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Found 7 papers, 1 papers with code
ReSpike: Residual Frames-based Hybrid Spiking Neural Networks for Efficient Action Recognition
Spiking Neural Networks (SNNs) have emerged as a compelling, energy-efficient alternative to traditional Artificial Neural Networks (ANNs) for static image tasks such as image classification and segmentation.
Decoupled Marked Temporal Point Process using Neural Ordinary Differential Equations
While most previous studies focus on the inter-event dependencies and their representations, how individual events influence the overall dynamics over time has been under-explored.
CATS: Contextually-Aware Thresholding for Sparsity in Large Language Models
We demonstrate that CATS can be applied to various base models, including Mistral-7B and Llama2-7B, and outperforms existing sparsification techniques in downstream task performance.
TT-SNN: Tensor Train Decomposition for Efficient Spiking Neural Network Training
Spiking Neural Networks (SNNs) have gained significant attention as a potentially energy-efficient alternative for standard neural networks with their sparse binary activation.
GenQ: Quantization in Low Data Regimes with Generative Synthetic Data
In the realm of deep neural network deployment, low-bit quantization presents a promising avenue for enhancing computational efficiency.
Faster Maximum Inner Product Search in High Dimensions
We provide theoretical guarantees that BanditMIPS returns the correct answer with high probability, while improving the complexity in $d$ from $O(\sqrt{d})$ to $O(1)$.
Data-free mixed-precision quantization using novel sensitivity metric
Post-training quantization is a representative technique for compressing neural networks, making them smaller and more efficient for deployment on edge devices.
