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Efficient Neural Network

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TitleStatusHype
Interact2Vec -- An efficient neural network-based model for simultaneously learning users and items embeddings in recommender systems—0
Linearity-based neural network compression—0
Accelerating 3D Gaussian Splatting with Neural Sorting and Axis-Oriented Rasterization—0
SpikePingpong: High-Frequency Spike Vision-based Robot Learning for Precise Striking in Table Tennis Game—0
Dynamic Spectral Backpropagation for Efficient Neural Network Training—0
Probabilistic Emulation of the Community Radiative Transfer Model Using Machine Learning—0
Can LLMs Revolutionize the Design of Explainable and Efficient TinyML Models?—0
LightSNN: Lightweight Architecture Search for Sparse and Accurate Spiking Neural NetworksCode0
Efficient Reachability Analysis for Convolutional Neural Networks Using Hybrid Zonotopes—0
Reinforcement Learning-based Threat Assessment—0
Automatic Joint Structured Pruning and Quantization for Efficient Neural Network Training and CompressionCode1
When do they StOP?: A First Step Towards Automatically Identifying Team Communication in the Operating RoomCode0
Harmonic Loss Trains Interpretable AI ModelsCode0
Multiscale Training of Convolutional Neural Networks—0
Mono-Forward: Backpropagation-Free Algorithm for Efficient Neural Network Training Harnessing Local Errors—0
HALO: Hadamard-Assisted Lower-Precision Optimization for LLMsCode1
Learn2Mix: Training Neural Networks Using Adaptive Data IntegrationCode0
Efficient Neural Network Encoding for 3D Color Lookup TablesCode0
A Digital twin for Diesel Engines: Operator-infused PINNs with Transfer Learning for Engine Health Monitoring—0
Prediction-Enhanced Monte Carlo: A Machine Learning View on Control Variate—0
VQ4ALL: Efficient Neural Network Representation via a Universal Codebook—0
MTSpark: Enabling Multi-Task Learning with Spiking Neural Networks for Generalist Agents—0
EfficientViM: Efficient Vision Mamba with Hidden State Mixer based State Space DualityCode2
Can KAN Work? Exploring the Potential of Kolmogorov-Arnold Networks in Computer Vision—0
LUTMUL: Exceed Conventional FPGA Roofline Limit by LUT-based Efficient Multiplication for Neural Network Inference—0
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