Autoencoder neural networks reconstruct missing indoor environment data.
problem Missing data in building operation data sets.
method Three different autoencoder neural networks trained to reconstruct missing data.
result Reconstructing variables with average RMSEs of 0.42 °C, 1.30 % and 78.41 ppm.
Safe Bayesian optimization reduces HVAC costs by 32%.
problem Optimizing room temperature PID control for energy savings and comfort.
method Safe Contextual Bayesian Optimization.
result 32% reduction in room temperature PID control costs.
Paper presents a method to detect out-of-distribution spectra in intra-operative functional imaging.
problem Detecting out-of-distribution (OoD) spectra in multispectral optical imaging during surgery.
method Information theory-based approach using WAIC with an ensemble of INNs.
result The method is effective in detecting OoD spectra, improving the reliability of functional imaging.
Paper combines LSTM and XGB for better hypoxemia prediction.
problem Forecasting hypoxemia in operating rooms using time series data.
method Feature learning via LSTM networks and prediction via gradient boosting trees.
result Augmenting XGB with LSTM features improves prediction performance.
F-GAM improves clinical prediction models for OR outcomes.
problem Limited expressive capability of logistic regression for clinical predictions.
method Factored generalized additive model (F-GAM) that extends GAM with feature interactions.
result F-GAM outperforms other models in AUPRC and AUROC for predicting OR outcomes.
Study predicts room occupancy using machine learning, achieving high accuracy.
problem Efficient room occupancy management for energy conservation.
method Leveraged diverse machine learning models, focusing on Random Forest.
result Random Forest model achieved highest predictive accuracy.
Quantum computing at room temperature achieves high accuracy in image classification.
problem Classifying images with single photons at room temperature.
method Optical transformation of quantum state to exploit interference and entanglement.
result Theoretical accuracy of 41.27% for MNIST and 36.14% for Fashion-MNIST.
The paper optimizes air conditioning setpoints using machine learning.
problem Static setpoints waste energy and increase costs.
method Deep-learning model based on RNNs for predicting future temperatures.
result RNNs outperform state-of-the-art models in prediction accuracy.
Cyber-physical systems, such as mobile robots, must respond adaptively to dynamic operating conditions. Effective operation of these systems requires that sensing and actuation tasks are performed in a timely manner. Additionally, execution of mission specific tasks such as imaging a room must be balanced against the n…
Dataset of 50k hotels aids in human trafficking investigations.
problem Identify hotels from low-quality, occluded images.
method Curated dataset of 1M annotated hotel room images, baseline model with data augmentation.
result Demonstrated the feasibility of hotel recognition from challenging images.
New method considers subjectivity in text analysis using 'Room Theory'.
problem Detecting emotions in text considering subjective context.
method Framework Theory, Word2Vec, similarity measure between words.
result Measures relative relevance of emotions for a document.
Deep learning predicts hypoxemia from SpO2 data better than anesthesiologists.
problem Improving hypoxemia prediction for better patient outcomes.
method Trained deep learning model on SpO2 data to predict hypoxemia.
result Deep learning model outperforms anesthesiologists in predicting hypoxemia.
A new graph pooling method using self-attention improves graph classification performance.
problem Challenges in applying downsampling to graphs in graph neural networks.
method Proposes a self-attention-based graph pooling method.
result Our method achieves superior graph classification performance on benchmark datasets.
Proposes a flexible deep learning model for complex distributions.
problem Complex shapes, strong skews, and multiple modes in output variable distributions.
method Uncountable Mixture of Asymmetric Laplacians (UMAL) deep learning framework.
result UMAL can estimate heterogeneous distributions without strong assumptions.
Paper tackles robust offline RL with heavy-tailed rewards.
problem Real-world applications often encounter heavy-tailed rewards, challenging offline RL.
method Proposes ROAM and ROOM algorithms using median-of-means method for robust off-policy evaluation and OPO.
result Demonstrates superior performance on heavy-tailed reward datasets compared to existing methods.
CSAC enables cooperative reinforcement learning for multi-stage tasks.
problem Coordinating consecutive reinforcement learning agents for long-term multi-stage tasks.
method CSAC modifies each agent's policy to maximize both current and next agent's critic.
result CSAC outperforms uncooperative policies and single-agent training in multi-room maze domain.
