Uncertainty estimation is crucial for machine learning models to detect out-of-distribution (OOD) inputs. However, the conventional discriminative deep learning classifiers produce uncalibrated closed-set predictions for OOD data. A more robust class...
Predicting the S&P 500 index volatility is crucial for investors and financial analysts as it helps assess market risk and make informed investment decisions. Volatility represents the level of uncertainty or risk related to the size of changes in a...
The international database community refers to the manipulation of data with inaccuracy and uncertainty using the term fuzzy, which has been translated into Spanish as "borroso" and into French as "flou". Semantically, this term conveys two main idea...
Galaxy clusters are important cosmological probes since their abundance and spatial distribution are directly linked to structure formation on large scales. The principal uncertainty source on the cosmological parameter constraints concerns the clust...
President Donald Trump’s announcement of a “massive and ongoing” US military campaign against Iran — and his explicit call for the country’s citizens to shake off their oppressive leadership — put on display his fresh appetite for geopolitical risk and thrust his presidency into…
This paper discusses about future challenges in terms of big data and new technologies. Utilities have been collecting data in large amounts but they are hardly utilized because they are huge in amount and also there is uncertainty associated with it...
Momentum-based optimizers are widely adopted for training neural networks. However, the optimal selection of momentum coefficients remains elusive. This uncertainty impedes a clear understanding of the role of momentum in stochastic gradient methods....
Coral reefs are increasingly subjected to major disturbances threatening the health of marine ecosystems. Substantial research underway to develop intervention strategies that assist reefs in recovery from, and resistance to, inevitable future climat...
We propose a novel robust composition framework for drone delivery services considering changes in the wind patterns in urban areas. The proposed framework incorporates the dynamic arrival of drone services at the recharging stations. We propose a Pr...
Organizations around the world schedule jobs (programs) regularly to perform various tasks dictated by their end users. With the major movement towards using a cloud computing infrastructure, our organization follows a hybrid approach with both cloud...
Full quote by an NBA exec to HoopsHype: > “The Raptors as a team have been battling injuries and lineup uncertainty all year long, but Scottie Barnes has been one of the few constants for them a...
With so much uncertainty right now it would be a good time to take some time to go over what happened recently and how to invest moving foward. We've seen a peak bubble at around 850 billion total mar...
The integration of large-scale renewable generation has major implications on the operation of power systems, two of which we address in this work. First, system operators have to deal with higher degrees of uncertainty due to forecast errors and var...
Jan 7, 2025 · Technological change, geoeconomic fragmentation, economic uncertainty, demographic shifts and the green transition – individually and in combination are among the major drivers …
Evidential deep learning, built upon belief theory and subjective logic, offers a principled and computationally efficient way to turn a deterministic neural network uncertainty-aware. The resultant evidential models can quantify fine-grained uncerta...
Many large scale problems in computational fluid dynamics such as uncertainty quantification, Bayesian inversion, data assimilation and PDE constrained optimization are considered very challenging computationally as they require a large number of exp...
Social media and other platforms rely on automated detection of abusive content to help combat disinformation, harassment, and abuse. One common approach is to check user content for similarity against a server-side database of problematic items. How...
The secretary problem is probably the purest model of decision making under uncertainty. In this paper we ask which advice can we give the algorithm to improve its success probability? We propose a general model that unifies a broad range of proble...
Camera rotation estimation from a single image is a challenging task, often requiring depth data and/or camera intrinsics, which are generally not available for in-the-wild videos. Although external sensors such as inertial measurement units (IMUs) c...
She described a "feeling of uncertainty" in dealing with her stepdaughters, particularly in challenging situations.