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Found 6 papers, 3 papers with code
NDJIR: Neural Direct and Joint Inverse Rendering for Geometry, Lights, and Materials of Real Object
The goal of inverse rendering is to decompose geometry, lights, and materials given pose multi-view images.
Neural Network Libraries: A Deep Learning Framework Designed from Engineers' Perspectives
While there exist a plethora of deep learning tools and frameworks, the fast-growing complexity of the field brings new demands and challenges, such as more flexible network design, speedy computation on distributed setting, and compatibility between different tools.
Efficient Sampling for Predictor-Based Neural Architecture Search
Usually, we compute the proxy for all DNNs in the network search space and pick those that maximize the proxy as candidates for optimization.
Iteratively Training Look-Up Tables for Network Quantization
For each layer, we learn a value dictionary and an assignment matrix to represent the network weights.
Mixed Precision DNNs: All you need is a good parametrization
Since choosing the optimal bitwidths is not straight forward, training methods, which can learn them, are desirable.
Iteratively Training Look-Up Tables for Network Quantization
In this paper we introduce a training method, called look-up table quantization, LUT-Q, which learns a dictionary and assigns each weight to one of the dictionary's values.
