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Greatest papers with code

Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift

NeurIPS 2019 google-research/google-research

Modern machine learning methods including deep learning have achieved great success in predictive accuracy for supervised learning tasks, but may still fall short in giving useful estimates of their predictive {\em uncertainty}.

PROBABILISTIC DEEP LEARNING

Multivariate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows

ICLR 2021 zalandoresearch/pytorch-ts

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.

DECISION MAKING MULTIVARIATE TIME SERIES FORECASTING PROBABILISTIC DEEP LEARNING PROBABILISTIC TIME SERIES FORECASTING TIME SERIES

Learning Monocular Dense Depth from Events

16 Oct 2020uzh-rpg/rpg_e2depth

Event cameras are novel sensors that output brightness changes in the form of a stream of asynchronous events instead of intensity frames.

DEPTH ESTIMATION PROBABILISTIC DEEP LEARNING

Estimating and Evaluating Regression Predictive Uncertainty in Deep Object Detectors

13 Jan 2021asharakeh/probdet

We show that in the context of object detection, training variance networks with negative log likelihood (NLL) can lead to high entropy predictive distributions regardless of the correctness of the output mean.

OBJECT DETECTION PROBABILISTIC DEEP LEARNING

Olympus: a benchmarking framework for noisy optimization and experiment planning

8 Oct 2020aspuru-guzik-group/olympus

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.

PROBABILISTIC DEEP LEARNING

Deep Directional Statistics: Pose Estimation with Uncertainty Quantification

ECCV 2018 sergeyprokudin/deep_direct_stat

However, in challenging imaging conditions such as on low-resolution images or when the image is corrupted by imaging artifacts, current systems degrade considerably in accuracy.

POSE ESTIMATION PROBABILISTIC DEEP LEARNING

DeepSynth: Automata Synthesis for Automatic Task Segmentation in Deep Reinforcement Learning

22 Nov 2019grockious/deepsynth

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.

HIERARCHICAL REINFORCEMENT LEARNING MONTEZUMA'S REVENGE PROBABILISTIC DEEP LEARNING PROGRAM SYNTHESIS

A Quantum-Inspired Probabilistic Model for the Inverse Design of Meta-Structures

11 Nov 2020yingtaoluo/Probabilistic-density-network

Here, inspired by quantum theory, we propose a probabilistic deep learning paradigm for the inverse design of functional meta-structures.

PROBABILISTIC DEEP LEARNING