arXiv research

Study detects if a circuit bounds a disc using curve intersections.

Bayesian model detects altered neural circuits in MCI patients.

In this paper, we firstly introduce a method to efficiently implement large-scale high-dimensional convolution with realistic memristor-based circuit components. An experiment verified simulator is adapted for accurate prediction of analog crossbar behavior. An improved conversion algorithm is developed to convert conv…

EMODM detects abnormal patterns in complex systems.

Neural circuits contain heterogeneous groups of neurons that differ in type, location, connectivity, and basic response properties. However, traditional methods for dimensionality reduction and clustering are ill-suited to recovering the structure underlying the organization of neural circuits. In particular, they do n…

The focus of this paper is on intrinsic methods to detect overfitting. By intrinsic methods, we mean methods that rely only on the model and the training data, as opposed to traditional methods (we call them extrinsic methods) that rely on performance on a test set or on bounds from model complexity. We propose a famil…

Improved PQM for pattern classification on quantum computers.

Neuronal circuits formed in the brain are complex with intricate connection patterns. Such complexity is also observed in the retina as a relatively simple neuronal circuit. A retinal ganglion cell receives excitatory inputs from neurons in previous layers as driving forces to fire spikes. Analytical methods are requir…

A vast majority of computation in the brain is performed by spiking neural networks. Despite the ubiquity of such spiking, we currently lack an understanding of how biological spiking neural circuits learn and compute in-vivo, as well as how we can instantiate such capabilities in artificial spiking circuits in-silico.…

While on some natural distributions, neural-networks are trained efficiently using gradient-based algorithms, it is known that learning them is computationally hard in the worst-case. To separate hard from easy to learn distributions, we observe the property of local correlation: correlation between local patterns of t…

Study shows how specialized attention circuits emerge during transformer training.

Study evaluates capacity and trainability of parametrized quantum circuits.

VOWEL trains WTA-SNNs for multi-valued events, overcoming resource limitations.

BiLiNGAM model reveals brain emotion circuit development in adolescents.

The paper tests if LLMs' capabilities are executed by small subnetworks (circuits).

Reconstructing network connectivity from the collective dynamics of a system typically requires access to its complete continuous-time evolution although these are often experimentally inaccessible. Here we propose a theory for revealing physical connectivity of networked systems only from the event time series their i…

A quantum walk-based method for generating precise probability distributions efficiently.

A new router uses attention-based reinforcement learning to solve detailed routing problems efficiently.

Evolutionary strategy optimizes quantum circuit design and parameters.

This work uses SVM to identify track component failures in AC Track Circuits.

The statistical complexity of quantum circuits is studied using Rademacher complexity.

Quantum circuit Born machines are generative models which represent the probability distribution of classical dataset as quantum pure states. Computational complexity considerations of the quantum sampling problem suggest that the quantum circuits exhibit stronger expressibility compared to classical neural networks. O…

The study examines how quantum resources enhance the complexity of quantum circuits.

Quantum mechanics is inherently probabilistic in light of Born's rule. Using quantum circuits as probabilistic generative models for classical data exploits their superior expressibility and efficient direct sampling ability. However, training of quantum circuits can be more challenging compared to classical neural net…

Program connects quantum computing and topological field theories.

Machine learning identifies key metabolic control circuits in bacterial pathways.

A new approach uses circuit topology to study complex polymer interactions.

Study bounds VAR model's circuit complexity, showing it's limited to TC^0 circuits.

Study shows limitations and possibilities of learning quantum circuit output distributions.

Quantum mechanics fundamentally forbids deterministic discrimination of quantum states and processes. However, the ability to optimally distinguish various classes of quantum data is an important primitive in quantum information science. In this work, we train near-term quantum circuits to classify data represented by …

We constructed an analog electrical circuit which generates fluctuations in which probability density function has power law tails. In the circuit fluctuations with an arbitrary exponent of the power law can be obtained by adjusting the resistance. With this low cost circuit the random fluctuations which have the simil…

Spin networks boost quantum algorithms solving SU(2) symmetric problems.

We survey distributed deep learning models for training or inference without accessing raw data from clients. These methods aim to protect confidential patterns in data while still allowing servers to train models. The distributed deep learning methods of federated learning, split learning and large batch stochastic gr…

Enhances quantum circuit synthesis using deep learning and geometric methods.

Single T-gate makes distribution learning hard for deep circuits.

Paper develops data-driven compact models for diodes.

We propose a neural information processing system which is obtained by re-purposing the function of a biological neural circuit model, to govern simulated and real-world control tasks. Inspired by the structure of the nervous system of the soil-worm, C. elegans, we introduce Neuronal Circuit Policies (NCPs), defined as…

Superconducting circuit technologies have recently achieved quantum protocols involving closed feedback loops. Quantum artificial intelligence and quantum machine learning are emerging fields inside quantum technologies which may enable quantum devices to acquire information from the outer world and improve themselves …

Quantum circuits are hard to learn on average.

Study improves probabilistic circuits using transformations for better predictions.

Surface mount technology (SMT) is a process for producing printed circuit boards. Solder paste printer (SPP), package mounter, and solder reflow oven are used for SMT. The board on which the solder paste is deposited from the SPP is monitored by solder paste inspector (SPI). If SPP malfunctions due to the printer defec…

Quantum circuit optimization speeds up financial derivatives pricing.

Bayesian approach optimizes quantum circuits for noisy hardware.

The state-of-the-art machine learning approaches are based on classical von Neumann computing architectures and have been widely used in many industrial and academic domains. With the recent development of quantum computing, researchers and tech-giants have attempted new quantum circuits for machine learning tasks. How…

New method enhances hotspot prediction in IC designs.

Automatically designs analog circuits with deep learning.

The lattice stick number $s_L(L)$ of a link $L$ is defined to be the minimal number of straight line segments required to construct a stick presentation of $L$ in the cubic lattice. Hong, No and Oh found a general upper bound $s_L(K) \leq 3 c(K) +2$. A rational link can be represented by a lattice presentation with exa…

Two proofs show that removing a loop from a plane circuit splits the plane.