Bing's house-like spines approximate all PL manifolds.
problem Understanding spines of PL manifolds.
method Approximation rigidity and h-principle.
result PL manifolds have spines similar to Bing's house.
The paper proposes a method to align surgical videos using kinematic data.
problem Difficulty in learning from comparing novice to expert surgical videos due to variability in gesture duration and execution.
method A novel technique using Dynamic Time Warping to synchronize videos of the same gesture at different speeds.
result The proposed approach allows for the alignment of surgical videos, enabling better learning for novice trainees.
We consider the problem of learning object arrangements in a 3D scene. The key idea here is to learn how objects relate to human poses based on their affordances, ease of use and reachability. In contrast to modeling object-object relationships, modeling human-object relationships scales linearly in the number of objec…
Study on packing links with geometric constraints.
problem Maximizing link density in space with geometric restrictions.
method Investigates packing essential links within Euclidean space.
result Upper bounds on maximal density are found, but are large.
New method for efficient uncertainty quantification in DeepONets.
problem Efficient uncertainty quantification for DeepONets with limited and noisy data.
method Ensemble Kalman Inversion (EKI) for ensembles of DeepONets.
result Improved uncertainty estimates for DeepONet predictions.
OtoWorld helps agents learn to navigate by listening in interactive environments.
problem Training agents to navigate using auditory information.
method Interactive environment with agents learning to listen and navigate.
result Agents can win at OtoWorld, a navigation game with sound sources.
Quantum kernel machines need to use more complex kernels to fully exploit their potential.
problem Current quantum kernels struggle with complex learning tasks due to limited degrees of freedom.
method Propose using operator-valued kernels and C ∗ C^* C ∗ -algebraic representations to enhance quantum kernels. result Quantum operator-valued kernels can reveal structural dependencies that scalar-valued kernels miss.
PriceAggregator optimizes hotel price fetching to increase Agoda's bookings.
problem Limited QPS from suppliers causes many user searches to be ignored.
method Intelligently determines queries to suppliers for price fetching.
result PriceAggregator increases Agoda's bookings significantly.
Framework uses human judgment to distinguish algorithmically indistinguishable cases.
problem Clarifying human-AI collaboration in prediction and decision tasks.
method Integrates human judgment to distinguish algorithmically indistinguishable cases.
result Improves performance of any feasible algorithmic predictor.
EUREKA builds classifiers that use surprising features.
problem Building classifiers that are interesting, not just accurate.
method Uses large language models to rank features by interestingness and builds interpretable classifiers using only selected features.
result EUREKA discovers non-obvious yet predictive features, improving accuracy and offering insights.
Energy-efficient sampling for machine learning using magnetic tunnel junctions.
problem Costly and inefficient random sampling in machine learning.
method Energy-efficient algorithm using stochastic magnetic tunnel junctions for uniform Float16 sampling.
result Higher energy efficiency than state-of-the-art algorithms, with a minimum factor of 9721.
Study optimizes pricing under uncertainty and capacity constraints.
problem Optimizing pricing decisions under demand uncertainty and capacity constraints.
method Analyzes linear demand, stochastic noise, and finite capacity; uses certified demand forecasts and control variates.
result Certified demand forecasts reduce regret from O ( T ) O(\sqrt{T}) O ( T ) to O ( log T ) O(\log T) O ( log T ) under certain conditions. Ray marching method visualizes flat surfaces efficiently.
problem Efficient visualization of flat surfaces.
method Ray marching approach for intuitive exploration.
result Effective visualization of translation surfaces and polyhedra.
We study the fundamental limits to communication-efficient distributed methods for convex learning and optimization, under different assumptions on the information available to individual machines, and the types of functions considered. We identify cases where existing algorithms are already worst-case optimal, as well…
DDSP integrates signal processing with deep learning for high-fidelity audio synthesis.
problem Efficiently combining signal processing knowledge with deep learning for audio synthesis.
method Integrates classic signal processing elements with deep learning methods.
result High-fidelity audio synthesis without large models or adversarial losses.
This paper develops a method to estimate the rate-distortion function for general data sources.
problem Estimating the rate-distortion function for general data sources.
method Develops an algorithm for sandwiching the R-D function of a general (not necessarily discrete) source using i.i.d. data samples.
result Estimates R-D sandwich bounds for various data sources, including natural images, indicating potential for improving compression methods.
