Search Results for author:
Found 39 papers, 20 papers with code
On the Pitfalls of Heteroscedastic Uncertainty Estimation with Probabilistic Neural Networks
In this work, we examine this approach and identify potential hazards associated with the use of log-likelihood in conjunction with gradient-based optimizers.
Hierarchical Reinforcement Learning with Timed Subgoals
Hierarchical reinforcement learning (HRL) holds great potential for sample-efficient learning on challenging long-horizon tasks.
A soft thumb-sized vision-based sensor with accurate all-round force perception
Insight has an overall spatial resolution of 0. 4 mm, force magnitude accuracy around 0. 03 N, and force direction accuracy around 5 degrees over a range of 0. 03--2 N for numerous distinct contacts with varying contact area.
Sparsely Changing Latent States for Prediction and Planning in Partially Observable Domains
A common approach to prediction and planning in partially observable domains is to use recurrent neural networks (RNNs), which ideally develop and maintain a latent memory about hidden, task-relevant factors.
Planning from Pixels in Environments with Combinatorially Hard Search Spaces
The ability to form complex plans based on raw visual input is a litmus test for current capabilities of artificial intelligence, as it requires a seamless combination of visual processing and abstract algorithmic execution, two traditionally separate areas of computer science.
Self-supervised Reinforcement Learning with Independently Controllable Subgoals
We propose a novel self-supervised agent that estimates relations between environment components and uses them to independently control different parts of the environment state.
Causal Influence Detection for Improving Efficiency in Reinforcement Learning
Many reinforcement learning (RL) environments consist of independent entities that interact sparsely.
Assessing aesthetics of generated abstract images using correlation structure
We demonstrate that even with the randomly selected weights the correlation functions remain largely determined by the network architecture.
Informed Equation Learning
Our system then utilizes a robust method to learn equations with atomic functions exhibiting singularities, as e. g. logarithm and division.
CombOptNet: Fit the Right NP-Hard Problem by Learning Integer Programming Constraints
Bridging logical and algorithmic reasoning with modern machine learning techniques is a fundamental challenge with potentially transformative impact.
The dynamical regime and its importance for evolvability, task performance and generalization
Surprisingly, we find that all populations, regardless of their initial regime, evolve to be subcritical in simple tasks and even strongly subcritical populations can reach comparable performance.
Neuro-algorithmic Policies enable Fast Combinatorial Generalization
Furthermore, we show that for a certain subclass of the MDP framework, this can be alleviated by neuro-algorithmic architectures.
Demystifying Inductive Biases for $β$-VAE Based Architectures
By small, elaborate perturbations of existing datasets, we hide the convenient correlation structure that is easily exploited by a variety of architectures.
Machine learning time-local generators of open quantum dynamics
Such evolution is typically emerging under the assumption of a weak coupling between the system and an infinitely large bath.
Quantum Physics Quantum Gases
Neuro-algorithmic Policies for Discrete Planning
Although model-based and model-free approaches to learning control of systems have achieved impressive results on standard benchmarks, most have been shown to be lacking in their generalization capabilities.
Extracting Strong Policies for Robotics Tasks from zero-order trajectory optimizers
Solving high-dimensional, continuous robotic tasks is a challenging optimization problem.
Clearing the Path for Truly Semantic Representation Learning
The performance of $\beta$-Variational-Autoencoders ($\beta$-VAEs) and their variants on learning semantically meaningful, disentangled representations is unparalleled.
How to Train Your Differentiable Filter
In many robotic applications, it is crucial to maintain a belief about the state of a system, which serves as input for planning and decision making and provides feedback during task execution.
Self-supervised Visual Reinforcement Learning with Object-centric Representations
We show that the structure in the representations in combination with goal-conditioned attention policies helps the autonomous agent to discover and learn useful skills.
Fit The Right NP-Hard Problem: End-to-end Learning of Integer Programming Constraints
Bridging logical and algorithmic reasoning with modern machine learning techniques is a fundamental challenge with potentially transformative impact.
Discrete Planning with Neuro-algorithmic Policies
Although model-based and model-free approaches to learning the control of systems have achieved impressive results on standard benchmarks, most have been shown to be lacking in their generalization capabilities.
Sample-efficient Cross-Entropy Method for Real-time Planning
However, their sampling inefficiency prevents them from being used for real-time planning and control.
Deep Graph Matching via Blackbox Differentiation of Combinatorial Solvers
Building on recent progress at the intersection of combinatorial optimization and deep learning, we propose an end-to-end trainable architecture for deep graph matching that contains unmodified combinatorial solvers.
Ranked #3 on
Graph Matching
on PASCAL VOC
Optimizing Rank-based Metrics with Blackbox Differentiation
Rank-based metrics are some of the most widely used criteria for performance evaluation of computer vision models.
Differentiation of Blackbox Combinatorial Solvers
Achieving fusion of deep learning with combinatorial algorithms promises transformative changes to artificial intelligence.
Fast Non-Parametric Learning to Accelerate Mixed-Integer Programming for Online Hybrid Model Predictive Control
Today's fast linear algebra and numerical optimization tools have pushed the frontier of model predictive control (MPC) forward, to the efficient control of highly nonlinear and hybrid systems.
Analytical classical density functionals from an equation learning network
We explore the feasibility of using machine learning methods to obtain an analytic form of the classical free energy functional for two model fluids, hard rods and Lennard--Jones, in one dimension .
Control What You Can: Intrinsically Motivated Task-Planning Agent
We present a novel intrinsically motivated agent that learns how to control the environment in the fastest possible manner by optimizing learning progress.
Autonomous Identification and Goal-Directed Invocation of Event-Predictive Behavioral Primitives
Here, we introduce a computational learning architecture, termed surprise-based behavioral modularization into event-predictive structures (SUBMODES), that explores behavior and identifies the underlying behavioral units completely from scratch.
Variational Autoencoders Pursue PCA Directions (by Accident)
The Variational Autoencoder (VAE) is a powerful architecture capable of representation learning and generative modeling.
Deep Reinforcement Learning for Event-Triggered Control
Event-triggered control (ETC) methods can achieve high-performance control with a significantly lower number of samples compared to usual, time-triggered methods.
Learning Equations for Extrapolation and Control
We present an approach to identify concise equations from data using a shallow neural network approach.
L4: Practical loss-based stepsize adaptation for deep learning
We propose a stepsize adaptation scheme for stochastic gradient descent.
Extrapolation and learning equations
In classical machine learning, regression is treated as a black box process of identifying a suitable function from a hypothesis set without attempting to gain insight into the mechanism connecting inputs and outputs.
Self-organized control for musculoskeletal robots
The paper presents a solution with a controller that is devoid of any functionalities of its own, given by a fixed, explicit and context-free function of the recent history of the sensor values.
Quantifying Emergent Behavior of Autonomous Robots
For deterministic systems both measures will diverge with increasing resolution.
A novel plasticity rule can explain the development of sensorimotor intelligence
Grounding autonomous behavior in the nervous system is a fundamental challenge for neuroscience.
Linear combination of one-step predictive information with an external reward in an episodic policy gradient setting: a critical analysis
Previous experiments have shown that the predictive information (PI) is a good candidate to support autonomous, open-ended learning of complex behaviours, because a maximisation of the PI corresponds to an exploration of morphology- and environment-dependent behavioural regularities.
Information driven self-organization of complex robotic behaviors
We study nonlinear and nonstationary systems and introduce the time-local predicting information (TiPI) which allows us to derive exact results together with explicit update rules for the parameters of the controller in the dynamical systems framework.
