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Found 17 papers, 11 papers with code
Conditional Density Estimation with Neural Networks: Best Practices and Benchmarks
Given a set of empirical observations, conditional density estimation aims to capture the statistical relationship between a conditional variable $\mathbf{x}$ and a dependent variable $\mathbf{y}$ by modeling their conditional probability $p(\mathbf{y}|\mathbf{x})$.
Noise Regularization for Conditional Density Estimation
To address this issue, we develop a model-agnostic noise regularization method for CDE that adds random perturbations to the data during training.
UniGrasp: Learning a Unified Model to Grasp with Multifingered Robotic Hands
The majority of previous work has focused on developing grasp methods that generalize over novel object geometry but are specific to a certain robot hand.
Zero-Shot AutoML with Pretrained Models
Given a new dataset D and a low compute budget, how should we choose a pre-trained model to fine-tune to D, and set the fine-tuning hyperparameters without risking overfitting, particularly if D is small?
MDP Playground: An Analysis and Debug Testbed for Reinforcement Learning
We define a parameterised collection of fast-to-run toy environments in OpenAI Gym by varying these dimensions and propose to use these to understand agents better.
Learning Synthetic Environments for Reinforcement Learning with Evolution Strategies
This work explores learning agent-agnostic synthetic environments (SEs) for Reinforcement Learning.
Learning Synthetic Environments and Reward Networks for Reinforcement Learning
In a one-to-one comparison, learning an SE proxy requires more interactions with the real environment than training agents only on the real environment.
Quick-Tune: Quickly Learning Which Pretrained Model to Finetune and How
With the ever-increasing number of pretrained models, machine learning practitioners are continuously faced with which pretrained model to use, and how to finetune it for a new dataset.
Deep Episodic Memory: Encoding, Recalling, and Predicting Episodic Experiences for Robot Action Execution
We present a novel deep neural network architecture for representing robot experiences in an episodic-like memory which facilitates encoding, recalling, and predicting action experiences.
Learning Visual Dynamics Models of Rigid Objects using Relational Inductive Biases
The first, Graph Networks (GN) based approach, considers explicitly defined edge attributes and not only does it consistently underperform an auto-encoder baseline that we modified to predict future states, our results indicate how different edge attributes can significantly influence the predictions.
Introducing the Simulated Flying Shapes and Simulated Planar Manipulator Datasets
We release two artificial datasets, Simulated Flying Shapes and Simulated Planar Manipulator that allow to test the learning ability of video processing systems.
Software Engineering Meets Deep Learning: A Mapping Study
Deep Learning (DL) is being used nowadays in many traditional Software Engineering (SE) problems and tasks.
MDP Playground: Controlling Orthogonal Dimensions of Hardness in Toy Environments
We present MDP Playground, an efficient benchmark for Reinforcement Learning (RL) algorithms with various dimensions of hardness that can be controlled independently to challenge algorithms in different ways and to obtain varying degrees of hardness in generated environments.
Winning solutions and post-challenge analyses of the ChaLearn AutoDL challenge 2019
Code submissions were executed on hidden tasks, with limited time and computational resources, pushing solutions that get results quickly.
Lessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification
Although deep neural networks are capable of achieving performance superior to humans on various tasks, they are notorious for requiring large amounts of data and computing resources, restricting their success to domains where such resources are available.
On the Importance of Hyperparameters and Data Augmentation for Self-Supervised Learning
Self-Supervised Learning (SSL) has become a very active area of Deep Learning research where it is heavily used as a pre-training method for classification and other tasks.
Hard View Selection for Self-Supervised Learning
Many Self-Supervised Learning (SSL) methods train their models to be invariant to different "views" of an image input for which a good data augmentation pipeline is crucial.
