- The Why, What and How of Artificial General Intelligence Chip Development
- Machine Learning for Computer Architecture
- Transferable Graph Optimizers for ML Compilers
- MLGO: a Machine Learning Guided Compiler Optimizations Framework
- Cortex: A Compiler for Recursive Deep Learning Models
- A Stealthy Hardware Trojan Exploiting the Architectural Vulnerability of Deep Learning Architectures: Input Interception Attack (IIA)
- QFold: Quantum Walks and Deep Learning to Solve Protein Folding
- Noisy intermediate-scale quantum (NISQ) algorithms
- Enhancing Generative Models via Quantum Correlations
- The Born Supremacy: Quantum Advantage and Training of an Ising Born Machine
- Quantum Generative Models for Small Molecule Drug Discovery
- Quantum Earth Mover's Distance: A New Approach to Learning Quantum Data
- Software Pipelining for Quantum Loop Programs
- Hardware Acceleration of Fully Quantized BERT for Efficient Natural Language Processing
- Rethinking Co-design of Neural Architectures and Hardware Accelerators
- Endurance-Aware Mapping of Spiking Neural Networks to Neuromorphic Hardware
- Hardware Acceleration of Explainable Machine Learning using Tensor Processing Units
- Learning on Hardware: A Tutorial on Neural Network Accelerators and Co-Processors
- A Microarchitecture Implementation Framework for Online Learning with Temporal Neural Networks
- Compiling Halide Programs to Push-Memory Accelerators
- NAAS: Neural Accelerator Architecture Search
- A Full-stack Accelerator Search Technique for Vision Applications
- Hardware Synthesis of State-Space Equations; Application to FPGA Implementation of Shallow and Deep Neural Networks
- VirtualFlow: Decoupling Deep Learning Models from the Underlying Hardware
- A Survey of Transformers
- Technologies for Trustworthy Machine Learning: A Survey in a Socio-Technical Context
- Algorithm to Compilation Co-design: An Integrated View of Neural Network Sparsity
- Code Generation Based on Deep Learning: a Brief Review
- Reverse Engineering of Generative Models: Inferring Model Hyperparameters from Generated Images
- Pitfalls of Explainable ML: An Industry Perspective
- Counterfactual Explanations for Machine Learning: Challenges Revisited
- Learning Deep Morphological Networks with Neural Architecture Search
- Hardware Acceleration of Sparse and Irregular Tensor Computations of ML Models: A Survey and Insights
- Long-time simulations with high fidelity on quantum hardware
- A Long Short-Term Memory for AI Applications in Spike-based Neuromorphic Hardware
- The Backpropagation Algorithm Implemented on Spiking Neuromorphic Hardware
- RHNAS: Realizable Hardware and Neural Architecture Search
- HASCO: Towards Agile HArdware and Software CO-design for Tensor Computation
- Learning on Hardware: A Tutorial on Neural Network Accelerators and Co-Processors
- Brain-inspired global-local learning incorporated with neuromorphic computing
- Bottom-Up and Top-Down Neural Processing Systems Design: Neuromorphic Intelligence as the Convergence of Natural and Artificial Intelligence
- A toolbox for neuromorphic sensing in robotics
- Thermal-Aware Compilation of Spiking Neural Networks to Neuromorphic Hardware
- Neuromorphic Computing is Turing-Complete
- A Theory of Consciousness from a Theoretical Computer Science Perspective 2: Insights from the Conscious Turing Machine
- Data science and AI in FinTech: An overview
- DGL-LifeSci: An Open-Source Toolkit for Deep Learning on Graphs in Life Science
- Data Science Methodologies: Current Challenges and Future Approaches
- Automating Data Science: Prospects and Challenges
- Revisiting Citizen Science Through the Lens of Hybrid Intelligence
- Accelerating science with human versus alien artificial intelligences