning

Skymind | A Beginner's Guide to Deep Reinforcement Learning

Hariharasudhan A S - @HariharasudhanA Twitter Profile and Downloader

Hariharasudhan A S - @HariharasudhanA Twitter Profile and Downloader

One-Shot Imitation from Observing Humans via Domain-Adaptive Meta

One-Shot Imitation from Observing Humans via Domain-Adaptive Meta

Stochastic Adversarial Video Prediction

Stochastic Adversarial Video Prediction

Model-Based Reinforcement Learning for Atari on ShortScience org

Model-Based Reinforcement Learning for Atari on ShortScience org

Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic

Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic

GMC Assets search | GlobalMediaCentre com

GMC Assets search | GlobalMediaCentre com

Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic

Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

A Beginners Guide To Dopamine Reinforcement Learning Framework

A Beginners Guide To Dopamine Reinforcement Learning Framework

MONTRÉAL AI | Montréal Artificial Intelligence - MONTRÉAL AI

MONTRÉAL AI | Montréal Artificial Intelligence - MONTRÉAL AI

Learning How to Actively Learn: A Deep Imitation Learning Approach

Learning How to Actively Learn: A Deep Imitation Learning Approach

RVArt Review – Page 2 – -a home for dance and theater

RVArt Review – Page 2 – -a home for dance and theater

PDF) Reinforcement learning for non-prehensile manipulation

PDF) Reinforcement learning for non-prehensile manipulation

DISCRIMINATOR-ACTOR-CRITIC: ADDRESSING SAMPLE INEFFICIENCY AND

DISCRIMINATOR-ACTOR-CRITIC: ADDRESSING SAMPLE INEFFICIENCY AND

PDF] Model-Agnostic Meta-Learning for Fast Adaptation of Deep

PDF] Model-Agnostic Meta-Learning for Fast Adaptation of Deep

Supplementary material for Learning to Adapt for Stereo

Supplementary material for Learning to Adapt for Stereo

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

Papers With Code : Learning to Adapt in Dynamic, Real-World

Papers With Code : Learning to Adapt in Dynamic, Real-World

Deep Reinforcement Learning: Our Prescribed Study Path

Deep Reinforcement Learning: Our Prescribed Study Path

Stochastic Adversarial Video Prediction

Stochastic Adversarial Video Prediction

D] Machine Learning - WAYR (What Are You Reading) - Week 63

D] Machine Learning - WAYR (What Are You Reading) - Week 63

Chelsea Finn (@chelseabfinn) | Twitter

Chelsea Finn (@chelseabfinn) | Twitter

Inverse reinforcement learning for video games

Inverse reinforcement learning for video games

Profillic: AI research & source code to supercharge your projects

Profillic: AI research & source code to supercharge your projects

Model-Ensemble Trust-Region Policy Optimization – arXiv Vanity

Model-Ensemble Trust-Region Policy Optimization – arXiv Vanity

Introduction to Neural Networks and Brief Tutorial with Caffe 10th

Introduction to Neural Networks and Brief Tutorial with Caffe 10th