HuMaINs combines human and machine strengths for better inference tasks.
problem Improving inference performance through human-machine collaboration.
method Novel signal processing and machine learning solutions for HuMaINs architecture.
result HuMaINs achieves higher performance than either humans or machines individually.
Develops methods for cooperative Bayesian inference.
problem Cooperation between learning agents.
method Sequential Bayesian inference approaches.
result Theoretical foundation for cooperative inference.
AI agents learn to cooperate with users of unknown type.
problem Designing AI agents that can cooperate with new users effectively.
method Modeling user behavior as parameters, observing user actions to infer type, and adapting policies.
result Adaptive AI agents perform significantly better than non-adaptive ones in real scenarios.
AI uses statistical PQRS workflow for human-machine collaboration.
problem Integrating AI with statistical principles for better results.
method PQRS workflow integrating statistical concepts with AI.
result Reproducibility and interpretability of AI algorithms and results.
Human+Machine models show no advantage in recidivism prediction.
problem Improving fairness in recidivism risk assessments.
method Constructed Human Risk Score and hybrid Human+Machine models.
result Human and COMPAS decision making differed but not in ways that improved prediction.
Study human-machine interaction with private info using offline RL.
problem Confounding bias and distributional mismatch in offline RL for human-guided interaction.
method Developed a novel identification result and OPE method to address confounding bias, and used pessimism to tackle distributional mismatch.
result Policy pair converges to optimal one at satisfactory rate under mild assumptions.
MCAL reduces labeling costs by 6x for auto-labeling data sets.
problem Expensive human annotation for ground-truth data sets.
method Iterative approach that trains a classifier to auto-label part of the data set, determining which samples to label using humans and which to label using the classifier at each step.
result 6x lower overall cost compared to human labeling the entire data set, always cheaper than competing strategies.
New AI assistant for power grid operators simplifies complex decision-making.
problem Complexity and uncertainty in power grid operations.
method Unified human-machine interface and AI integration.
result Development of a new assistant framework for power grid operators.
Improved speech emotion recognition using pre-trained language models.
problem Challenging task of speech emotion recognition for natural human-machine interaction.
method Fine-tuning pre-trained language models for text emotion recognition, combining with speech emotion recognition.
result 73.5% accuracy in speech emotion recognition on a subset of IEMOCAP dataset.
Study on predictable forward processes in trading without frequent evaluations.
problem Trading performance evaluation times not matching trading times.
method Solving a linear functional equation to construct predictable forward processes.
result Predictable forward processes are inherently myopic and optimal strategies do not use future information.
A model for human-machine decision-making with private info and opacity.
problem Optimizing decisions in a human-machine system with private info and opacity.
method Formulated as a two-player learning problem, proved lower and upper bounds on optimality.
result Simple coordination strategy is nearly minimax optimal, efficient learning possible under certain assumptions.
Interfacing a kinetic action of a person to an action of a machine system is an important research topic in many application areas. One of the key factors for intimate human-machine interaction is the ability of the control algorithm to detect and classify different user commands with shortest possible latency, thus ma…
Framework ensures alignment between humans and machines in LLMs.
problem Human-machine misalignment in LLMs scoring mechanisms.
method Lightweight calibration framework for blackbox models.
result Provably guarantees alignment between humans and machines.
Pandora hybridizes human and machine methods to explain AI system failures.
problem Understanding and explaining failures in complex AI systems.
method Hybrid human-machine methods and tools for summarizing system malfunction.
result Detailed performance views help in analysis and debugging of AI systems.
This paper evaluates deferring systems using causal inference.
problem Evaluating the impact of deferring systems on model accuracy is challenging.
method The paper uses a causal inference framework to evaluate deferring systems, distinguishing between scenarios with and without access to human predictions.
result The approach allows identifying causal effects of deferring strategies on predictive accuracy.
Deep neural networks improve sEMG-based hand gesture classification.
problem Accurate classification of hand gestures from sEMG signals.
method Master-slave architecture with DNNs and synthetic feature data.
result Up to 9% improvement in accuracy with synthetic data.
