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.
Optimizing Solution-Samplers for Combinatorial Problems: The Landscape of Policy-Gradient Methods
Neural Information Processing Systems
Deep Neural Networks and Reinforcement Learning methods have empirically shown great promise in tackling challenging combinatorial problems.
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StEik: Stabilizing the Optimization of Neural Signed Distance Functions and Finer Shape Representation Huizong Yang 1* Y uxin Sun
Neural Information Processing Systems
We show analytically how other terms added to the loss, currently used in the literature for other purposes, can actually eliminate these instabilities.
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A Closer Look at the Robustness of Contrastive Language-Image Pre-Training (CLIP)
Neural Information Processing Systems
A.2 Models Included in Experiments A.2.1 Zero-shot CLIP
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