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Found 30 papers, 11 papers with code
Identification and multiply robust estimation in causal mediation analysis with treatment noncompliance
In this article, we consider a semiparametric framework for assessing causal mediation in the presence of treatment noncompliance without the exclusion restriction.
Theoretical Evaluation of the Capacity-Achieving Distribution for IM-DD Fiber-Optic Channels
For the IM-DD system under peak power constraint (PPC), a dynamic-assignment BA algorithm is applied to find the capacity-achieving distribution with minimum cardinality.
A Probabilistic Generative Model for Tracking Multi-Knowledge Concept Mastery Probability
To better model students' exercise responses, we proposed a logarithmic linear model with three interactive strategies, which models students' exercise responses by considering the relationship among students' knowledge status, knowledge concept, and problems.
Overlapping oriented imbalanced ensemble learning method based on projective clustering and stagewise hybrid sampling
Secondly, according to the characteristics of subset classes, a stage-wise hybrid sampling algorithm is designed to realize the de-overlapping and balancing of subsets.
Person Text-Image Matching via Text-Feature Interpretability Embedding and External Attack Node Implantation
Specifically, we improve the interpretability of text features by providing them with consistent semantic information with image features to achieve the alignment of text and describe image region features. To address the challenges posed by the diversity of text and the corresponding person images, we treat the variation caused by diversity to features as caused by perturbation information and propose a novel adversarial attack and defense method to solve it.
A new Stack Autoencoder: Neighbouring Sample Envelope Embedded Stack Autoencoder Ensemble Model
Second, an embedded stack autoencoder (ESAE) is proposed and trained in each layer of sample space to consider the original samples during training and in the network structure, thereby better finding the relationship between original feature samples and deep feature samples.
Envelope imbalanced ensemble model with deep sample learning and local-global structure consistency
Based on the analysis above, an imbalanced ensemble algorithm with the deep sample pre-envelope network (DSEN) and local-global structure consistency mechanism (LGSCM) is proposed here to solve the problem. This algorithm can guarantee high-quality deep envelope samples for considering the local manifold and global structures information, which is helpful for imbalance learning.
CADG: A Model Based on Cross Attention for Domain Generalization
In Domain Generalization (DG) tasks, models are trained by using only training data from the source domains to achieve generalization on an unseen target domain, this will suffer from the distribution shift problem.
Ranked #3 on
Domain Generalization
on VLCS
Subject Enveloped Deep Sample Fuzzy Ensemble Learning Algorithm of Parkinson's Speech Data
Therefore, it is necessary to reconstruct the existing large segments within one subject into few segments even one segment within one subject, which can facilitate the extraction of relevant speech features to characterize diagnostic markers for the whole subject.
Envelope Imbalance Learning Algorithm based on Multilayer Fuzzy C-means Clustering and Minimum Interlayer discrepancy
However, all existing clustering methods are based on a one-time approach.
Hölder Bounds for Sensitivity Analysis in Causal Reasoning
We examine interval estimation of the effect of a treatment T on an outcome Y given the existence of an unobserved confounder U.
Simpler, Faster, Stronger: Breaking The log-K Curse On Contrastive Learners With FlatNCE
InfoNCE-based contrastive representation learners, such as SimCLR, have been tremendously successful in recent years.
Tight Mutual Information Estimation With Contrastive Fenchel-Legendre Optimization
Successful applications of InfoNCE and its variants have popularized the use of contrastive variational mutual information (MI) estimators in machine learning.
A Compact and Interpretable Convolutional Neural Network for Cross-Subject Driver Drowsiness Detection from Single-Channel EEG
Driver drowsiness is one of main factors leading to road fatalities and hazards in the transportation industry.
Are deep learning models superior for missing data imputation in large surveys? Evidence from an empirical comparison
We conduct extensive simulation studies based on a subsample of the American Community Survey to compare the repeated sampling properties of four machine learning based MI methods: MICE with classification trees, MICE with random forests, generative adversarial imputation networks, and multiple imputation using denoising autoencoders.
Integrated Age Estimation Mechanism
Traditional age estimation mechanism focuses estimation age error, but ignores that there is a deviation between the estimated age and real age due to disease.
Generalizing trial evidence to target populations in non-nested designs: Applications to AIDS clinical trials
Motivated by the need to generalize evidence from two trials conducted in the AIDS Clinical Trials Group (ACTG), we consider weighting, regression and doubly robust estimators to estimate the causal effects of HIV interventions in a specified population of people living with HIV in the USA.
Methodology Applications
QoE-driven Secure Video Transmission in Cloud-edge Collaborative Networks
In this paper, by considering the interaction between video encoding and edge caching, we investigate the quality of experience (QoE)-driven cross-layer optimization of secure video transmission over the wireless backhaul link in cloud-edge collaborative networks.
Multimedia
Reconsidering Generative Objectives For Counterfactual Reasoning
As a step towards more flexible, scalable and accurate ITE estimation, we present a novel generative Bayesian estimation framework that integrates representation learning, adversarial matching and causal estimation.
STCNet: Spatio-Temporal Cross Network for Industrial Smoke Detection
To overcome these problems, a novel Spatio-Temporal Cross Network (STCNet) is proposed to recognize industrial smoke emissions.
Counterfactual Representation Learning with Balancing Weights
A key to causal inference with observational data is achieving balance in predictive features associated with each treatment type.
Revenue and Energy Efficiency-Driven Delay Constrained Computing Task Offloading and Resource Allocation in a Vehicular Edge Computing Network: A Deep Reinforcement Learning Approach
In this paper, we propose a joint task type and vehicle speed-aware task offloading and resource allocation strategy to decrease the vehicl's energy cost for executing tasks and increase the revenue of the vehicle for processing tasks within the delay constraint.
Double Robust Representation Learning for Counterfactual Prediction
Causal inference, or counterfactual prediction, is central to decision making in healthcare, policy and social sciences.
Estimating heterogeneous survival treatment effect in observational data using machine learning
Methods for estimating heterogeneous treatment effect in observational data have largely focused on continuous or binary outcomes, and have been relatively less vetted with survival outcomes.
Learning Consistency Pursued Correlation Filters for Real-Time UAV Tracking
By minimizing the difference between the practical and the scheduled ideal consistency map, the consistency level is constrained to maintain temporal smoothness, and rich temporal information contained in response maps is introduced.
Deep Double-Side Learning Ensemble Model for Few-Shot Parkinson Speech Recognition
To solve these two problems, based on the existing Parkinson speech feature data set, a deep double-side learning ensemble model is designed in this paper that can reconstruct speech features and samples deeply and simultaneously.
WasteNet: Waste Classification at the Edge for Smart Bins
In this paper, we propose WasteNet, a waste classification model based on convolutional neural networks that can be deployed on a low power device at the edge of the network, such as a Jetson Nano.
Training-Set Distillation for Real-Time UAV Object Tracking
After the establishment of a new slot, the weighted fusion of the previous samples generates one key-sample, in order to reduce the number of samples to be scored.
Distributed Equivalent Substitution Training for Large-Scale Recommender Systems
We present Distributed Equivalent Substitution (DES) training, a novel distributed training framework for large-scale recommender systems with dynamic sparse features.
The Liver Tumor Segmentation Benchmark (LiTS)
In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018.
