A framework for improving localisation prediction algorithms. حسب Sven B. Gould (12237287)

Impact of multiunit merging on neural information encoding. حسب Daniel Suárez-Barrera (22676654)

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Dataset for Fine-Tuning Code Generation Models: Kannada-English Algorithmic Statements and Python Code حسب Goutami Sooda (20714729)

(a) Proposed dynamic low RF-complexity DBF architecture. حسب Usman Haider (12630481)

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A chart of associated parameters, along with various other miscellaneous parameters [39]. حسب Usman Haider (12630481)

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Flowchart of the study. حسب Usman Haider (12630481)

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Number of selected antennas per group, NLOS street canyon scenario. حسب Usman Haider (12630481)

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Channel capacity of MIMO system with variable . حسب Usman Haider (12630481)

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Channel capacity of MIMO system with NLOS street canyon scenario. حسب Usman Haider (12630481)

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Channel capacity of MIMO system with LOS street canyon scenario. حسب Usman Haider (12630481)

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Probability of LOS models and average transmit correlation analysis for MIMO system in street canyon and shopping mall scenarios, (a) Probability of LOS models. حسب Usman Haider (12630481)

الموضوعات:

Statistical analysis of the (a) Percentage of several subarrays formed (same as the required number of RF chains). حسب Usman Haider (12630481)

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Number of selected antennas per group, LOS street canyon scenario. حسب Usman Haider (12630481)

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Data and code resources. حسب Sam Hall-McMaster (10343795)

The parameter setting of the evolutionary neural network. حسب Wenbing Shi (5806160)

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The genome coding scheme. حسب Wenbing Shi (5806160)

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On the Algorithmic Bias of Aligning Large Language Models with RLHF: Preference Collapse and Matching Regularization حسب Jiancong Xiao (22265935)

"…<p>Accurately aligning large language models (LLMs) with human preferences is crucial for informing fair, economically sound, and statistically efficient decision-making processes. However, we argue that the predominant approach for aligning LLMs with human preferences through a reward model—reinforcement learning from human feedback (RLHF)—suffers from an inherent algorithmic bias due to its Kullback–Leibler-based regularization in optimization. …"

Research data for paper: Efficient Event-based Delay Learning in Spiking Neural Networks حسب Balázs Mészáros (16890225)

"…Our method supports multiple spikes per neuron and introduces a delay learning algorithm that can, in contrast to previous methods, also be applied to recurrent Spiking Neural Networks. …"

Uniform Manifold Approximation and Projection (UMAP)-based sorting enhances detection of low-firing rate neurons and boosts mutual information. حسب Daniel Suárez-Barrera (22676654)

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Comparative analysis of spike sorting using Uniform Manifold Approximation and Projection (UMAP) vs. feature-based methods in in vivo extracellular recordings. حسب Daniel Suárez-Barrera (22676654)

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