Uncertainty Quantification for Deep Context-Aware Mobile Activity Recognition and Unknown Context Discovery

• URL: http://arxiv.org/abs/2003.01753v1
• Date: Tue, 3 Mar 2020 19:35:34 GMT
• Title: Uncertainty Quantification for Deep Context-Aware Mobile Activity Recognition and Unknown Context Discovery
• Authors: Zepeng Huo, Arash PakBin, Xiaohan Chen, Nathan Hurley, Ye Yuan, Xiaoning Qian, Zhangyang Wang, Shuai Huang, Bobak Mortazavi
• Abstract summary: We develop a context-aware mixture of deep models termed the alpha-beta network. We improve accuracy and F score by 10% by identifying high-level contexts. In order to ensure training stability, we have used a clustering-based pre-training in both public and in-house datasets.
• Score: 85.36948722680822
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Activity recognition in wearable computing faces two key challenges: i) activity characteristics may be context-dependent and change under different contexts or situations; ii) unknown contexts and activities may occur from time to time, requiring flexibility and adaptability of the algorithm. We develop a context-aware mixture of deep models termed the {\alpha}-\b{eta} network coupled with uncertainty quantification (UQ) based upon maximum entropy to enhance human activity recognition performance. We improve accuracy and F score by 10% by identifying high-level contexts in a data-driven way to guide model development. In order to ensure training stability, we have used a clustering-based pre-training in both public and in-house datasets, demonstrating improved accuracy through unknown context discovery.

