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Found 19 papers, 3 papers with code
On the Lipschitz Constant of Deep Networks and Double Descent
Existing bounds on the generalization error of deep networks assume some form of smooth or bounded dependence on the input variable, falling short of investigating the mechanisms controlling such factors in practice.
Deep Double Descent via Smooth Interpolation
The ability of overparameterized deep networks to interpolate noisy data, while at the same time showing good generalization performance, has been recently characterized in terms of the double descent curve for the test error.
Dance Style Transfer with Cross-modal Transformer
We present CycleDance, a dance style transfer system to transform an existing motion clip in one dance style to a motion clip in another dance style while attempting to preserve motion context of the dance.
On the Subspace Structure of Gradient-Based Meta-Learning
In this work we provide an analysis of the distribution of the post-adaptation parameters of Gradient-Based Meta-Learning (GBML) methods.
Training and Evaluation of Deep Policies using Reinforcement Learning and Generative Models
We present a data-efficient framework for solving sequential decision-making problems which exploits the combination of reinforcement learning (RL) and latent variable generative models.
Are All Linear Regions Created Equal?
The number of linear regions has been studied as a proxy of complexity for ReLU networks.
Monte Carlo Filtering Objectives: A New Family of Variational Objectives to Learn Generative Model and Neural Adaptive Proposal for Time Series
Learning generative models and inferring latent trajectories have shown to be challenging for time series due to the intractable marginal likelihoods of flexible generative models.
Graph-based Normalizing Flow for Human Motion Generation and Reconstruction
Data-driven approaches for modeling human skeletal motion have found various applications in interactive media and social robotics.
Combining Planning and Learning of Behavior Trees for Robotic Assembly
Industrial robots can solve very complex tasks in controlled environments, but modern applications require robots able to operate in unpredictable surroundings as well.
FEW-SHOTLEARNING WITH WEAK SUPERVISION
Few-shot meta-learning methods aim to learn the common structure shared across a set of tasks to facilitate learning new tasks with small amounts of data.
Bayesian Meta-Learning for Few-Shot Policy Adaptation Across Robotic Platforms
Our results show that the proposed method can successfully adapt a trained policy to different robotic platforms with novel physical parameters and the superiority of our meta-learning algorithm compared to state-of-the-art methods for the introduced few-shot policy adaptation problem.
Data-efficient visuomotor policy training using reinforcement learning and generative models
We present a data-efficient framework for solving visuomotor sequential decision-making problems which exploits the combination of reinforcement learning (RL) and latent variable generative models.
Human-centered collaborative robots with deep reinforcement learning
We present a reinforcement learning based framework for human-centered collaborative systems.
Hyperplane Arrangements of Trained ConvNets Are Biased
We investigate the geometric properties of the functions learned by trained ConvNets in the preactivation space of their convolutional layers, by performing an empirical study of hyperplane arrangements induced by a convolutional layer.
Imitating by generating: deep generative models for imitation of interactive tasks
To coordinate actions with an interaction partner requires a constant exchange of sensorimotor signals.
Adversarial Feature Training for Generalizable Robotic Visuomotor Control
Deep reinforcement learning (RL) has enabled training action-selection policies, end-to-end, by learning a function which maps image pixels to action outputs.
Meta-Learning for Multi-objective Reinforcement Learning
Multi-objective reinforcement learning (MORL) is the generalization of standard reinforcement learning (RL) approaches to solve sequential decision making problems that consist of several, possibly conflicting, objectives.
A Sensorimotor Reinforcement Learning Framework for Physical Human-Robot Interaction
Modeling of physical human-robot collaborations is generally a challenging problem due to the unpredictive nature of human behavior.
Feature Descriptors for Tracking by Detection: a Benchmark
In this paper, we provide an extensive evaluation of the performance of local descriptors for tracking applications.
