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Found 9 papers, 4 papers with code
Evaluating Robustness of Neural Networks with Mixed Integer Programming
The computational speedup allows us to verify properties on convolutional networks with an order of magnitude more ReLUs than networks previously verified by any complete verifier.
Deep Learning Analysis of Defect and Phase Evolution During Electron Beam Induced Transformations in WS2
Understanding elementary mechanisms behind solid-state phase transformations and reactions is the key to optimizing desired functional properties of many technologically relevant materials.
Materials Science
A FRAMEWORK FOR ROBUSTNESS CERTIFICATION OF SMOOTHED CLASSIFIERS USING F-DIVERGENCES
Formal verification techniques that compute provable guarantees on properties of machine learning models, like robustness to norm-bounded adversarial perturbations, have yielded impressive results.
Noise or Signal: The Role of Image Backgrounds in Object Recognition
We assess the tendency of state-of-the-art object recognition models to depend on signals from image backgrounds.
3DB: A Framework for Debugging Computer Vision Models
We introduce 3DB: an extendable, unified framework for testing and debugging vision models using photorealistic simulation.
SHORING: Design Provable Conditional High-Order Interaction Network via Symbolic Testing
Deep learning provides a promising way to extract effective representations from raw data in an end-to-end fashion and has proven its effectiveness in various domains such as computer vision, natural language processing, etc.
On Distinctive Properties of Universal Perturbations
We identify properties of universal adversarial perturbations (UAPs) that distinguish them from standard adversarial perturbations.
GPT-4 Technical Report
We report the development of GPT-4, a large-scale, multimodal model which can accept image and text inputs and produce text outputs.
Ranked #1 on
Long-Context Understanding
on Ada-LEval (BestAnswer)
Quantifying and Defending against Privacy Threats on Federated Knowledge Graph Embedding
Knowledge Graph Embedding (KGE) is a fundamental technique that extracts expressive representation from knowledge graph (KG) to facilitate diverse downstream tasks.