Related papers

• Stochastic Encodings for Active Feature Acquisition [100.47043816019888]
  Active Feature Acquisition is an instance-wise, sequential decision making problem.<n>The aim is to dynamically select which feature to measure based on current observations, independently for each test instance.<n>Common approaches either use Reinforcement Learning, which experiences training difficulties, or greedily maximize the conditional mutual information of the label and unobserved features, which makes myopic.<n>We introduce a latent variable model, trained in a supervised manner. Acquisitions are made by reasoning about the features across many possible unobserved realizations in a latent space.
  arXiv  Detail & Related papers  (2025-08-03T23:48:46Z)
• Dynamic Programming Techniques for Enhancing Cognitive Representation in Knowledge Tracing [125.75923987618977]
  We propose the Cognitive Representation Dynamic Programming based Knowledge Tracing (CRDP-KT) model.<n>It is a dynamic programming algorithm to optimize cognitive representations based on the difficulty of the questions and the performance intervals between them.<n>It provides more accurate and systematic input features for subsequent model training, thereby minimizing distortion in the simulation of cognitive states.
  arXiv  Detail & Related papers  (2025-06-03T14:44:48Z)
• Underlying Semantic Diffusion for Effective and Efficient In-Context Learning [113.4003355229632]
  Underlying Semantic Diffusion (US-Diffusion) is an enhanced diffusion model that boosts underlying semantics learning, computational efficiency, and in-context learning capabilities. We present a Feedback-Aided Learning (FAL) framework, which leverages feedback signals to guide the model in capturing semantic details. We also propose a plug-and-play Efficient Sampling Strategy (ESS) for dense sampling at time steps with high-noise levels.
  arXiv  Detail & Related papers  (2025-03-06T03:06:22Z)
• Post-hoc Probabilistic Vision-Language Models [51.12284891724463]
  Vision-language models (VLMs) have found remarkable success in classification, retrieval, and generative tasks. We propose post-hoc uncertainty estimation in VLMs that does not require additional training. Our results show promise for safety-critical applications of large-scale models.
  arXiv  Detail & Related papers  (2024-12-08T18:16:13Z)
• A Review of Bayesian Uncertainty Quantification in Deep Probabilistic Image Segmentation [0.0]
  Advancements in image segmentation play an integral role within the greater scope of Deep Learning-based computer vision. Uncertainty quantification has been extensively studied within this context, enabling expression of model ignorance (epistemic uncertainty) or data ambiguity (aleatoric uncertainty) to prevent uninformed decision making. This work provides a comprehensive overview of probabilistic segmentation by discussing fundamental concepts in uncertainty that govern advancements in the field and the application to various tasks.
  arXiv  Detail & Related papers  (2024-11-25T13:26:09Z)
• Uncertainty-boosted Robust Video Activity Anticipation [72.14155465769201]
  Video activity anticipation aims to predict what will happen in the future, embracing a broad application prospect ranging from robot vision to autonomous driving. Despite the recent progress, the data uncertainty issue, reflected as the content evolution process and dynamic correlation in event labels, has been somehow ignored. We propose an uncertainty-boosted robust video activity anticipation framework, which generates uncertainty values to indicate the credibility of the anticipation results.
  arXiv  Detail & Related papers  (2024-04-29T12:31:38Z)
• Multi-Modal Prompt Learning on Blind Image Quality Assessment [65.0676908930946]
  Image Quality Assessment (IQA) models benefit significantly from semantic information, which allows them to treat different types of objects distinctly. Traditional methods, hindered by a lack of sufficiently annotated data, have employed the CLIP image-text pretraining model as their backbone to gain semantic awareness. Recent approaches have attempted to address this mismatch using prompt technology, but these solutions have shortcomings. This paper introduces an innovative multi-modal prompt-based methodology for IQA.
  arXiv  Detail & Related papers  (2024-04-23T11:45:32Z)
• Uncertainty Estimation by Fisher Information-based Evidential Deep Learning [61.94125052118442]
  Uncertainty estimation is a key factor that makes deep learning reliable in practical applications. We propose a novel method, Fisher Information-based Evidential Deep Learning ($mathcalI$-EDL) In particular, we introduce Fisher Information Matrix (FIM) to measure the informativeness of evidence carried by each sample, according to which we can dynamically reweight the objective loss terms to make the network more focused on the representation learning of uncertain classes.
  arXiv  Detail & Related papers  (2023-03-03T16:12:59Z)
• Incremental Permutation Feature Importance (iPFI): Towards Online Explanations on Data Streams [8.49072000414555]
  We are interested in dynamic scenarios where data is sampled progressively, and learning is done in an incremental rather than a batch mode. We seek efficient incremental algorithms for computing feature importance (FI) measures, specifically, an incremental FI measure based on feature marginalization of absent features similar to permutation feature importance (PFI)
  arXiv  Detail & Related papers  (2022-09-05T12:34:27Z)
• Adaptive Discrete Communication Bottlenecks with Dynamic Vector Quantization [76.68866368409216]
  We propose learning to dynamically select discretization tightness conditioned on inputs. We show that dynamically varying tightness in communication bottlenecks can improve model performance on visual reasoning and reinforcement learning tasks.
  arXiv  Detail & Related papers  (2022-02-02T23:54:26Z)
• Data Augmentation through Expert-guided Symmetry Detection to Improve Performance in Offline Reinforcement Learning [0.0]
  offline estimation of the dynamical model of a Markov Decision Process (MDP) is a non-trivial task. Recent works showed that an expert-guided pipeline relying on Density Estimation methods effectively detects this structure in deterministic environments. We show that the former results lead to a performance improvement when solving the learned MDP and then applying the optimized policy in the real environment.
  arXiv  Detail & Related papers  (2021-12-18T14:32:32Z)
• On Efficient Uncertainty Estimation for Resource-Constrained Mobile Applications [0.0]
  Predictive uncertainty supplements model predictions and enables improved functionality of downstream tasks. We tackle this problem by building upon Monte Carlo Dropout (MCDO) models using the Axolotl framework. We conduct experiments on (1) a multi-class classification task using the CIFAR10 dataset, and (2) a more complex human body segmentation task.
  arXiv  Detail & Related papers  (2021-11-11T22:24:15Z)
• Minimum-Delay Adaptation in Non-Stationary Reinforcement Learning via Online High-Confidence Change-Point Detection [7.685002911021767]
  We introduce an algorithm that efficiently learns policies in non-stationary environments. It analyzes a possibly infinite stream of data and computes, in real-time, high-confidence change-point detection statistics. We show that (i) this algorithm minimizes the delay until unforeseen changes to a context are detected, thereby allowing for rapid responses.
  arXiv  Detail & Related papers  (2021-05-20T01:57:52Z)
• Spectrum-Guided Adversarial Disparity Learning [52.293230153385124]
  We propose a novel end-to-end knowledge directed adversarial learning framework. It portrays the class-conditioned intraclass disparity using two competitive encoding distributions and learns the purified latent codes by denoising learned disparity. The experiments on four HAR benchmark datasets demonstrate the robustness and generalization of our proposed methods over a set of state-of-the-art.
  arXiv  Detail & Related papers  (2020-07-14T05:46:27Z)

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.