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Found 19 papers, 8 papers with code
Generating Piano Practice Policy with a Gaussian Process
Because we each learn differently and there are many choices for possible piano practice tasks and methods, the set of practice modes should be dynamically adapted to the human learner, a process typically guided by a teacher.
Video Diffusion Models: A Survey
Diffusion generative models have recently become a powerful technique for creating and modifying high-quality, coherent video content.
Lane Segmentation Refinement with Diffusion Models
The lane graph is a key component for building high-definition (HD) maps and crucial for downstream tasks such as autonomous driving or navigation planning.
Benchmarks for Physical Reasoning AI
Therefore, we aim to offer an overview of existing benchmarks and their solution approaches and propose a unified perspective for measuring the physical reasoning capacity of AI systems.
Towards Transferring Tactile-based Continuous Force Control Policies from Simulation to Robot
The advent of tactile sensors in robotics has sparked many ideas on how robots can leverage direct contact measurements of their environment interactions to improve manipulation tasks.
Shape complexity estimation using VAE
In this paper, we compare methods for estimating the complexity of two-dimensional shapes and introduce a method that exploits reconstruction loss of Variational Autoencoders with different sizes of latent vectors.
Stroke-based Rendering: From Heuristics to Deep Learning
In the last few years, artistic image-making with deep learning models has gained a considerable amount of traction.
Face Generation and Editing with StyleGAN: A Survey
Our goal with this survey is to provide an overview of the state of the art deep learning methods for face generation and editing using StyleGAN.
Solving Learn-to-Race Autonomous Racing Challenge by Planning in Latent Space
The agent is required to pass the previously unknown F1-style track in the minimum time with the least amount of off-road driving violations.
YOLO -- You only look 10647 times
With this work we are explaining the "You Only Look Once" (YOLO) single-stage object detection approach as a parallel classification of 10647 fixed region proposals.
Critic Guided Segmentation of Rewarding Objects in First-Person Views
This work discusses a learning approach to mask rewarding objects in images using sparse reward signals from an imitation learning dataset.
Optimizing piano practice with a utility-based scaffold
Ideally, a focus on a particular practice method should be made in a way to maximize the learner's progress in learning to play the piano.
Towards robust and domain agnostic reinforcement learning competitions
Reinforcement learning competitions have formed the basis for standard research benchmarks, galvanized advances in the state-of-the-art, and shaped the direction of the field.
Guiding Representation Learning in Deep Generative Models with Policy Gradients
Therefore, this paper investigates the issues of joint training approaches and explores incorporation of policy gradients from RL into the VAE's latent space to find a task-specific latent space representation.
Solving Physics Puzzles by Reasoning about Paths
We propose a new deep learning model for goal-driven tasks that require intuitive physical reasoning and intervention in the scene to achieve a desired end goal.
From Crystallized Adaptivity to Fluid Adaptivity in Deep Reinforcement Learning -- Insights from Biological Systems on Adaptive Flexibility
Recent developments in machine-learning algorithms have led to impressive performance increases in many traditional application scenarios of artificial intelligence research.
Learning efficient haptic shape exploration with a rigid tactile sensor array
In the present work, we connect recent advances in recurrent models of visual attention with previous insights about the organisation of human haptic search behavior, exploratory procedures and haptic glances for a novel architecture that learns a generative model of haptic exploration in a simulated three-dimensional environment.
Modularization of End-to-End Learning: Case Study in Arcade Games
Complex environments and tasks pose a difficult problem for holistic end-to-end learning approaches.
Learning to Run challenge solutions: Adapting reinforcement learning methods for neuromusculoskeletal environments
In the NIPS 2017 Learning to Run challenge, participants were tasked with building a controller for a musculoskeletal model to make it run as fast as possible through an obstacle course.
