37 research outputs found
Graphical Object-Centric Actor-Critic
There have recently been significant advances in the problem of unsupervised
object-centric representation learning and its application to downstream tasks.
The latest works support the argument that employing disentangled object
representations in image-based object-centric reinforcement learning tasks
facilitates policy learning. We propose a novel object-centric reinforcement
learning algorithm combining actor-critic and model-based approaches to utilize
these representations effectively. In our approach, we use a transformer
encoder to extract object representations and graph neural networks to
approximate the dynamics of an environment. The proposed method fills a
research gap in developing efficient object-centric world models for
reinforcement learning settings that can be used for environments with discrete
or continuous action spaces. Our algorithm performs better in a visually
complex 3D robotic environment and a 2D environment with compositional
structure than the state-of-the-art model-free actor-critic algorithm built
upon transformer architecture and the state-of-the-art monolithic model-based
algorithm
Scalable Batch Acquisition for Deep Bayesian Active Learning
In deep active learning, it is especially important to choose multiple
examples to markup at each step to work efficiently, especially on large
datasets. At the same time, existing solutions to this problem in the Bayesian
setup, such as BatchBALD, have significant limitations in selecting a large
number of examples, associated with the exponential complexity of computing
mutual information for joint random variables. We, therefore, present the Large
BatchBALD algorithm, which gives a well-grounded approximation to the BatchBALD
method that aims to achieve comparable quality while being more computationally
efficient. We provide a complexity analysis of the algorithm, showing a
reduction in computation time, especially for large batches. Furthermore, we
present an extensive set of experimental results on image and text data, both
on toy datasets and larger ones such as CIFAR-100.Comment: Accepted to SIAM International Conference on Data Mining 202
Neural Potential Field for Obstacle-Aware Local Motion Planning
Model predictive control (MPC) may provide local motion planning for mobile
robotic platforms. The challenging aspect is the analytic representation of
collision cost for the case when both the obstacle map and robot footprint are
arbitrary. We propose a Neural Potential Field: a neural network model that
returns a differentiable collision cost based on robot pose, obstacle map, and
robot footprint. The differentiability of our model allows its usage within the
MPC solver. It is computationally hard to solve problems with a very high
number of parameters. Therefore, our architecture includes neural image
encoders, which transform obstacle maps and robot footprints into embeddings,
which reduce problem dimensionality by two orders of magnitude. The reference
data for network training are generated based on algorithmic calculation of a
signed distance function. Comparative experiments showed that the proposed
approach is comparable with existing local planners: it provides trajectories
with outperforming smoothness, comparable path length, and safe distance from
obstacles. Experiment on Husky UGV mobile robot showed that our approach allows
real-time and safe local planning. The code for our approach is presented at
https://github.com/cog-isa/NPField together with demo video
Conformal Prediction for Federated Uncertainty Quantification Under Label Shift
Federated Learning (FL) is a machine learning framework where many clients
collaboratively train models while keeping the training data decentralized.
Despite recent advances in FL, the uncertainty quantification topic (UQ)
remains partially addressed. Among UQ methods, conformal prediction (CP)
approaches provides distribution-free guarantees under minimal assumptions. We
develop a new federated conformal prediction method based on quantile
regression and take into account privacy constraints. This method takes
advantage of importance weighting to effectively address the label shift
between agents and provides theoretical guarantees for both valid coverage of
the prediction sets and differential privacy. Extensive experimental studies
demonstrate that this method outperforms current competitors.Comment: ICML 202
SegmATRon: Embodied Adaptive Semantic Segmentation for Indoor Environment
This paper presents an adaptive transformer model named SegmATRon for
embodied image semantic segmentation. Its distinctive feature is the adaptation
of model weights during inference on several images using a hybrid
multicomponent loss function. We studied this model on datasets collected in
the photorealistic Habitat and the synthetic AI2-THOR Simulators. We showed
that obtaining additional images using the agent's actions in an indoor
environment can improve the quality of semantic segmentation. The code of the
proposed approach and datasets are publicly available at
https://github.com/wingrune/SegmATRon.Comment: 14 pages, 6 figure
Nonparametric Uncertainty Quantification for Single Deterministic Neural Network
This paper proposes a fast and scalable method for uncertainty quantification
of machine learning models' predictions. First, we show the principled way to
measure the uncertainty of predictions for a classifier based on
Nadaraya-Watson's nonparametric estimate of the conditional label distribution.
Importantly, the proposed approach allows to disentangle explicitly aleatoric
and epistemic uncertainties. The resulting method works directly in the feature
space. However, one can apply it to any neural network by considering an
embedding of the data induced by the network. We demonstrate the strong
performance of the method in uncertainty estimation tasks on text
classification problems and a variety of real-world image datasets, such as
MNIST, SVHN, CIFAR-100 and several versions of ImageNet.Comment: NeurIPS 2022 pape