NeurIPS 2024PastOther
NeurIPS 2024 Workshop Machine Learning with new Compute Paradigms
MLNCP
- Submission deadline
- Sep 12, 2024, 11:59 UTCimported from OpenReview — check the website for extensions
- Submission portal
- OpenReview
- Notes
- Topics were auto-suggested and may be imprecise — edits welcome.
Accepted papers (48)
Fetched from OpenReview (v2) on 2026-06-10.
A cookbook for hardware-friendly implicit learning on static data
A Diagonal State Space Model on Loihi 2 for Efficient Streaming Sequence Processing
A fast algorithm to simulate nonlinear resistive networks
A Fully Analog Pipeline for Portfolio Optimization
A primer on in vitro biological neural networks
Accelerating AI Performance using Anderson Extrapolation on GPUs
Advancing Neuromorphic Computing Algorithms and Systems with NeuroBench
AIHWKIT-Lightning: A Scalable HW-Aware Training Toolkit for Analog In-Memory Computing
Analog Bayesian neural networks are insensitive to the shape of the weight distribution
Analog Computing for AI Sometimes Needs Correction by Digital Computing: Why and When
Analog Gradient Calculation of Optical Activation Function Material
Annealing Machine-assisted Learning of Graph Neural Network for Combinatorial Optimization
Bulk Bitwise Accumulation in Commercial DRAM
Casting hybrid digital-analog training into hierarchical energy-based learning
Deep activity propagation via weight initialization in spiking neural networks
Designing Polaritonic Integrated Circuits for Quantum Processing
DQA: An Efficient Method for Deep Quantization of Deep Neural Network Activations
Dyadic Learning in Recurrent and Feedforward Models
Enabling On-Device Large Language Models with 3D-Stacked Memory
Energy-Efficient Random Number Generation Using Stochastic Magnetic Tunnel Junctions
Event-based backpropagation on the neuromorphic platform SpiNNaker2
Federated Learning with Quantum Computing and Fully Homomorphic Encryption: A Novel Computing Paradigm Shift in Privacy-Preserving ML
Gaussian Process Predictions with Uncertain Inputs Enabled by Uncertainty-Tracking Processor Architectures
Hardware-Algorithm Co-Design for Hyperdimensional Computing Based on Memristive System-on-Chip
High-speed secure random number generator co-processors for privacy-preserving machine learning
Hyperspectral Compute-In-Memory: An Opto-Electronic Computing Architecture Enabling Compute Density Beyond PetaOPS/mm$^2$
Improving Analog Neural Network Robustness: A Noise-Agnostic Approach with Explainable Regularizations
Improving Deep Learning Speed and Performance through Synaptic Neural Balance
Information Bottleneck of Quantum Neural Networks
Integrated Photonic Lattice Filter for Accelerating Deep Convolutional Networks
Legendre-SNN on Loihi-2: Evaluation and Insights
Lie-Equivariant Quantum Graph Neural Networks
MoQ: Mixture-of-format Activation Quantization for Communication-efficient AI Inference System
Multi-Task Neural Network Mapping onto Analog-Digital Heterogeneous Accelerators
N Multipliers for N Bits: Learning Bit Multipliers for Non-Uniform Quantization
Nanowire Neural Networks for time-series processing
Noise Aware Finetuning for Analog Non-Linear Dot Product Engine
On the role of noise in factorizers for disentangling distributed representations
Photonic KAN: a Kolmogorov-Arnold Network Inspired Efficient Photonic Neuromorphic Architecture
Quantum Diffusion Model for Quark and Gluon Jet Generation
Quantum Equilibrium Propagation: gradient-descent training of quantum systems
Quantum Generative Adversarial Networks for High Energy Physics Simulations
Regularizing the Infinite: Improved Generalization Performance with Deep Equilibrium Models
SLaNC: Static LayerNorm Calibration
Thermodynamic Bayesian Inference
Training Machine Learning Models with Ising Machines
Training Spiking Neural Networks via Augmented Direct Feedback Alignment
Universal approximation capabilities of coherent diffractive systems