arXiv research

Polynomial-time convex optimization for CNNs with ReLU activations.

Study clarifies Bayesian generalization error in CBM for 3-layered linear neural networks.

Three-layer networks learn more complex features than two-layer networks.

New proof shows neural networks can represent all multivariate functions.

Three-layer neural networks learn hierarchical polynomial functions efficiently.

Analytical solution found for a three-layer network with a specific activation function.

Global convergence proved for three-layer neural networks in mean field regime.

We propose a planning and perception mechanism for a robot (agent), that can only observe the underlying environment partially, in order to solve an image classification problem. A three-layer architecture is suggested that consists of a meta-layer that decides the intermediate goals, an action-layer that selects local…

The study analyzes a three-layer neural network's training dynamics using a functional-space mean-field theory.

Three-layer networks learn complex hierarchical polynomials of multiple nonlinear features.

Deep ReLU networks can be simplified to a three-layer model.

The paper extends mean field results to three-layer neural networks using SGD.

This paper extends stability and generalization analysis of GD for multi-layer NNs.

The paper demonstrates the use of the fully convolutional neural network for glioma segmentation on the BraTS 2019 dataset. Three-layers deep encoder-decoder architecture is used along with dense connection at encoder part to propagate the information from coarse layer to deep layers. This architecture is used to train…

Study analyzes error in neural network solving PDEs, providing convergence and error bounds.

Sleep disorders are implicated in a growing number of health problems. In this paper, we present a signal-processing/machine learning approach to detecting arousals in the multi-channel polysomnographic recordings of the Physionet/CinC Challenge2018 dataset. Methods: Our network architecture consists of two components.…

This work explains neural collapse in shallow neural networks and its impact on generalization.

Deep learning can learn compositional functions more efficiently by breaking them into stages.

New algorithm interpolates data with neural nets, independent of sample size.

The paper sets limits on neural network sizes based on dataset shapes.

Global convergence of multilayer neural networks proven for any depth.

Simplifies deep learning scaling analysis without sacrificing accuracy.

A new neural network model MDRBM improves noise-robustness in classification.

PCBM improves neural network generalization by partially observing concepts.

New AI platform screens portfolios for desirable firms and news.

3-layer NTK models generalize better than 2-layer models, especially with large input dimensions.

In this paper, we prove a conjecture published in 1989 and also partially address an open problem announced at the Conference on Learning Theory (COLT) 2015. With no unrealistic assumption, we first prove the following statements for the squared loss function of deep linear neural networks with any depth and any widths…

A new framework explains GNN predictions by simulating graph structure and feature changes.

Examines learning efficiency in neural networks and related models.

This informal technical report details the geometric illustration of decision boundaries for ReLU units in a three layer fully connected neural network. The network is designed and trained to predict pixel intensity from an (x, y) input location. The Geometric Illustration of Neural Networks (GINN) tool was built to vi…

The study explores the compressive power of Boolean threshold autoencoders, finding that seven layers are necessary but three are not.

Study improves chiral photonic metasurface design using neural networks and genetic algorithms.

FinRL automates trading in quantitative finance with deep reinforcement learning.

FluxLayer solves cross-chain liquidity fragmentation for better MEV capture.

SmallML predicts customer churn for SMEs with small data, improving accuracy by 24.2 points.

We analyzed optimism in linear and kernel regression models.

Adversarial attacks against machine learning models are a rather hefty obstacle to our increasing reliance on these models. Due to this, provably robust (certified) machine learning models are a major topic of interest. Lipschitz continuous models present a promising approach to solving this problem. By leveraging the …

New approach optimizes sales process for B2B businesses.

We develop a mathematically rigorous framework for multilayer neural networks in the mean field regime. As the network's widths increase, the network's learning trajectory is shown to be well captured by a meaningful and dynamically nonlinear limit (the \textit{mean field} limit), which is characterized by a system of …

We develop a corrective mechanism for neural network approximation: the total available non-linear units are divided into multiple groups and the first group approximates the function under consideration, the second group approximates the error in approximation produced by the first group and corrects it, the third gro…

In this work, we utilize T1-weighted MR images and StackNet to predict fluid intelligence in adolescents. Our framework includes feature extraction, feature normalization, feature denoising, feature selection, training a StackNet, and predicting fluid intelligence. The extracted feature is the distribution of different…

Robust algorithm for distributed optimization resistant to Byzantine failures.

The fundamental learning theory behind neural networks remains largely open. What classes of functions can neural networks actually learn? Why doesn't the trained network overfit when it is overparameterized? In this work, we prove that overparameterized neural networks can learn some notable concept classes, including…

A comparison of SLDS and LSTM for pedestrian behavior prediction shows SLDS works better with shorter sequences.

Two methods estimate effect size for online experiments, improving accuracy and efficiency.

To address potential gaps noted in patient monitoring in the hospital, a novel patient behavior detection system using mmWave radar and deep convolution neural network (CNN), which supports the simultaneous recognition of multiple patients' behaviors in real-time, is proposed. In this study, we use an mmWave radar to t…

Smart mobility management would be an important prerequisite for future fog computing systems. In this research, we propose a learning-based handover optimization for the Internet of Vehicles that would assist the smooth transition of device connections and offloaded tasks between fog nodes. To accomplish this, we make…

Proves deep networks can learn hierarchical structures efficiently.