Technology
BMRS: Bayesian Model Reduction for Structured Pruning
In this work, we address this challenge by proposing BMRS: B ayesian M odel R eduction for S tructured pruning.
A Evaluation Tasks 450 Table 4 lists the tasks in our evaluation, and examples from each task
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SMART: Towards Pre-trained Missing-Aware Model for Patient Health Status Prediction Zhihao Y u
Electronic health record (EHR) data has emerged as a valuable resource for analyzing patient health status.
Supplementary Material A Experimentation Details
A.1 Source code Upon request, we will provide an anonymized version of our code in the rebuttal. We replicated our experiments using the codebase provided by Shah et al. [ 2022 ], which can be found at github . To ensure consistency, we used the same hyperparameters as mentioned in the code or article for the baselines. This helps ensure the stability of metric learning. We initialize the parameters in such a way that the predicted metric is close to the Euclidean metric.
Training Binary Neural Networks via Gaussian Variational Inference and Low-Rank Semidefinite Programming
IoT devices, where limitations in memory, processing power and energy are critical [Sze et al., 2017]. To address these issues, Muller and Indiveri [2015] and Courbariaux et al. [2015b] noted that
LMC: Large Model Collaboration with Cross-assessment for Training-Free Open-Set Object Recognition (Supplementary Material)
In Figure 1, we compare our LMC framework with the baseline Softmax, and present qualitative results on the TinyImageNet dataset. Below, we discuss them in more detail. AUROC is a widely-used threshold-independent evaluation metric. Both authors contributed equally to the work. Before entering the inference process, similar to our framework, Softmax also pre-stores certain CLIP and DINO features to make the inference process more efficient.