Probabilistic Deep Learning
29 papers with code • 0 benchmarks • 5 datasets
Benchmarks
These leaderboards are used to track progress in Probabilistic Deep Learning
DENSE
VQA-HAT
ADVIO
CBC
Hotel Sales
Most implemented papers
Overcoming Limitations of Mixture Density Networks: A Sampling and Fitting Framework for Multimodal Future Prediction
lmb-freiburg/Multimodal-Future-Prediction
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CVPR 2019
Future prediction is a fundamental principle of intelligence that helps plan actions and avoid possible dangers.
DeepSynth: Automata Synthesis for Automatic Task Segmentation in Deep Reinforcement Learning
This paper proposes DeepSynth, a method for effective training of deep Reinforcement Learning (RL) agents when the reward is sparse and non-Markovian, but at the same time progress towards the reward requires achieving an unknown sequence of high-level objectives.
Multivariate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows
zalandoresearch/pytorch-ts
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ICLR 2021
In this work we model the multivariate temporal dynamics of time series via an autoregressive deep learning model, where the data distribution is represented by a conditioned normalizing flow.
Olympus: a benchmarking framework for noisy optimization and experiment planning
Experiment planning strategies based on off-the-shelf optimization algorithms can be employed in fully autonomous research platforms to achieve desired experimentation goals with the minimum number of trials.
Learning Monocular Dense Depth from Events
Event cameras are novel sensors that output brightness changes in the form of a stream of asynchronous events instead of intensity frames.
A Quantum-Inspired Probabilistic Model for the Inverse Design of Meta-Structures
yingtaoluo/Probabilistic-density-network
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Here, inspired by quantum theory, we propose a probabilistic deep learning paradigm for the inverse design of functional meta-structures.
Towards Adversarial Robustness of Bayesian Neural Network through Hierarchical Variational Inference
ByungKwanLee/Hierarchical-Bayesian-Defense
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Recent works have applied Bayesian Neural Network (BNN) to adversarial training, and shown the improvement of adversarial robustness via the BNN's strength of stochastic gradient defense.
Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles
NASA's Global Ecosystem Dynamics Investigation (GEDI) is a key climate mission whose goal is to advance our understanding of the role of forests in the global carbon cycle.
Graph-based Thermal-Inertial SLAM with Probabilistic Neural Networks
Simultaneous Localization and Mapping (SLAM) system typically employ vision-based sensors to observe the surrounding environment.
Probabilistic Deep Learning for Electric-Vehicle Energy-Use Prediction
linas-p/EVDPEP
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International Symposium on Spatial and Temporal Databases 2021
For example, long-distance route planning for such vehicles relies on the prediction of both the expected travel time as well as energy use.
