Set Prediction without Imposing Structure as Conditional Density Estimation

• URL: http://arxiv.org/abs/2010.04109v2
• Date: Sun, 21 Feb 2021 17:22:48 GMT
• Title: Set Prediction without Imposing Structure as Conditional Density Estimation
• Authors: David W. Zhang, Gertjan J. Burghouts, Cees G.M. Snoek
• Abstract summary: We propose an alternative to training via set losses by viewing learning as conditional density estimation. Our framework fits deep energy-based models and approximates the intractable likelihood with gradient-guided sampling. Our approach is competitive with previous set prediction models on standard benchmarks.
• Score: 40.86881969839325
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Set prediction is about learning to predict a collection of unordered variables with unknown interrelations. Training such models with set losses imposes the structure of a metric space over sets. We focus on stochastic and underdefined cases, where an incorrectly chosen loss function leads to implausible predictions. Example tasks include conditional point-cloud reconstruction and predicting future states of molecules. In this paper, we propose an alternative to training via set losses by viewing learning as conditional density estimation. Our learning framework fits deep energy-based models and approximates the intractable likelihood with gradient-guided sampling. Furthermore, we propose a stochastically augmented prediction algorithm that enables multiple predictions, reflecting the possible variations in the target set. We empirically demonstrate on a variety of datasets the capability to learn multi-modal densities and produce different plausible predictions. Our approach is competitive with previous set prediction models on standard benchmarks. More importantly, it extends the family of addressable tasks beyond those that have unambiguous predictions.

Related papers

• Multi-model Ensemble Conformal Prediction in Dynamic Environments [14.188004615463742]
  We introduce a novel adaptive conformal prediction framework, where the model used for creating prediction sets is selected on the fly from multiple candidate models. The proposed algorithm is proven to achieve strongly adaptive regret over all intervals while maintaining valid coverage.
  arXiv  Detail & Related papers  (2024-11-06T05:57:28Z)
• Awareness of uncertainty in classification using a multivariate model and multi-views [1.3048920509133808]
  The proposed model regularizes uncertain predictions, and trains to calculate both the predictions and their uncertainty estimations. Given the multi-view predictions together with their uncertainties and confidences, we proposed several methods to calculate final predictions. The proposed methodology was tested using CIFAR-10 dataset with clean and noisy labels.
  arXiv  Detail & Related papers  (2024-04-16T06:40:51Z)
• Conformal online model aggregation [29.43493007296859]
  This paper proposes a new approach towards conformal model aggregation in online settings. It is based on combining the prediction sets from several algorithms by voting, where weights on the models are adapted over time based on past performance.
  arXiv  Detail & Related papers  (2024-03-22T15:40:06Z)
• Conformal Prediction for Deep Classifier via Label Ranking [29.784336674173616]
  Conformal prediction is a statistical framework that generates prediction sets with a desired coverage guarantee. We propose a novel algorithm named $textitSorted Adaptive Prediction Sets$ (SAPS) SAPS discards all the probability values except for the maximum softmax probability.
  arXiv  Detail & Related papers  (2023-10-10T08:54:14Z)
• Quantification of Predictive Uncertainty via Inference-Time Sampling [57.749601811982096]
  We propose a post-hoc sampling strategy for estimating predictive uncertainty accounting for data ambiguity. The method can generate different plausible outputs for a given input and does not assume parametric forms of predictive distributions.
  arXiv  Detail & Related papers  (2023-08-03T12:43:21Z)
• Post-selection Inference for Conformal Prediction: Trading off Coverage for Precision [0.0]
  Traditionally, conformal prediction inference requires a data-independent specification of miscoverage level. We develop simultaneous conformal inference to account for data-dependent miscoverage levels.
  arXiv  Detail & Related papers  (2023-04-12T20:56:43Z)
• Uncertainty estimation of pedestrian future trajectory using Bayesian approximation [137.00426219455116]
  Under dynamic traffic scenarios, planning based on deterministic predictions is not trustworthy. The authors propose to quantify uncertainty during forecasting using approximation which deterministic approaches fail to capture. The effect of dropout weights and long-term prediction on future state uncertainty has been studied.
  arXiv  Detail & Related papers  (2022-05-04T04:23:38Z)
• Dense Uncertainty Estimation [62.23555922631451]
  In this paper, we investigate neural networks and uncertainty estimation techniques to achieve both accurate deterministic prediction and reliable uncertainty estimation. We work on two types of uncertainty estimations solutions, namely ensemble based methods and generative model based methods, and explain their pros and cons while using them in fully/semi/weakly-supervised framework.
  arXiv  Detail & Related papers  (2021-10-13T01:23:48Z)
• Predicting Temporal Sets with Deep Neural Networks [50.53727580527024]
  We propose an integrated solution based on the deep neural networks for temporal sets prediction. A unique perspective is to learn element relationship by constructing set-level co-occurrence graph. We design an attention-based module to adaptively learn the temporal dependency of elements and sets.
  arXiv  Detail & Related papers  (2020-06-20T03:29:02Z)
• Ambiguity in Sequential Data: Predicting Uncertain Futures with Recurrent Models [110.82452096672182]
  We propose an extension of the Multiple Hypothesis Prediction (MHP) model to handle ambiguous predictions with sequential data. We also introduce a novel metric for ambiguous problems, which is better suited to account for uncertainties.
  arXiv  Detail & Related papers  (2020-03-10T09:15:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.