Search Results
Diffprivlib: The IBM Differential Privacy Library
Since its conception in 2006, differential privacy has emerged as the de-facto standard in data privacy, owing to its robust mathematical guarantees, generalised applicability and rich body of literature.
IBM Deep Learning Service
Deep learning driven by large neural network models is overtaking traditional machine learning methods for understanding unstructured and perceptual data domains such as speech, text, and vision.
Distributed, Parallel, and Cluster Computing
IBM Federated Learning: an Enterprise Framework White Paper V0.1
Federated Learning (FL) is an approach to conduct machine learning without centralizing training data in a single place, for reasons of privacy, confidentiality or data volume.
Using the IBM Analog In-Memory Hardware Acceleration Kit for Neural Network Training and Inference
In this tutorial, we provide a deep dive into how such adaptations can be achieved and evaluated using the recently released IBM Analog Hardware Acceleration Kit (AIHWKit), freely available at https://github. com/IBM/aihwkit.
CLAIMED -- the open source framework for building coarse-grained operators for accelerated discovery in science
Scientists are well skilled in the process from data to publication.
CLAIMED, a visual and scalable component library for Trusted AI
Deep Learning models are getting more and more popular but constraints on explainability, adversarial robustness and fairness are often major concerns for production deployment.
Verifying Results of the IBM Qiskit Quantum Circuit Compilation Flow
In this paper, we propose an efficient scheme for quantum circuit equivalence checking---specialized for verifying results of the IBM Qiskit quantum circuit compilation flow.
Quantum Circuit Equivalence Checking
Quantum Physics
AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias
Such architectural design and abstractions enable researchers and developers to extend the toolkit with their new algorithms and improvements, and to use it for performance benchmarking.
Adversarial Robustness Toolbox v1.0.0
Defending Machine Learning models involves certifying and verifying model robustness and model hardening with approaches such as pre-processing inputs, augmenting training data with adversarial samples, and leveraging runtime detection methods to flag any inputs that might have been modified by an adversary.