A method to prevent overfitting by modeling user knowledge in interactive machine learning.
problem Overfitting due to user reinforcement of noisy patterns in training data.
method User modelling based on rational behavior to correct overfitting.
result Improves predictive performance in sentiment analysis tasks.
MeLA learns succinct model codes from few examples to predict unseen tasks.
problem Machine learning models struggle with extrapolating from limited training data.
method Meta-learning autoencoder structure to learn model code from few examples.
result MeLA constructs models that match true underlying models with lower loss.
EdgeSpeechNets improve speech recognition on mobile devices.
problem Deploying deep learning for speech recognition on edge devices is challenging.
method Human-machine collaboration for designing efficient DNN architectures.
result EdgeSpeechNets achieve higher accuracy with smaller network size and lower computational cost.
Deep architecture learns transferable features for robust speech emotion recognition.
problem Robust and discriminative features for diverse speech emotion domains.
method Jointly uses CNN for domain-shared features and LSTM for domain-specific emotion classification.
result Transferable features provide gains up to 18.4% in speech emotion recognition.
CA connects visual concepts to neural network representations.
problem Global association of human interpretable concepts with neural network internals.
method Established a taxonomy for CA methods and reviewed state-of-the-art research.
result Unified understanding of CA methods and datasets.
Paper compares dimensionality reduction methods for affect classification.
problem Difficulty in obtaining labeled training samples for affect classification.
method Five dimensionality reduction approaches are compared.
result No single approach universally outperforms others in affect classification.
The paper offers a checklist for comparing human and machine visual perception.
problem Comparing human and machine visual perception.
method Designing, conducting, and interpreting experiments to investigate mechanisms.
result Feedback mechanisms may not be necessary for visual reasoning tasks.
We present a systematic analysis on the performance of a phonetic recogniser when the window of input features is not symmetric with respect to the current frame. The recogniser is based on Context Dependent Deep Neural Networks (CD-DNNs) and Hidden Markov Models (HMMs). The objective is to reduce the latency of the sy…
Bayesian CNN estimates uncertainty in COVID-19 detection.
problem Uncertainty in deep learning predictions for medical diagnosis.
method Drop-weight based Bayesian Convolutional Neural Networks (BCNN).
result Uncertainty correlates with prediction accuracy.
Mathematical framework for cooperative communication explains belief transmission.
problem Lack of understanding why cooperation enables effective belief transmission.
method Connection to optimal transport theory, deriving prior models, statistical interpretations, proofs of robustness and instability.
result Cooperative communication provably enables effective, robust belief transmission.
Brain2Char decodes text from brain recordings, achieving state-of-the-art performance.
problem Directly decoding text from brain recordings for communication.
method Combines 3D Inception layers, bidirectional recurrent layers, and language model weighted beam search. Uses CTC loss and auxiliary losses for regularization.
result Achieves 10.6%, 8.5%, and 7.0% WER on vocabulary sizes from 1200 to 1900 words.
Paper tackles dynamic behavior of variable topology mechanisms, presenting new transition conditions.
problem Dynamic behavior of mechanisms with changing kinematic topology.
method Presented new transition conditions for variable topology mechanisms using projected motion equations and Voronets equations.
result Results show the dynamic behavior of joint locking in 3R and 6DOF mechanisms.
Autonomous systems can substantially enhance a human's efficiency and effectiveness in complex environments. Machines, however, are often unable to observe the preferences of the humans that they serve. Despite the fact that the human's and machine's objectives are aligned, asymmetric information, along with heterogene…
Proposes a graph-based method for active learning in regression.
problem Active learning for regression is understudied and faces challenges with noisy labels and parameter uncertainty.
method Formulates active regression as a bipartite graph optimization problem.
result Demonstrates the effectiveness of the proposed graph-based approach on benchmark data sets.
Dual-stage sEMG classification improves gesture recognition accuracy.
problem Improving accuracy in hand gesture recognition from sEMG signals.
method Dual-stage classification approach: first stage groups similar activities, second stage classifies within groups.
result Dual-stage classification yields significantly higher accuracy than single-stage approach.
Adding metadata abruptly changes network inference outcomes.
problem Understanding the impact of metadata on network inference.
method Investigated the effect of metadata on network inference problems.
result Metadata causes abrupt transitions in inference outcomes.
