New method calibrates Gaussian product experts for better predictions.
problem Erratic predictions and uncalibrated uncertainty in Gaussian product experts.
method Calibration via tempered softmax and Wasserstein barycenter for predictions.
result Improved predictions with better mean and uncertainty quantification.
A new method uses a product of experts with Dirichlet variables to approximate complex distributions.
problem Approximating complex distributions with tractable models.
method A product of experts with auxiliary Dirichlet variables, using a Feynman identity to sample and optimize.
result The method efficiently approximates complex distributions using a product of experts and Dirichlet variables.
New method combines FMEA and Bayesian Network for root cause analysis in lithium-ion battery production.
problem Complex cause-effect relationships in lithium-ion battery production.
method Combining FMEA with Bayesian Network to detect and resolve inconsistencies.
result Holistic method builds large-scale cross-process Bayesian Failure Network for root cause analysis.
Adversarial MoE learns category-specific models for product search.
problem Variations in product features and importance across categories.
method Mixture of Experts with adversarial regularization and soft gating constraints.
result Improved clustering of gate output vectors and shared experts among similar categories.
We investigate online classification with paid stochastic experts. Here, before making their prediction, each expert must be paid. The amount that we pay each expert directly influences the accuracy of their prediction through some unknown Lipschitz "productivity" function. In each round, the learner must decide how mu…
In this paper, we derive a novel probabilistic model of boosting as a Product of Experts. We re-derive the boosting algorithm as a greedy incremental model selection procedure which ensures that addition of new experts to the ensemble does not decrease the likelihood of the data. These learning rules lead to a generic …
In this work, we propose a generalized product of experts (gPoE) framework for combining the predictions of multiple probabilistic models. We identify four desirable properties that are important for scalability, expressiveness and robustness, when learning and inferring with a combination of multiple models. Through a…
Product models of low dimensional experts are a powerful way to avoid the curse of dimensionality. We present the ``under-complete product of experts' (UPoE), where each expert models a one dimensional projection of the data. The UPoE is fully tractable and may be interpreted as a parametric probabilistic model for pro…
We present a new approach for learning compact and intuitive distributed representations with binary encoding. Rather than summing up expert votes as in products of experts, we employ for each variable the opinion of the most reliable expert. Data points are hence explained through a partitioning of the variables into …
Improved collaborative filtering with neural network models of reviews.
problem Improve collaborative filtering performance using side information from reviews.
method Introduced two neural network models (product-of-experts and recurrent neural network) to incorporate reviews into collaborative filtering.
result The product-of-experts model achieved state-of-the-art performance, outperforming LDA-based approach.
We introduce a framework for analyzing transductive combination of Gaussian process (GP) experts, where independently trained GP experts are combined in a way that depends on test point location, in order to scale GPs to big data. The framework provides some theoretical justification for the generalized product of GP e…
A new method for sparse Gaussian process regression using correlated experts.
problem Sparse Gaussian process regression for large datasets with cubic computational complexity.
method Aggregating predictions from correlated experts to improve scalability and accuracy.
result Superior performance compared to state-of-the-art methods for synthetic and real-world datasets.
Sharp bounds found on expert error in binary advice aggregation.
problem Aggregating binary advice from conditionally independent experts.
method Sharp upper and lower bounds on optimal error probability in asymmetric case.
result Sharp bounds recover and sharpen known results in symmetric case.
Combining distributed Gaussian Processes with deep CNNs improves performance on action recognition.
problem Improving performance on action recognition datasets.
method Combining distributed Gaussian Processes with multi-stream deep CNNs, treating each CNN as an expert and combining predictions using a Product of Experts (PoE) framework.
result Improves performance on HMDB-51 dataset by 0.4\% compared to hand-crafted feature frameworks.
Improved sales forecasting for new products using transfer learning.
problem Insufficient training data for new products leads to inaccurate sales forecasts.
method Network-based Transfer Learning approach for deep neural networks.
result Deep neural networks' prediction accuracy for food sales forecasting can be effectively increased.
PIMA autoencoders discover shared features in multimodal scientific data.
problem Discovering shared information in high-throughput scientific datasets.
method Physics-informed multimodal autoencoders (PIMA) with Gaussian mixture prior and product of experts formulation.
result Accurate cross-modal inference between images and mechanical stress-strain response in lattice metamaterials.
