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Found 5 papers, 1 papers with code
Cold-start Bundle Recommendation via Popularity-based Coalescence and Curriculum Heating
To estimate the user-bundle relationship more accurately, CoHeat addresses the highly skewed distribution of bundle interactions through a popularity-based coalescence approach, which incorporates historical and affiliation information based on the bundle's popularity.
Modulating Regularization Frequency for Efficient Compression-Aware Model Training
While model compression is increasingly important because of large neural network size, compression-aware training is challenging as it needs sophisticated model modifications and longer training time. In this paper, we introduce regularization frequency (i. e., how often compression is performed during training) as a new regularization technique for a practical and efficient compression-aware training method.
FleXOR: Trainable Fractional Quantization
Quantization based on the binary codes is gaining attention because each quantized bit can be directly utilized for computations without dequantization using look-up tables.
BiQGEMM: Matrix Multiplication with Lookup Table For Binary-Coding-based Quantized DNNs
Success of quantization in practice, hence, relies on an efficient computation engine design, especially for matrix multiplication that is a basic computation engine in most DNNs.
Decoupling Weight Regularization from Batch Size for Model Compression
Using various models, we show that simple weight updates to comply with compression formats along with long NR period is enough to achieve high compression ratio and model accuracy.
