Learning from survey propagation: a neural network for MAX-E-3-SAT

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Typical operators for the decomposition of Boolean functions in state-of-the-art algorithms are AND, exclusive-OR (XOR), and the 2-to-1 multiplexer (MUX). We propose a logic decomposition algorithm that uses the majority-of-three (MAJ) operation. Such a de ...

In recent years, Boolean methods in logic synthesis have been drawing the attention of EDA researchers due to the continuous push to advance quality of results. Boolean methods require high computational cost, as they rely on complete functional properties ...

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The channel impulse response (CIR) of the wireless propagation channel is influenced by the surrounding environment and thus can be used to retrieve environmental information such as location, the presence of objects, and the speed of objects. In this work ...

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A subfamily {F-1, F-2, ..., F-vertical bar P vertical bar} subset of F is a copy of the poset P if there exists a bijection i : P -> {F-1, F-2, ..., F-vertical bar P vertical bar}, such that p

In this manuscript, we investigate the problem of how two-layer neural networks learn features from data, and improve over the kernel regime, after being trained with a single gradient descent step. Leveraging the insight from (Ba et al., 2022), we model t ...

Electrochemical sensors play a pivotal role in advancing therapeutic drug monitoring (TDM) and personalized therapy. However, the insufficient selectivity of electrochemical sensors based on cytochromes P450 poses a great need for innovative machine learni ...

We present a constructive method to create quantum circuits that implement oracles vertical bar x vertical bar y |0 (k) -> vertical bar x vertical bar y circle plus f(x)vertical bar 0 (k) for n-variable Boolean functions f with low T-count. In our method f ...

While momentum-based accelerated variants of stochastic gradient descent (SGD) are widely used when training machine learning models, there is little theoretical understanding on the generalization error of such methods. In this work, we first show that th ...

This work presents a novel hybrid (physics- and data -driven) model for short-term (intra-day and day-ahead, 3h -24h) wind power forecasting (STWPF). Traditionally, STWPF predictors admitted very few meteorological variables only from the grid points close ...

The study of the complexity of Boolean functions has recently found applications in logic synthesis and optimization algorithms, as for instance in logic rewriting. Previous works have focused on the minimum length of Boolean chains for functions up to 5 i ...

When enough annotated training data is available, supervised deep-learning. algorithms excel at estimating human body pose and shape using a single camera. The effects of too little such data being available can be mitigated by using other information sour ...

Designing digital circuits well is notoriously difficult. This difficulty stems in part from the very many degrees of freedom inherent in circuit design, typically coupled with the need to satisfy various constraints. In this thesis, we demonstrate how for ...

Machine learning models have been shown to leak sensitive information about their training datasets. Models are increasingly deployed on devices, raising concerns that white-box access to the model parameters increases the attack surface compared to black- ...

Learning from survey propagation: a neural network for MAX-E-3-SAT

Chattez avec Graph Search

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