Learning Near-optimal Decision Rules for Energy Efficient Building Control

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This study integrates deep learning and geospatial analysis to enhance soil loss estimation in the Moulouya Watershed, a region prone to erosion due to diverse topography and climatic conditions. Traditional models like the Universal Soil Loss Equation (US ...

Earth scientists study a variety of problems with remote sensing data, but they most often consider them in isolation from each other, which limits information flows across disciplines. In this work, we present METEOR, a meta-learning methodology for Earth ...

Automatic speech recognition (ASR) systems are well known to perform poorly on dysarthric speech. Previous works have addressed this by speaking rate modification to reduce the mismatch with typical speech. Unfortunately, these approaches rely on transcrib ...

Modern optimization is tasked with handling applications of increasingly large scale, chiefly due to the massive amounts of widely available data and the ever-growing reach of Machine Learning. Consequently, this area of research is under steady pressure t ...

Scientific Machine Learning (SciML) is a recently emerged research field which combines physics-based and data-driven models for the numerical approximation of differential problems. Physics-based models rely on the physical understanding of the problem at ...

The remarkable ability of deep learning (DL) models to approximate high-dimensional functions from samples has sparked a revolution across numerous scientific and industrial domains that cannot be overemphasized. In sensitive applications, the good perform ...

Self-supervised pretrained models exhibit competitive performance in automatic speech recognition (ASR) on finetuning, even with limited in-domain supervised data. However, popular pretrained models are not suitable for streaming ASR because they are train ...

Jointly estimating hand and object shape facilitates the grasping task in human-to-robot handovers. Relying on handcrafted prior knowledge about the geometric structure of the object fails when generalising to unseen objects, and depth sensors fail to dete ...

Informative sample selection in an active learning (AL) setting helps a machine learning system attain optimum performance with minimum labeled samples, thus reducing annotation costs and boosting performance of computer-aided diagnosis systems in the pres ...

Fuel performance codes are essential for predicting fuel rod behavior in nuclear reactors. These codes comprise a complex system of models with empirical constants that require calibration against experimental data. To support this calibration, this study ...

Healthcare sector buildings, particularly hospitals, significantly contribute to global energy consumption and greenhouse gas emissions. Hospitals require a substantial amount of steam, including the essential hygienic or " clean steam, " that is currently ...

Synthesizability in generative molecular design remains a pressing challenge. Existing methods to assess synthesizability include heuristics-based metrics or retrosynthesis models which predict a synthetic pathway. By contrast, an explicit approach anchors ...

While momentum-based accelerated variants of stochastic gradient descent (SGD) are widely used when training machine learning models, there is little theoretical understanding on the generalization error of such methods. In this work, we first show that th ...

Photorealistic synthetic data and novel rendering techniques significantly advanced computer vision research. However, datasets focused on computer vision applications cannot be easily applied to robotics because they typically lack physics-related informa ...

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Faithful human performance capture and free-view rendering from sparse RGB observations is a long-standing problem in Vision and Graphics. The main challenges are the lack of observations and the inherent ambiguities of the setting, e.g. occlusions and dep ...

A range of behavioral and contextual factors, including eating and drinking behavior, mood, social context, and other daily activities, can significantly impact an individual's quality of life and overall well-being. Therefore, inferring everyday life aspe ...

Interactive simulations enhance science education and foster inquiry skills, but their open-ended nature can be cognitively overloading. While adaptive systems offer timely support, research on predicting conceptual understanding in these environments is l ...

Macroscopic fundamental diagrams (MFDs) have been widely adopted to model the traffic flow of large-scale urban networks. Coupling perimeter control and regional route guidance (PCRG) is a promising strategy to decrease congestion heterogeneity and reduce ...

Image steganalysis, detecting hidden data in digital images, is essential for enhancing digital security. Traditional steganalysis methods typically rely on large, pre-labeled image datasets, which are difficult and costly to compile. To address this, this ...

Learning Near-optimal Decision Rules for Energy Efficient Building Control

Chat with Graph Search

Application of Deep Learning and Geospatial Analysis in Soil Loss Risk in the Moulouya Watershed, Morocco

Meta-learning to address diverse Earth observation problems across resolutions

Unsupervised Rhythm and Voice Conversion of Dysarthric to Healthy Speech for ASR

Scalable constrained optimization

Combining physics-based and data-driven models: advancing the frontiers of research with scientific machine learning

Towards Trustworthy Deep Learning for Image Reconstruction

XLSR-Transducer: Streaming ASR for Self-Supervised Pretrained Models

Stereo Hand-Object Reconstruction for Human-to-Robot Handover

GANDALF: Graph-based transformer and Data Augmentation Active Learning Framework with interpretable features for multi-label chest Xray classification

Preliminary Sensitivity Analysis of Time-Dependent Code Outputs in Fuel Performance Models Using OFFBEAT for Future Calibration

Solar-assisted clean steam generator, a cleaner production approach for sustainable healthcare facilities

Directly optimizing for synthesizability in generative molecular design using retrosynthesis models

On the Generalization of Stochastic Gradient Descent with Momentum

GRADE: Generating Realistic and Dynamic Environments for robotics research with Isaac Sim

Seeking the new, learning from the unexpected: Computational models of surprise and novelty in the brain

MetaCap: Meta-learning Priors from Multi-view Imagery for Sparse-View Human Performance Capture and Rendering

Generalization and Personalization of Machine Learning for Multimodal Mobile Sensing in Everyday Life

One Code to Predict Them All: Universal Encoding for Inquiry Modeling

An Iterative Adaptive Dynamic Programming Approach for Macroscopic Fundamental Diagram-Based Perimeter Control and Route Guidance

Image steganalysis using active learning and hyperparameter optimization