Hydra boosts efficiency for long-context reasoning in resource-constrained settings.
problem Quadratic complexity of transformers limits long-context reasoning in resource-constrained systems.
method Hydra uses a modular architecture with adaptive routing between sparse global attention, mixture-of-experts, and dual memories.
result Hydra achieves significant throughput and accuracy improvements for long-context reasoning.
Paper improves uncertainty estimation in LLM-as-a-judge systems.
problem Improving uncertainty estimation in LLM-as-a-judge frameworks.
method Generalised probabilistic modelling and improved uncertainty estimates.
result Proposed uncertainty estimates significantly improve system efficiency.
Enhances BO with expert preferences about abstract properties.
problem Lack of expert knowledge in BO for black-box experimental design.
method Human-AI collaboration to incorporate expert preferences into surrogate modeling.
result Superior performance compared to baselines in synthetic and real-world datasets.
Capsules learn from expert neurons using dynamic routing.
problem Training complex multidimensional neural networks.
method Formulated capsule networks as a product of expert neurons with dynamic routing.
result Capsule networks can generate realistic images.
It is well-known to the experts that multi-dimensional state integrals of products of Faddeev's quantum dilogarithm which arise in Quantum Topology can be written as finite sums of products of basic hypergeometric series in q=e^{2πiτ} and \tilde{q}=e^{-2πi/τ}. We illustrate this fact by giving a detailed proof for a fa…
uMoE trains NNs with uncertain data by embedding uncertainty into training.
problem Managing aleatoric uncertainty in NN-based predictive models.
method Divide and Conquer strategy, Expert components, Gating Unit.
result uMoE outperforms baseline methods in uncertainty management.
Unsupervised deep learning is one of the most powerful representation learning techniques. Restricted Boltzman machine, sparse coding, regularized auto-encoders, and convolutional neural networks are pioneering building blocks of deep learning. In this paper, we propose a new building block -- distributed random models…
A novel framework for regression with multiple experts, addressing challenges in infinite and continuous label spaces.
problem Challenges in regression with multiple experts due to the infinite and continuous nature of the label space.
method Introduces a novel framework for regression with deferral, analyzing both single-stage and two-stage scenarios with new surrogate loss functions.
result Proves H-consistency bounds for both single-stage and two-stage methods, providing stronger guarantees than Bayes consistency. Unified sampling approach for Bayesian imaging problems.
problem Sampling from complex prior and posterior distributions in Bayesian imaging.
method Gaussian latent machine model for efficient prior and posterior sampling.
result Unified and generalized sampling algorithms for various imaging problems.
The performance of EM in learning mixtures of product distributions often depends on the initialization. This can be problematic in crowdsourcing and other applications, e.g. when a small number of 'experts' are diluted by a large number of noisy, unreliable participants. We develop a new EM algorithm that is driven by…
The paper provides an algorithm for the risk estimation when a company selects an outsourcing service provider for innovation product. Calculations are based on expert surveys conducted among customers and among providers of outsourcing. The surveys assessed the degree of materiality of species at risk.
Generative Multisensory Network learns 3D scene representations from multiple modalities.
problem Learning robust 3D scene representations from multiple sensory modalities.
method Amortized Product-of-Experts for efficient inference and cross-modal generation.
result The model can infer modality-invariant 3D scene representations efficiently from various sensory modalities.
Gated attention improves performance by using a hierarchical mixture of experts.
problem Improving performance of self-attention mechanisms in Transformers.
method Rigorously show that gated attention can be modeled as a hierarchical mixture of experts, providing a theoretical justification for its benefits.
result Gated attention is more sample-efficient than multi-head self-attention, requiring fewer data points to achieve the same estimation error.
This work improves multi-modal generative models by using permutation-invariant neural networks.
problem Improving multi-modal generative models with tighter variational objectives.
method Developed more flexible aggregation schemes based on permutation-invariant neural networks.
result Our variational objective and flexible aggregation models can better approximate the true joint distribution.
Improved text-conditioned regression using LLMs and diffusion-based neural processes.
problem Major error cascades and computational inefficiency in LLMs for short sequences.
method Combining LLM predictive densities with a diffusion-based neural process.
result Better-calibrated predictions and locally consistent trajectories.
An unsupervised learning algorithm trains capsule networks for generating realistic images.
problem Training capsule networks for generating realistic images without labeled data.
method Developed an unsupervised learning algorithm using dynamic routing and an energy function for capsule networks.
result The algorithm successfully generates realistic looking images from a learned distribution.
