Tensor Networks
59 papers with code • 0 benchmarks • 0 datasets
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Tensor Ring Optimized Quantum-Enhanced Tensor Neural Networks
Quantum machine learning researchers often rely on incorporating Tensor Networks (TN) into Deep Neural Networks (DNN) and variational optimization.
Symbolically integrating tensor networks over various random tensors by the second version of Python RTNI
We are upgrading the Python-version of RTNI, which symbolically integrates tensor networks over the Haar-distributed unitary matrices.
Fuzzy Logic Visual Network (FLVN): A neuro-symbolic approach for visual features matching
grains2/flvn
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The latter allow, for instance, to handle exceptions in class-level attributes, and to enforce similarity between images of the same class, preventing premature overfitting to seen classes and improving overall performance.
logLTN: Differentiable Fuzzy Logic in the Logarithm Space
sbadredd/logltn-experiments
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A significant trend in the literature involves integrating axioms and facts in loss functions by grounding logical symbols with neural networks and operators with fuzzy semantics.
TensorKrowch: Smooth integration of tensor networks in machine learning
Tensor networks are factorizations of high-dimensional tensors into networks of smaller tensors.
ANTN: Bridging Autoregressive Neural Networks and Tensor Networks for Quantum Many-Body Simulation
Quantum many-body physics simulation has important impacts on understanding fundamental science and has applications to quantum materials design and quantum technology.
Interval Logic Tensor Networks
In this paper, we introduce Interval Real Logic (IRL), a two-sorted logic that interprets knowledge such as sequential properties (traces) and event properties using sequences of real-featured data.
Tensor Networks Meet Neural Networks: A Survey and Future Perspectives
tnbar/awesome-tensorial-neural-networks
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Interestingly, although these two types of networks originate from different observations, they are inherently linked through the common multilinearity structure underlying both TNs and NNs, thereby motivating a significant number of intellectual developments regarding combinations of TNs and NNs.
Positive unlabeled learning with tensor networks
qml-tn/pul
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Positive unlabeled learning is a binary classification problem with positive and unlabeled data.
Sampling-Based Decomposition Algorithms for Arbitrary Tensor Networks
We show how to develop sampling-based alternating least squares (ALS) algorithms for decomposition of tensors into any tensor network (TN) format.
