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Found 15 papers, 8 papers with code
Invariant Correlation of Representation with Label
The Invariant Risk Minimization (IRM) approach aims to address the challenge of domain generalization by training a feature representation that remains invariant across multiple environments.
Safeguarding Large Language Models: A Survey
In the burgeoning field of Large Language Models (LLMs), developing a robust safety mechanism, colloquially known as "safeguards" or "guardrails", has become imperative to ensure the ethical use of LLMs within prescribed boundaries.
Direct Training Needs Regularisation: Anytime Optimal Inference Spiking Neural Network
Notably, with STR and cutoff, SNN achieves 2. 14 to 2. 89 faster in inference compared to the pre-configured timestep with near-zero accuracy drop of 0. 50% to 0. 64% over the event-based datasets.
Building Guardrails for Large Language Models
As Large Language Models (LLMs) become more integrated into our daily lives, it is crucial to identify and mitigate their risks, especially when the risks can have profound impacts on human users and societies.
TrajPAC: Towards Robustness Verification of Pedestrian Trajectory Prediction Models
Firstly, the previous definitions of robustness in trajectory prediction are ambiguous.
A Survey of Safety and Trustworthiness of Large Language Models through the Lens of Verification and Validation
Large Language Models (LLMs) have exploded a new heatwave of AI for their ability to engage end-users in human-level conversations with detailed and articulate answers across many knowledge domains.
Randomized Adversarial Training via Taylor Expansion
The randomized weights enable our design of a novel adversarial training method via Taylor expansion of a small Gaussian noise, and we show that the new adversarial training method can flatten loss landscape and find flat minima.
Optimising Event-Driven Spiking Neural Network with Regularisation and Cutoff
The Top-K cutoff technique optimises the inference of SNN, and the regularisation are proposed to affect the training and construct SNN with optimised performance for cutoff.
Certified Policy Smoothing for Cooperative Multi-Agent Reinforcement Learning
The experimental results show that our method produces meaningful guaranteed robustness for all models and environments.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
SAFARI: Versatile and Efficient Evaluations for Robustness of Interpretability
Finally, we demonstrate two applications of our methods: ranking robust XAI methods and selecting training schemes to improve both classification and interpretation robustness.
Enhancing Adversarial Training with Second-Order Statistics of Weights
In this paper, we show that treating model weights as random variables allows for enhancing adversarial training through \textbf{S}econd-Order \textbf{S}tatistics \textbf{O}ptimization (S$^2$O) with respect to the weights.
Weight Expansion: A New Perspective on Dropout and Generalization
While dropout is known to be a successful regularization technique, insights into the mechanisms that lead to this success are still lacking.
Neuronal Correlation: a Central Concept in Neural Network
This paper proposes to study neural networks through neuronal correlation, a statistical measure of correlated neuronal activity on the penultimate layer.
How does Weight Correlation Affect Generalisation Ability of Deep Neural Networks?
This paper studies the novel concept of weight correlation in deep neural networks and discusses its impact on the networks' generalisation ability.
How does Weight Correlation Affect the Generalisation Ability of Deep Neural Networks
This paper studies the novel concept of weight correlation in deep neural networks and discusses its impact on the networks' generalisation ability.
