Evolving Multi-Channel Confidence-Aware Activation Functions for Missing Data with Channel Propagation

• URL: http://arxiv.org/abs/2602.13864v1
• Date: Sat, 14 Feb 2026 19:52:10 GMT
• Title: Evolving Multi-Channel Confidence-Aware Activation Functions for Missing Data with Channel Propagation
• Abstract summary: In neural networks, activation functions significantly affect performance yet do not account for missingness indicators or confidence scores.<n>We propose Three-Channel Evolved Activations (3C-EA) and ChannelProp, an algorithm that deterministically propagates missingness and confidence values.<n>Results indicate that integrating missingness and confidence inputs into the activation search improves classification performance under missingness.
• Score: 1.3999481573773072
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning in the presence of missing data can result in biased predictions and poor generalizability, among other difficulties, which data imputation methods only partially address. In neural networks, activation functions significantly affect performance yet typical options (e.g., ReLU, Swish) operate only on feature values and do not account for missingness indicators or confidence scores. We propose Three-Channel Evolved Activations (3C-EA), which we evolve using Genetic Programming to produce multivariate activation functions f(x, m, c) in the form of trees that take (i) the feature value x, (ii) a missingness indicator m, and (iii) an imputation confidence score c. To make these activations useful beyond the input layer, we introduce ChannelProp, an algorithm that deterministically propagates missingness and confidence values via linear layers based on weight magnitudes, retaining reliability signals throughout the network. We evaluate 3C-EA and ChannelProp on datasets with natural and injected (MCAR/MAR/MNAR) missingness at multiple rates under identical preprocessing and splits. Results indicate that integrating missingness and confidence inputs into the activation search improves classification performance under missingness.

Related papers

• Are LLM Uncertainty and Correctness Encoded by the Same Features? A Functional Dissociation via Sparse Autoencoders [10.172598963520961]
  Large language models can be uncertain yet correct, or confident yet wrong, raising the question of whether their output-level uncertainty and their actual correctness are driven by the same internal mechanisms or by distinct feature populations.<n>We introduce a 2x2 framework that partitions model predictions along correctness and confidence axes, and uses sparse autoencoders to identify features associated with each dimension independently.
  arXiv  Detail & Related papers  (2026-04-21T20:34:14Z)
• Self-Supervised Learning via Flow-Guided Neural Operator on Time-Series Data [57.85958428020496]
  Flow-Guided Neural Operator (FGNO) is a novel framework combining operator learning with flow matching for SSL training.<n>FGNO learns mappings in functional spaces by using Short-Time Fourier Transform to unify different time resolutions.<n>Unlike prior generative SSL methods that use noisy inputs during inference, we propose using clean inputs for representation extraction while learning representations with noise.
  arXiv  Detail & Related papers  (2026-02-12T18:54:57Z)
• DDFI: Diverse and Distribution-aware Missing Feature Imputation via Two-step Reconstruction [22.492502807174237]
  DDFI is a Diverse and Distribution-aware Missing Feature Imputation method.<n>It combines feature propagation with a graph-based Masked AutoEncoder.<n>It outperforms state-of-the-art methods under both transductive and inductive settings.
  arXiv  Detail & Related papers  (2025-12-06T09:06:08Z)
• Continual Action Quality Assessment via Adaptive Manifold-Aligned Graph Regularization [53.82400605816587]
  Action Quality Assessment (AQA) quantifies human actions in videos, supporting applications in sports scoring, rehabilitation, and skill evaluation.<n>A major challenge lies in the non-stationary nature of quality distributions in real-world scenarios.<n>We introduce Continual AQA (CAQA), which equips with Continual Learning capabilities to handle evolving distributions.
  arXiv  Detail & Related papers  (2025-10-08T10:09:47Z)
• ResAD++: Towards Class Agnostic Anomaly Detection via Residual Feature Learning [52.11294707895649]
  This paper explores the problem of class-agnostic anomaly detection (AD)<n>The objective is to train one class-agnostic AD model that can generalize to detect anomalies in diverse new classes from different domains without any retraining or fine-tuning on the target data.<n> Comprehensive experiments on eight real-world AD datasets demonstrate that our ResAD++ can achieve remarkable AD results when directly used in new classes.
  arXiv  Detail & Related papers  (2025-09-28T08:41:05Z)
• R-Sparse: Rank-Aware Activation Sparsity for Efficient LLM Inference [77.47238561728459]
  R-Sparse is a training-free activation sparsity approach capable of achieving high sparsity levels in advanced LLMs.<n> Experiments on Llama-2/3 and Mistral models across ten diverse tasks demonstrate that R-Sparse achieves comparable performance at 50% model-level sparsity.
  arXiv  Detail & Related papers  (2025-04-28T03:30:32Z)
• PEEL the Layers and Find Yourself: Revisiting Inference-time Data Leakage for Residual Neural Networks [64.90981115460937]
  This paper explores inference-time data leakage risks of deep neural networks (NNs)<n>We propose a novel backward feature inversion method, textbfPEEL, which can effectively recover block-wise input features from the intermediate output of residual NNs.<n>Our results show that PEEL outperforms the state-of-the-art recovery methods by an order of magnitude when evaluated by mean squared error (MSE)
  arXiv  Detail & Related papers  (2025-04-08T20:11:05Z)
• Generative Data Imputation for Sparse Learner Performance Data Using Generative Adversarial Imputation Networks [3.0800525961862992]
  Missing responses due to skips or incomplete attempts create data sparsity.<n>We propose a generative imputation approach using Generative Adrial Imputation Networks (GAIN)<n>Our method features a three-dimensional (3D) framework (learners, questions, and attempts), flexibly accommodating various sparsity levels.
  arXiv  Detail & Related papers  (2025-03-23T06:11:53Z)
• Handling Missing Data in Downstream Tasks With Distribution-Preserving Guarantees [2.54990557236581]
  We propose an imputation approach named F3I based on the iterative improvement of a K-nearest neighbor imputation.<n>We provide a theoretical analysis of imputation quality and data distribution preservation by F3I for several types of missing mechanisms.<n>We demonstrate the superior performance of F3I on several imputation and classification tasks.
  arXiv  Detail & Related papers  (2025-01-23T16:04:18Z)
• M$^3$-Impute: Mask-guided Representation Learning for Missing Value Imputation [12.174699459648842]
  M$3$-Impute aims to explicitly leverage the missingness information and such correlations with novel masking schemes. Experiment results show the effectiveness of M$3$-Impute by achieving 20 best and 4 second-best MAE scores on average.
  arXiv  Detail & Related papers  (2024-10-11T13:25:32Z)
• Multi-scale Quaternion CNN and BiGRU with Cross Self-attention Feature Fusion for Fault Diagnosis of Bearing [5.3598912592106345]
  Deep learning has led to significant advances in bearing fault diagnosis (FD) We propose a novel FD model by integrating multiscale quaternion convolutional neural network (MQCNN), bidirectional gated recurrent unit (BiG), and cross self-attention feature fusion (CSAFF)
  arXiv  Detail & Related papers  (2024-05-25T07:55:02Z)
• Uncertainty Modeling for Out-of-Distribution Generalization [56.957731893992495]
  We argue that the feature statistics can be properly manipulated to improve the generalization ability of deep learning models. Common methods often consider the feature statistics as deterministic values measured from the learned features. We improve the network generalization ability by modeling the uncertainty of domain shifts with synthesized feature statistics during training.
  arXiv  Detail & Related papers  (2022-02-08T16:09:12Z)

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.