PCWorld reports on Windows 11's optional update KB5089573, the first release from Microsoft's secret Project K2 initiative aimed at improving performance through 2027. The update introduces a Low Latency Profile that boosts CPU speed for important tasks, delivering up to 70% faster flyouts and 40% quicker app launches. Users can manually install this preview update via Windows Update or Microsoft Update Catalog for immediate performance gains. Detailed instructions are provided below. Microsoft is following through on a previous announcement with a new optional update for Windows 11 that makes the operating system genuinely faster, especially in certain situations. It's called update KB5089573 and it includes the "Low Latency Profile" feature.

Transfer in reinforcement learning aims at solving a new target task with no additional learning or sample-efficiently by exploiting agents and information obtained from source tasks. We review a line of research with relevant approaches. This group of approaches reuses policies learned on source tasks for target tasks. Fernández and Veloso [17] suggest an exploration strategy for the learning of a new policy given a new task and learned source policies, where the gain of using each policy is estimated together on-line and one of the policies in the set is selected probabilistically at each step, based on the gain, but they focus on aiding the training of the target policy with samples from the target task rather than improving the zero-shot transfer performance. On the other hand, Dayan [14] introduce successor representations (SRs), state space occupancy representations disentangled from rewards, which allow linear decomposition of value functions.

As performance gains through scaling data and/or model size experience diminishing returns, it is becoming increasingly popular to turn to ensembling, where the predictions of multiple models are combined to improve accuracy. In this paper, we provide a detailed analysis of how the disagreement and the polarization (a notion we introduce and define in this paper) among classifiers relate to the performance gain achieved by aggregating individual classifiers, for majority vote strategies in classification tasks.We address these questions in the following ways.

In this way, even with limited meta samples, MTG-Net holds the potential to produce reasonable gain estimations on arbitrary task combinations.

Wetherefore startbydelving deeper intoatop-performing frameworkandshow evidence that harder negatives are needed to facilitate better and faster learning.