Enhanced nearest neighbor improves accuracy in crowdsourced data.
problem Noise in crowdsourced labels degrades classification accuracy.
method Developed two algorithms to estimate worker quality and an enhanced nearest neighbor classifier.
result Proposed methods achieve the same regret as oracle version based on expert data.
Prediction markets show considerable promise for developing flexible mechanisms for machine learning. Here, machine learning markets for multivariate systems are defined, and a utility-based framework is established for their analysis. This differs from the usual approach of defining static betting functions. It is sho…
Enhances early-exit neural networks for anytime classification.
problem Lack of guaranteed prediction quality improvement with longer computation time.
method Post-hoc modification based on Product-of-Experts to enforce conditional monotonicity.
result Achieves conditional monotonicity in prediction quality, enabling anytime classification.
AI agent predicts industry and product/service codes for companies.
problem Manual curation of company data is expensive and prone to errors.
method Hierarchical multi-class industry code classifier with multi-label product/service code classifier.
result High accuracy (92-96%) achieved with limited labeled data.
Demonstration-regularized RL reduces sample complexity for policy identification.
problem Improving reinforcement learning's sample efficiency with expert demonstrations.
method KL-regularization of behavior cloning using expert demonstrations.
result Demonstration-regularized RL achieves optimal policy identification with reduced sample complexity.
The paper reduces BO evaluations using LF data to improve optimization efficiency.
problem High cost of evaluating BOF limits BO applicability.
method Posterior regularization with LF-GP model and DW-POE fusion.
result The proposed algorithm outperforms state-of-the-art BO methods.
This paper uses SPNs with GPs to efficiently model complex data.
problem Inference cost and memory issues in Gaussian processes.
method Integrating Gaussian processes into sum-product networks.
result The model efficiently learns input-dependent parameters and hyper-parameters.
Proposes a FoE prior for improving CNN performance in distribution shifts.
problem Improving CNN performance in image analysis tasks with distribution shifts.
method Uses a field-of-experts (FoE) prior to match feature distributions of test and training images.
result Outperforms previous TTA methods in lesion segmentation and most healthy tissue segmentation tasks.
Slice-based Learning improves model performance on critical data subsets.
problem Low performance on critical data subsets in machine learning models.
method Proposes a new programming model (Slice-based Learning) that uses slicing functions to specify critical data subsets and combines these with an attention mechanism.
result Improves model performance by up to 19.0 F1 on slices and 4.6 F1 overall.
Multi-expert L2D underfits more severely, requiring new methods.
problem Underfitting in multi-expert L2D settings.
method PiCCE (Pick the Confident and Correct Expert), a surrogate-based method.
result PiCCE effectively reduces multi-expert L2D to a single-expert-like problem, resolving underfitting.
Automated Bayesian inference for massive datasets with theoretical guarantees.
problem Intractable posterior inference in massive datasets.
method Hilbert coreset construction under log-likelihood space inner-product norm.
result Fully-automated, scalable Bayesian inference with theoretical guarantees.
Generates sutured manifolds from a surface using surgery.
problem Creating sutured manifolds from a given surface.
method Using surgery triads to generate sutured manifolds.
result The set of sutured manifolds is generated by a specific manifold.
Algorithm learns data structure in real-time with outliers and change points.
problem Sequential online prediction in the presence of outliers and change points.
method INTEL algorithm using WGPs and POE model for real-time structure learning.
result Significantly better performance than benchmarks in real datasets.
TENP prunes experts and neurons in Mixture-of-Experts models for efficient deployment.
problem Efficient deployment of large language models constrained by static parameter footprint.
method Structured Trapezoidal ExpertNeuron Pruning (TENP) identifies and retains important experts and neurons.
result DeepSeek model achieves 10% better performance on code generation tasks with 40% expert sparsity.
A method to select important experts for Gaussian processes to balance computational efficiency and uncertainty quantification.
problem Balancing computational efficiency and uncertainty quantification in Gaussian processes for big data.
method Using graphical models to select important experts and aggregate their predictions while ensuring uncertainty quantification.
result Substantially reduces computational cost of aggregating dependent experts while ensuring calibrated uncertainty quantification.
New method for disaggregate electricity demand forecasting at household level.
problem Challenges in forecasting electricity demand at individual household level.
method Additive stacking method for probabilistic disaggregate electricity demand forecasting.
result Improved accuracy in disaggregate electricity demand forecasting.