Such an embedding allows answering membership based complex logical reasoning queries with impressive accuracy improvements over popular SRL baselines.Dinesh GargShajith IkbalSantosh K. SrivastavaHarit VishwakarmaHima KaranamL Venkata SubramaniamAdvances in Neural Information Processing Systems 32, Volume 8 of ...
这篇文章被2020NIPS录用,在2019NIPS有一篇基础工作《Quantum Embedding of Knowledge for Reasoning》。思路都差不多,用Quantum Logic来进行知识图谱的推理,和quantum computing没有明显关系。有用到复数域做embedding的工作还有MILA在2019ICLR的RotatE。 红色椭圆是unary concepts,蓝色圆是entity。entity i 的embedding记为...
Garg D, Ikbal S, Srivastava SK, Vishwakarma H, Karanam H, Subramaniam LV (2019) Quantum embedding of knowledge for reasoning. In: Wallach H, Larochelle H, Beygelzimer A, Alché-Buc F, Fox E, Garnett R (eds.) Advances in neural information processing systems 32, pp 5594–5604 Giovannett...
Embedding Problems 53:05 ABHISHEK SAHA_ THE MANIN CONSTANT AND $P$-ADIC BOUNDS ON DENOMINATORS OF THE FOU 1:07:20 MATTHEW YOUNG_ THE FOURTH MOMENT OF DIRICHLET $L$-FUNCTIONS ALONG A COSET 56:47 THE COSMETIC SURGERY CONJECTURE FOR PRETZEL KNOTS 1:07:48 TOPOLOGY OF SMOOTHINGS OF NON-...
Embedding Problems 53:05 ABHISHEK SAHA_ THE MANIN CONSTANT AND $P$-ADIC BOUNDS ON DENOMINATORS OF THE FOU 1:07:20 MATTHEW YOUNG_ THE FOURTH MOMENT OF DIRICHLET $L$-FUNCTIONS ALONG A COSET 56:47 THE COSMETIC SURGERY CONJECTURE FOR PRETZEL KNOTS 1:07:48 TOPOLOGY OF SMOOTHINGS OF NON-...
Section “Background” gives background on the QAOA and Quantum Annealing algorithms. Section “Theory” describes the custom Ising models, the QAOA circuits to sample the Ising models, and the embedding of the Ising models onto the D-Wave quantum annealers. Section “Experiments” details all ...
We now construct an embedding of into . Apparently such an embedding ought to exist for all factors. This line, written in almost a throwaway manner, eventually came to be called“Connes’ embedding problem”: does every separable factor embed into an ultrapower of the hyperfinite ...
The Quantum-Genetic Binary Grey Wolf Optimizer (Q-GBGWO): Q-GBGWO improves on the classical Binary Grey Wolf Optimization (BGWO) by embedding quantum-inspired mechanisms that enrich the algorithm with precision and flexibility in the interpretation of binary space. One of the main innovative aspec...
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of interaction between documents for document representation and classification. Shi et al. [28] proposed an interpretable complex-valued word embedding and applied a convolutional layer on the projected matrix for more remarkable performance and interpretability on text classification. Their shared idea ...