Deep Learning
New computational algorithms make it possible to build neural networks with many input nodes and many layers, and distinguish "deep learning" of these networks from previous work on artificial neural nets.
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Cherry on Top: Parameter Heterogeneity and Quantization in Large Language Models
Neural Information Processing Systems
We find that a small subset of "cherry" parameters exhibit a
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Scaling Laws in Linear Regression: Compute, Parameters, and Data
Neural Information Processing Systems
From the perspective of statistical learning theory, (1) is rather intriguing. Moreover, they do not provide instance-wise matching lower bounds to verify the tightness of the upper bounds.
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LibAMM: Empirical Insights into Approximate Computing for Accelerating Matrix Multiplication
Neural Information Processing Systems
To support and inspire ongoing and future research, we introduce LibAMM, a framework within the PyTorch ecosystem.
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