Search Results for author:
Found 6 papers, 3 papers with code
Can We Utilize Pre-trained Language Models within Causal Discovery Algorithms?
Scaling laws have allowed Pre-trained Language Models (PLMs) into the field of causal reasoning.
AutoSNN: Towards Energy-Efficient Spiking Neural Networks
We investigate the design choices used in the previous studies in terms of the accuracy and number of spikes and figure out that they are not best-suited for SNNs.
AdvRush: Searching for Adversarially Robust Neural Architectures
Current efforts to improve the robustness of neural networks against adversarial examples are focused on developing robust training methods, which update the weights of a neural network in a more robust direction.
Accelerating Neural Architecture Search via Proxy Data
By analyzing proxy data constructed using various selection methods through data entropy, we propose a novel proxy data selection method tailored for NAS.
Fast and Efficient Information Transmission with Burst Spikes in Deep Spiking Neural Networks
The spiking neural networks (SNNs) are considered as one of the most promising artificial neural networks due to their energy efficient computing capability.
Near-Data Processing for Differentiable Machine Learning Models
The second is the popularity of NAND flash-based solid-state drives (SSDs) containing multicore processors that can accommodate extra computation for data processing.
