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Compiler Auto-Vectorization with Imitation Learning
Charith Mendis, Cambridge Yang, Yewen Pu, Dr.Saman Amarasinghe, Michael Carbin
Therefore, many existing SLP vectorization schemes are driven by hand-crafted heuristics (Liu et al., 2012;
The Pessimistic Limits and Possibilities of Margin-based Losses in Semi-supervised Learning
Neural Information Processing Systems http://nips.cc/
Learn, Imagine and Create: Text-to-Image Generation from Prior Knowledge
Tingting Qiao, Jing Zhang, Duanqing Xu, Dacheng Tao
Given a text description, we immediately imagine an overall visual impression using this prior and, based on this, we draw a picture by progressively adding more andmore details.
DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correction
Spider, the SOT A, in terms of execution accuracy, was 79.9 and the new SOT A at
Comparing Unsupervised Word Translation Methods Step by Step
Mareike Hartmann, Yova Kementchedjhieva, Anders Sรธgaard
Awordvectorspace-sometimes referred toasawordembedding-associates similar wordsina vocabularywithsimilar vectors.
Dirichlet-based Gaussian Processes for Large-scale Calibrated Classification
Dimitrios Milios, Raffaello Camoriano, Pietro Michiardi, Lorenzo Rosasco, Maurizio Filippone
Neural Information Processing Systems http://nips.cc/
Communication Bounds for the Distributed Experts Problem
For a large number of experts and days, it may not be feasible to run classical low-regret algorithms.
Generalizing Point Embeddings using the Wasserstein Space of Elliptical Distributions
Neural Information Processing Systems http://nips.cc/