A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
NACs learn modular neural architectures without domain knowledge.
A new router uses attention-based reinforcement learning to solve detailed routing problems efficiently.
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…
Investigates the fundamental components of attention mechanisms.
PNCs balance tractability and expressiveness in probabilistic modeling.
Bayesian approach optimizes quantum circuits for noisy hardware.
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…
Quantum neural tangent kernels help understand variational quantum circuits in machine learning.
The statistical complexity of quantum circuits is studied using Rademacher complexity.
CREIMBO models diverse brain activity by identifying hidden neural sub-circuits and their non-stationary interactions.
Paper analyzes neural network complexity for planning problems.
CSM-NN uses neural networks to speed up and improve the accuracy of logic circuit simulations.
Combines neural networks and logic circuits for interpretable, accurate, and cost-effective learning.
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…
Analyzes dynamics of quantum neural networks, predicting exponential decay of training error.
Bayesian model detects altered neural circuits in MCI patients.
Study on functions computed by deep-layered machines finds same distribution in neural networks and Boolean circuits.
Study shows how specialized attention circuits emerge during transformer training.
Metalearned neural circuit performs inference over open classes.
Deep neural networks predict CVCM track circuit failures early.
A brain-inspired spiking Transformer reduces energy consumption and enhances interpretability.
This study shows neural nets can approximate Turing machines with meaningful statistical properties.
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 …
Bayesian optimization with Gaussian process as surrogate model has been successfully applied to analog circuit synthesis. In the traditional Gaussian process regression model, the kernel functions are defined explicitly. The computational complexity of training is O(N 3 ), and the computation complexity of prediction i…
Study explores how neural networks and Transformers learn modular arithmetic with multiple inputs.
Deep convolutional neural networks (CNNs) have demonstrated impressive performance on visual object classification tasks. In addition, it is a useful model for predication of neuronal responses recorded in visual system. However, there is still no clear understanding of what CNNs learn in terms of visual neuronal circu…
Compact semiconductor device models are essential for efficiently designing and analyzing large circuits. However, traditional compact model development requires a large amount of manual effort and can span many years. Moreover, inclusion of new physics (eg, radiation effects) into an existing compact model is not triv…
SymCircuit learns PC structure via entropy-regularized RL, improving inference efficiency and accuracy.
We consider efficiency in the implementation of deep neural networks. Hardware accelerators are gaining interest as machine learning becomes one of the drivers of high-performance computing. In these accelerators, the directed graph describing a neural network can be implemented as a directed graph describing a Boolean…
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…
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…
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…
A fast method for learning MZI parameters in optical neural networks.
Recent work suggests goal-driven training of neural networks can be used to model neural activity in the brain. While response properties of neurons in artificial neural networks bear similarities to those in the brain, the network architectures are often constrained to be different. Here we ask if a neural network can…
Deep networks can be understood as logical circuits, improving interpretability and generalization.
Motivated by the resurgence of neural networks in being able to solve complex learning tasks we undertake a study of high depth networks using ReLU gates which implement the function . We try to understand the role of depth in such neural networks by showing size lowerbounds against such network …
Quantum self-attention boosts automated market maker performance in crypto trading.
Generating eye diagrams by using a circuit simulator can be very computationally intensive, especially in the presence of nonlinearities. It often involves multiple Newton-like iterations at every time step when a SPICE-like circuit simulator handles a nonlinear system in the transient regime. In this paper, we leverag…
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…
Temporal Functional Circuits explain KAN forecasts with interpretable edge functions.
VOWEL trains WTA-SNNs for multi-valued events, overcoming resource limitations.
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…
Quantum circuits optimize financial portfolios faster than classical methods.
Study evaluates capacity and trainability of parametrized quantum circuits.
Complex architectures of biological neural circuits, such as parallel processing pathways, has been behaviorally implicated in many cognitive studies. However, the theoretical consequences of circuit complexity on neural computation have only been explored in limited cases. Here, we introduce a mechanism by which direc…
The paper tests if LLMs' capabilities are executed by small subnetworks (circuits).
Study detects if a circuit bounds a disc using curve intersections.
A central challenge in neuroscience is to understand neural computations and circuit mechanisms that underlie the encoding of ethologically relevant, natural stimuli. In multilayered neural circuits, nonlinear processes such as synaptic transmission and spiking dynamics present a significant obstacle to the creation of…