Settings-free optimizationSelf-tuning algorithmsAmong the existing global optimization algorithms, Particle Swarm Optimization (PSO) is one of the most effective methods for non-linear and complex high-dimensio
{Kaixuan Wei and Angelica Aviles-Rivero and Jingwei Liang and Ying Fu and Hua Huang and Carola-Bibiane Sch{\"o}nlieb},title={TFPnP: Tuning-free Plug-and-Play Proximal Algorithms with Applications to Inverse Imaging Problems},journal={Journal of Machine Learning Research},year={2022},volume=...
Learn how optimization algorithms, like genetic algorithms and pattern search, can efficiently tune the parameters. Follow along with an example about tuning a fuzzy inference system using data that controls an artificial pancreas. Show more Published: 4 Oct 2021...
Advanced algorithms: GaLore, BAdam, APOLLO, Adam-mini, Muon, DoRA, LongLoRA, LLaMA Pro, Mixture-of-Depths, LoRA+, LoftQ and PiSSA. Practical tricks: FlashAttention-2, Unsloth, Liger Kernel, RoPE scaling, NEFTune and rsLoRA. Wide tasks: Multi-turn dialogue, tool using, image understanding...
Beginning with the J2SE Platform version 1.2, the virtual machine incorporated a number of different garbage collection algorithms that are combined usinggenerational collection. While naive garbage collection examines every live object in the heap, generational collection exploits several empirically observed...
In this work we examine how fine-tuning impacts the fairness of contrastive Self-Supervised Learning (SSL) models. Our findings indicate that Batch Normalization (BN) statistics play a crucial role, and that updating only the BN statistics of a pre-trained SSL backbone improves its downstream fai...
Algorithms When you tune a control system using aTuningGoal, the software converts the tuning goal into a normalized scalar valuef(x).xis the vector of free (tunable) parameters in the control system. The software then adjusts the parameter values to minimizef(x) or to drivef(x) below ...
Derivative-free optimization, as the name suggests, is a branch of mathematical optimization for situations where there is no derivative information. Notable derivative-free methods include genetic algorithms and the Nelder-Mead method. Essentially, the algorithms boil down to the following: try a bunc...
CarbonData uses a few lightweight compression and heavyweight compression algorithms to compress data. Although these algorithms can process any type of data, the compression performance is better if the data is ordered with similar values being together. During data loading, data is sorted based on...
The GPU Accelerator employs different algorithms that allow it to process more data than can fit in the GPU’s memory. We do not support this for all operations, and are constantly trying to add more. The way that this can work is by spilling parts of the data to host memory or to ...