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