Right Problem

"Right Problem" research focuses on identifying and effectively addressing the core challenges within various problem domains, ensuring that solutions are tailored to the specific needs and constraints of the problem. Current research emphasizes developing and evaluating appropriate performance metrics, employing advanced algorithms like deep learning and reinforcement learning to solve complex problems in diverse fields such as image analysis, robotics, and natural language processing. This work is crucial for advancing scientific understanding and improving the efficacy of practical applications by ensuring that computational resources are directed towards the most impactful and relevant aspects of a given problem.

Papers

April 6, 2022

Standardized feature extraction from pairwise conflicts applied to the train rescheduling problem
Anikó Kopacz, Ágnes Mester, Sándor Kolumbán, Lehel Csató
Reinforcement Learning Feature Selection Right Problem High Speed Train System Scheduling Policy

March 30, 2022

January 21, 2022

Can Machines Generate Personalized Music? A Hybrid Favorite-aware Method for User Preference Music Transfer
Zhejing Hu, Yan Liu, Gong Chen, Yongxu Liu
Style Transfer Efficient Hybrid Multi Scenario Right Problem

January 14, 2022

December 25, 2021

Stance Quantification: Definition of the Problem
Dilek Küçük
Stance Detection Right Problem Definition Defining Redefinition

November 28, 2021

Neural Symplectic Integrator with Hamiltonian Inductive Bias for the Gravitational $N$-body Problem
Maxwell X. Cai, Simon Portegies Zwart, Damian Podareanu
Neural Network Right Problem Hamiltonian Learning N$ Body

November 24, 2021

Dictionary-based Low-Rank Approximations and the Mixed Sparse Coding problem
Jeremy E. Cohen
Matrix Factorization Average Approximation Right Problem Low Rank Approximation Sparse Coding

November 11, 2021

On the Problem of Reformulating Systems with Uncertain Dynamics as a Stochastic Differential Equation
Thomas Lew, Apoorva Sharma, James Harrison, Edward Schmerling, Marco Pavone
Learning Based Epistemic Uncertainty Right Problem Aleatoric Uncertainty Uncertain Dynamic Exploratory Reformulation

December 2, 2020

The temporal overfitting problem with applications in wind power curve modeling
Abhinav Prakash, Rui Tuo, Yu Ding
Financial Application Gaussian Process Time Varying Right Problem Temporal Correlation Training Domain Temporal Learning Power Curve Model

Right Problem

Papers

Standardized feature extraction from pairwise conflicts applied to the train rescheduling problem

Unseen Classes at a Later Time? No Problem

Contribution of the Temperature of the Objects to the Problem of Thermal Imaging Focusing

Can Machines Generate Personalized Music? A Hybrid Favorite-aware Method for User Preference Music Transfer

Active Predictive Coding Networks: A Neural Solution to the Problem of Learning Reference Frames and Part-Whole Hierarchies

Agile Effort Estimation: Have We Solved the Problem Yet? Insights From A Replication Study

Stance Quantification: Definition of the Problem

Neural Symplectic Integrator with Hamiltonian Inductive Bias for the Gravitational $N$-body Problem

Dictionary-based Low-Rank Approximations and the Mixed Sparse Coding problem

On the Problem of Reformulating Systems with Uncertain Dynamics as a Stochastic Differential Equation

The temporal overfitting problem with applications in wind power curve modeling