Benchmarks
Greatest papers with code
COCO: A Platform for Comparing Continuous Optimizers in a Black-Box Setting
We introduce COCO, an open source platform for Comparing Continuous Optimizers in a black-box setting.
Efficient Continuous Pareto Exploration in Multi-Task Learning
ICML 2020
• mit-gfx/ContinuousParetoMTL
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We present a novel, efficient method that generates locally continuous Pareto sets and Pareto fronts, which opens up the possibility of continuous analysis of Pareto optimal solutions in machine learning problems.
Learning the Pareto Front with Hypernetworks
ICLR 2021
• AvivNavon/pareto-hypernetworks
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Here, we tackle the problem of learning the entire Pareto front, with the capability of selecting a desired operating point on the front after training.
FAIRNESS MULTIOBJECTIVE OPTIMIZATION MULTI-TARGET REGRESSION MULTI-TASK LEARNING SEMANTIC SEGMENTATION
Pareto Multi-Task Learning
NeurIPS 2019
• Xi-L/ParetoMTL
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Recently, a novel method is proposed to find one single Pareto optimal solution with good trade-off among different tasks by casting multi-task learning as multiobjective optimization.
Multiobjective Optimization Training of PLDA for Speaker Verification
Most current state-of-the-art text-independent speaker verification systems take probabilistic linear discriminant analysis (PLDA) as their backend classifiers.
MULTIOBJECTIVE OPTIMIZATION TEXT-INDEPENDENT SPEAKER VERIFICATION
Max-value Entropy Search for Multi-Objective Bayesian Optimization
We consider the problem of multi-objective (MO) blackbox optimization using expensive function evaluations, where the goal is to approximate the true Pareto-set of solutions by minimizing the number of function evaluations.
BAYESIAN OPTIMISATION GLOBAL OPTIMIZATION MULTIOBJECTIVE OPTIMIZATION
ALWANN: Automatic Layer-Wise Approximation of Deep Neural Network Accelerators without Retraining
A suitable approximate multiplier is then selected for each computing element from a library of approximate multipliers in such a way that (i) one approximate multiplier serves several layers, and (ii) the overall classification error and energy consumption are minimized.
Efficient and Sparse Neural Networks by Pruning Weights in a Multiobjective Learning Approach
• malena1906/Pruning-Weights-with-Biobjective-Optimization-Keras
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We suggest a multiobjective perspective on the training of neural networks by treating its prediction accuracy and the network complexity as two individual objective functions in a biobjective optimization problem.
How to Evaluate Solutions in Pareto-based Search-Based Software Engineering? A Critical Review and Methodological Guidance
We then conduct an in-depth analysis of quality evaluation indicators/methods and general situations in SBSE, which, together with the identified issues, enables us to codify a methodological guidance for selecting and using evaluation methods in different SBSE scenarios.
Pareto-optimal data compression for binary classification tasks
The goal of lossy data compression is to reduce the storage cost of a data set $X$ while retaining as much information as possible about something ($Y$) that you care about.