PHASE dataset simulates complex social interactions in physical environments.
problem Lack of datasets for evaluating physically grounded perception of complex social interactions.
method Created PHASE dataset of 2D animations with procedural generation and physics engine.
result SIMPLE model outperforms neural networks in recognizing complex social interactions.
Bayesian topological learning improves EEG signal analysis for brain state classification.
problem Challenges in classifying and analyzing noisy, nonlinear, nonstationary EEG signals.
method Persistent homology with Bayesian framework to track topological features and incorporate prior knowledge.
result Bayesian topological learning outperforms existing methods for noisy EEG classification.
Spectral Inference Networks learn eigenfunctions from data using optimization.
problem Learning eigenfunctions of linear operators from data.
method Spectral Inference Networks generalize Slow Feature Analysis to generic symmetric operators and use stochastic optimization.
result Spectral Inference Networks accurately recover eigenfunctions and discover interpretable representations from video data.
Inference network approximates structured argmax inference for structured prediction.
problem Efficient inference for complex structured prediction problems.
method Trains a neural network to approximate structured argmax inference, using large-margin training criteria.
result Significant speed-ups and improved accuracy compared to gradient descent methods.
BayesFlow trains neural networks for fast Bayesian inference.
problem Fast Bayesian inference for complex models.
method Amortized neural networks for intractable posterior distributions.
result Fast inference through pre-trained neural networks.
Proposes a new algorithm for efficient probabilistic inference.
problem Efficient probabilistic inference in deep models with graphical structures.
method Structured inference networks and variational message-passing algorithm.
result Enables fast and efficient natural-gradient inference for deep structured models.
Paper shows how to infer hidden states in neural networks analytically.
problem Intractability of Bayesian inference for neural networks.
method Leverage tractable approximate Gaussian inference (TAGI) for hidden states inference.
result Demonstrates inference of hidden states through constraints for various applications.
Combines human and model predictions for improved accuracy.
problem Improving classification accuracy when both human and model predictions are imperfect.
method Uses confusion matrices and calibration to combine probabilistic model outputs with human class-level predictions.
result Human-model combinations consistently outperform either alone, with accuracy gains even with limited human input.
Bayesian interpolants explain neural network inferences concisely.
problem Understanding neural network inferences.
method Adapting Craig interpolants for neural networks.
result Produces precise, understandable explanations.
We introduce the adversarially learned inference (ALI) model, which jointly learns a generation network and an inference network using an adversarial process. The generation network maps samples from stochastic latent variables to the data space while the inference network maps training examples in data space to the sp…
Study compares uncertainty estimation methods for Bayesian Neural Networks.
problem Quality of uncertainty quantification in Bayesian Neural Networks.
method Empirical comparison of 10 inference methods on regression and classification tasks.
result Common inference metrics can be misleading, and methods designed to capture posterior structure do not always produce high-quality approximations.
Active learning reduces spin network inference complexity by 10^6-fold.
problem Difficulty in inferring direct interactions in complex networks.
method Information geometry framework to quantify inference difficulty and information gain from perturbations.
result Designed perturbations reduce sampling complexity by 10^6-fold across various network architectures.
Variational inference improves neural network matrix factorization for stochastic blockmodels.
problem Improving predictive performance of neural network matrix factorization for stochastic blockmodels.
method Construct Bayesian neural networks and fit with variational inference.
result Variational inference can achieve equivalent performance to neural networks on Movielens data.
Tackles network structure inference from time series data using GNN.
problem Inferring network structure from incomplete or no information.
method Gumbel Graph Network (GGN) model for network reconstruction and completion.
result GGN can reconstruct up to 100% network structure and infer missing parts with up to 90% accuracy.
New method for inferring network topology from partial data.
problem Inferring network topology from limited node data.
method Vector autoregressive model and Gaussian mixture algorithm.
result The proposed method converges to the network combination matrix in probability.
Proposes TAGI for efficient Gaussian inference in Bayesian neural networks.
problem Efficient inference in Bayesian neural networks with complex architectures.
method Analytical method for tractable approximate Gaussian inference (TAGI).
result Matches performance of gradient-based methods with O(n) computational complexity.