Search Results for author:
Found 16 papers, 6 papers with code
Vanishing Variance Problem in Fully Decentralized Neural-Network Systems
Our findings suggest that this averaging approach inherently introduces a potential delay in model convergence.
NASH: Neural Architecture Search for Hardware-Optimized Machine Learning Models
We present four versions of the NASH strategy in this paper, all of which show higher accuracy than the original models.
NLICE: Synthetic Medical Record Generation for Effective Primary Healthcare Differential Diagnosis
In order to increase the expressive nature of the synthetic data, we use a medically-standardized symptom modeling method called NLICE to augment the synthetic data with additional contextual information for each condition.
QKSA: Quantum Knowledge Seeking Agent -- resource-optimized reinforcement learning using quantum process tomography
This formal framework is termed Quantum Knowledge Seeking Agent (QKSA).
An Attention Module for Convolutional Neural Networks
To solve the two problems together, we initially propose an attention module for convolutional neural networks by developing an AW-convolution, where the shape of attention maps matches that of the weights rather than the activations.
Fidel: Reconstructing Private Training Samples from Weight Updates in Federated Learning
The methodology of using this attack is discussed, and as a proof of viability we show how this attack method can be used to great effect for densely connected networks and convolutional neural networks.
Privacy-Preserving Object Detection & Localization Using Distributed Machine Learning: A Case Study of Infant Eyeblink Conditioning
Results show that the aggregation of models for the HOG algorithm using MWMA not only preserves the accuracy of the model but also allows for distributed learning with an accuracy increase of 0. 9% compared with traditional learning.
SoFAr: Shortcut-based Fractal Architectures for Binary Convolutional Neural Networks
Inspired by the shortcuts and fractal architectures, we propose two Shortcut-based Fractal Architectures (SoFAr) specifically designed for BCNNs: 1. residual connection-based fractal architectures for binary ResNet, and 2. dense connection-based fractal architectures for binary DenseNet.
NASB: Neural Architecture Search for Binary Convolutional Neural Networks
In this paper, we propose a strategy, named NASB, which adopts Neural Architecture Search (NAS) to find an optimal architecture for the binarization of CNNs.
Towards Lossless Binary Convolutional Neural Networks Using Piecewise Approximation
Binary Convolutional Neural Networks (CNNs) can significantly reduce the number of arithmetic operations and the size of memory storage, which makes the deployment of CNNs on mobile or embedded systems more promising.
Real-Time Face and Landmark Localization for Eyeblink Detection
To demonstrate the usefulness of our eyelid-detection algorithm, a research hypothesis was formed and a well-established neuroscientific experiment was employed: eyeblink detection.
Quantum circuit design for universal distribution using a superposition of classical automata
This is the first time, a superposition of classical automata is implemented on the circuit model of quantum computation, having the corresponding mechanistic parts of a classical Turing machine.
QuASeR -- Quantum Accelerated De Novo DNA Sequence Reconstruction
In this article, we present QuASeR, a reference-free DNA sequence reconstruction implementation via de novo assembly on both gate-based and quantum annealing platforms.
Quantum Physics Emerging Technologies Genomics
An Overview of Federated Deep Learning Privacy Attacks and Defensive Strategies
Additionally, we provide a literature study of the most recent defensive strategies and algorithms for FL aimed to overcome these attacks.
An algorithm for DNA read alignment on quantum accelerators
With small-scale quantum processors transitioning from experimental physics labs to industrial products, these processors allow us to efficiently compute important algorithms in various fields.
Quantum Physics Emerging Technologies Genomics
BrainFrame: A node-level heterogeneous accelerator platform for neuron simulations
Approach: In this paper we propose and build BrainFrame, a heterogeneous acceleration platform, incorporating three distinct acceleration technologies, a Dataflow Engine, a Xeon Phi and a GP-GPU.
