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Found 8 papers, 2 papers with code
AttentionLego: An Open-Source Building Block For Spatially-Scalable Large Language Model Accelerator With Processing-In-Memory Technology
Large language models (LLMs) with Transformer architectures have become phenomenal in natural language processing, multimodal generative artificial intelligence, and agent-oriented artificial intelligence.
Datasets and Benchmarks for Nanophotonic Structure and Parametric Design Simulations
To design and understand these nanophotonic structures, electrodynamic simulations are essential.
Interpretability is a Kind of Safety: An Interpreter-based Ensemble for Adversary Defense
While having achieved great success in rich real-life applications, deep neural network (DNN) models have long been criticized for their vulnerability to adversarial attacks.
Intelligent detect for substation insulator defects based on CenterMask
Based on the end-to-end learning paradigm, this paper proposes an intelligent detection method for substation insulator defects based on CenterMask.
Learning Generalizable Behavior via Visual Rewrite Rules
Though deep reinforcement learning agents have achieved unprecedented success in recent years, their learned policies can be brittle, failing to generalize to even slight modifications of their environments or unfamiliar situations.
Deep Compression of Neural Networks for Fault Detection on Tennessee Eastman Chemical Processes
Artificial neural network has achieved the state-of-art performance in fault detection on the Tennessee Eastman process, but it often requires enormous memory to fund its massive parameters.
Towards Sample Efficient Agents through Algorithmic Alignment
We demonstrate the potential of graph neural network in supporting sample efficient learning by showing that Deep Graph Value Network can outperform unstructured baselines by a large margin in solving the Markov Decision Process (MDP).
Are L2 adversarial examples intrinsically different?
That is, adversarial examples generated by $L_2$ attacks usually have larger input sensitivity which can be used to identify them efficiently.
