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Found 28 papers, 18 papers with code
Overcoming the Paradox of Certified Training with Gaussian Smoothing
Training neural networks with high certified accuracy against adversarial examples remains an open problem despite significant efforts.
Guiding LLMs The Right Way: Fast, Non-Invasive Constrained Generation
To ensure that text generated by large language models (LLMs) is in an expected format, constrained decoding proposes to enforce strict formal language constraints during generation.
Evading Data Contamination Detection for Language Models is (too) Easy
Large language models are widespread, with their performance on benchmarks frequently guiding user preferences for one model over another.
Controlled Text Generation via Language Model Arithmetic
In addition, the framework allows for more precise control of generated text than direct prompting and prior controlled text generation (CTG) techniques.
Automated Classification of Model Errors on ImageNet
To address this, new label-sets and evaluation protocols have been proposed for ImageNet showing that state-of-the-art models already achieve over 95% accuracy and shifting the focus on investigating why the remaining errors persist.
Prompt Sketching for Large Language Models
This way, sketching grants users more control over the generation process, e. g., by providing a reasoning framework via intermediate instructions, leading to better overall results.
Understanding Certified Training with Interval Bound Propagation
We, then, derive sufficient and necessary conditions on weight matrices for IBP bounds to become exact and demonstrate that these impose strong regularization, explaining the empirically observed trade-off between robustness and accuracy in certified training.
TAPS: Connecting Certified and Adversarial Training
Training certifiably robust neural networks remains a notoriously hard problem.
Efficient Certified Training and Robustness Verification of Neural ODEs
Neural Ordinary Differential Equations (NODEs) are a novel neural architecture, built around initial value problems with learned dynamics which are solved during inference.
Prompting Is Programming: A Query Language for Large Language Models
We show that LMQL can capture a wide range of state-of-the-art prompting methods in an intuitive way, especially facilitating interactive flows that are challenging to implement with existing high-level APIs.
Prompt Tuning for Parameter-efficient Medical Image Segmentation
As such, fine-tuning a model to a downstream task in a parameter-efficient but effective way, e. g. for a new set of classes in the case of semantic segmentation, is of increasing importance.
Private and Reliable Neural Network Inference
We employ these building blocks to enable privacy-preserving NN inference with robustness and fairness guarantees in a system called Phoenix.
Certified Training: Small Boxes are All You Need
To obtain, deterministic guarantees of adversarial robustness, specialized training methods are used.
(De-)Randomized Smoothing for Decision Stump Ensembles
Whereas most prior work on randomized smoothing focuses on evaluating arbitrary base models approximately under input randomization, the key insight of our work is that decision stump ensembles enable exact yet efficient evaluation via dynamic programming.
Robust and Accurate -- Compositional Architectures for Randomized Smoothing
Randomized Smoothing (RS) is considered the state-of-the-art approach to obtain certifiably robust models for challenging tasks.
Abstract Interpretation of Fixpoint Iterators with Applications to Neural Networks
We present a new abstract interpretation framework for the precise over-approximation of numerical fixpoint iterators.
Shared Certificates for Neural Network Verification
We perform an extensive experimental evaluation to demonstrate the effectiveness of shared certificates in reducing the verification cost on a range of datasets and attack specifications on image classifiers including the popular patch and geometric perturbations.
Scalable Certified Segmentation via Randomized Smoothing
We present a new certification method for image and point cloud segmentation based on randomized smoothing.
Boosting Randomized Smoothing with Variance Reduced Classifiers
Randomized Smoothing (RS) is a promising method for obtaining robustness certificates by evaluating a base model under noise.
Fully Automated and Standardized Segmentation of Adipose Tissue Compartments by Deep Learning in Three-dimensional Whole-body MRI of Epidemiological Cohort Studies
Methods: Quantification and localization of different adipose tissue compartments from whole-body MR images is of high interest to examine metabolic conditions.
Certified Defense to Image Transformations via Randomized Smoothing
We extend randomized smoothing to cover parameterized transformations (e. g., rotations, translations) and certify robustness in the parameter space (e. g., rotation angle).
Learning Certified Individually Fair Representations
That is, our method enables the data producer to learn and certify a representation where for a data point all similar individuals are at $\ell_\infty$-distance at most $\epsilon$, thus allowing data consumers to certify individual fairness by proving $\epsilon$-robustness of their classifier.
Practice of Streaming Processing of Dynamic Graphs: Concepts, Models, and Systems
We also crystallize the meaning of different concepts associated with streaming graph processing, such as dynamic, temporal, online, and time-evolving graphs, edge-centric processing, models for the maintenance of updates, and graph databases.
Distributed, Parallel, and Cluster Computing Databases Data Structures and Algorithms Performance
Online Robustness Training for Deep Reinforcement Learning
In deep reinforcement learning (RL), adversarial attacks can trick an agent into unwanted states and disrupt training.
Statistical Verification of General Perturbations by Gaussian Smoothing
We present a novel statistical certification method that generalizes prior work based on smoothing to handle richer perturbations.
DL2: Training and Querying Neural Networks with Logic
We present DL2, a system for training and querying neural networks with logical constraints.
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.
MedGAN: Medical Image Translation using GANs
Image-to-image translation is considered a new frontier in the field of medical image analysis, with numerous potential applications.