PHASE predicts surgical complications from physiological signals.
problem Predicting adverse surgical outcomes from physiological signals.
method Self-supervised transfer learning for physiological signals.
result PHASE outperforms other approaches in predicting five surgical complications.
New method segments power grids in real time for easier control.
problem Segmenting large-scale power grids for real-time control.
method Guided machine learning approach with Influence Graph and community detection.
result Promising and original interpretable results on various grids.
Dataset of human-written problem statements and solutions for program synthesis.
problem Creating programs from natural language problem descriptions.
method Crowdsourced problem statements and solutions from programming competitions.
result Best model achieved 8.8% accuracy, indicating high complexity.
HCFContext predicts mobile context using collaborative filtering and homomorphic encryption.
problem Accurate mobile context determination for enterprise policies.
method Proposes HPContext and HCFContext models using sequential history and collaborative filtering, with privacy-preserving homomorphic encryption.
result HCFContext enhances context prediction by leveraging related users' observations.
BCIQT model improves ML prediction effectiveness using quantum theory.
problem Improving prediction effectiveness in machine learning models.
method Proposes Binary Classifier Inspired by Quantum Theory (BCIQT) model.
result BCIQT model outperforms state-of-the-art models in recall.
Models predict drug interactions with high accuracy.
problem Detecting drug-drug interactions to prevent medical injuries.
method Artificial neural networks and graph similarity measures.
result Models achieve high accuracy in predicting drug interactions.
Study compares distances for indoor WiFi mapping, finding Earth Mover's Distance effective.
problem Indoor localization and mapping using WiFi signals.
method Comparison of distance measures and kernel density estimation.
result Earth Mover's Distance is most beneficial for indoor localization.
This paper tightens bounds on linear regions in piecewise linear neural networks.
problem Understanding the expressiveness of piecewise linear neural networks.
method Analyzes the maximal number of linear regions in single and multi-layer PLNNs, and rectifier networks.
result Derives tighter bounds on the maximal number of linear regions in PLNNs and rectifier networks.
Convolutional neural networks improve surgical skill evaluation.
problem Manual feedback from senior surgeons is laborious, expensive, and subjective.
method Fully convolutional neural networks (CNNs) for classifying and regressing surgical skills.
result Deep neural networks achieve competitive performance on JIGSAWS dataset.
Paper compares modern regression methods for time series data.
problem Regression analysis of time series data with time-indexed predictors.
method Classical statistical and recent machine learning approaches compared.
result Advantages and disadvantages of current methods identified.
RSHT algorithm simplifies complex shapes to points.
problem Simplifying complex shapes to points in higher dimensions.
method Combines simplicial collapses and expansions.
result Reduces triangulated d-manifolds to points using RSHT.
Method learns reusable actions from human demonstrations.
problem Learning reusable actions from human demonstrations.
method Skill segmentation, inverse reinforcement learning, one-class support vector machine.
result Inferred options improve learning and planning.
By restricting the iterate on a nonlinear manifold, the recently proposed Riemannian optimization methods prove to be both efficient and effective in low rank tensor completion problems. However, existing methods fail to exploit the easily accessible side information, due to their format mismatch. Consequently, there i…
Novel framework assesses optical imaging hardware uncertainties.
problem Uncertainty in optical imaging modalities, especially ambiguity in parameter estimation.
method Invertible neural networks to map multispectral measurements to posterior probability distributions.
result Ambiguity in blood volume fraction estimation is a key finding.
Joslim optimizes both width and weight configurations for slimmable neural networks, improving model efficiency.
problem Optimizing both width and weight configurations for slimmable neural networks to improve efficiency.
method Proposes a general framework for joint optimization of width configurations and weights, and introduces Joslim algorithm.
result Improves model efficiency by up to 1.7% in top-1 accuracy on the ImageNet dataset.
Green stocks show less factor exposure heterogeneity compared to brown stocks.
problem Exploring differences in factor exposure between green and brown stocks.
method Examined S&P 500 firms grouped by greenhouse gas emissions, analyzing factor exposure over 2014-2020.
result Green stocks have less factor exposure heterogeneity than brown stocks, except for the value factor.