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Found 12 papers, 3 papers with code
LLM Guided Inductive Inference for Solving Compositional Problems
While large language models (LLMs) have demonstrated impressive performance in question-answering tasks, their performance is limited when the questions require knowledge that is not included in the model's training data and can only be acquired through direct observation or interaction with the real world.
The Fundamental Limits of Interval Arithmetic for Neural Networks
Interval analysis (or interval bound propagation, IBP) is a popular technique for verifying and training provably robust deep neural networks, a fundamental challenge in the area of reliable machine learning.
Robustness Certification of Generative Models
Generative neural networks can be used to specify continuous transformations between images via latent-space interpolation.
Online Robustness Training for Deep Reinforcement Learning
In deep reinforcement learning (RL), adversarial attacks can trick an agent into unwanted states and disrupt training.
Universal Approximation with Certified Networks
To the best of our knowledge, this is the first work to prove the existence of accurate, interval-certified networks.
Verification of Generative-Model-Based Visual Transformations
Generative networks are promising models for specifying visual transformations.
A Provable Defense for Deep Residual Networks
We present a training system, which can provably defend significantly larger neural networks than previously possible, including ResNet-34 and DenseNet-100.
Fast and Effective Robustness Certification
We present a new method and system, called DeepZ, for certifying neural network robustness based on abstract interpretation.
Distilled Agent DQN for Provable Adversarial Robustness
As deep neural networks have become the state of the art for solving complex reinforcement learning tasks, susceptibility to perceptual adversarial examples have become a concern.
Training Neural Machines with Trace-Based Supervision
We investigate the effectiveness of trace-based supervision methods for training existing neural abstract machines.
Differentiable Abstract Interpretation for Provably Robust Neural Networks
We introduce a scalable method for training robust neural networks based on abstract interpretation.
Training Neural Machines with Partial Traces
We present a novel approach for training neural abstract architectures which in- corporates (partial) supervision over the machine’s interpretable components.
