Search Results for author:
Found 17 papers, 2 papers with code
Circuit Partitioning Using Large Language Models for Quantum Compilation and Simulations
Quantum circuit compilation algorithms attempt to minimize these noisy gates when mapping quantum algorithms onto quantum hardware but face computational challenges that restrict their application to circuits with no more than 5-6 qubits, necessitating the need to partition large circuits before the application of noisy quantum gate minimization algorithms.
Jailbreaking Large Language Models with Symbolic Mathematics
Recent advancements in AI safety have led to increased efforts in training and red-teaming large language models (LLMs) to mitigate unsafe content generation.
AutoSafeCoder: A Multi-Agent Framework for Securing LLM Code Generation through Static Analysis and Fuzz Testing
Recent advancements in automatic code generation using large language models (LLMs) have brought us closer to fully automated secure software development.
Data Augmentation for Image Classification using Generative AI
Scaling laws dictate that the performance of AI models is proportional to the amount of available data.
Development and Validation of Fully Automatic Deep Learning-Based Algorithms for Immunohistochemistry Reporting of Invasive Breast Ductal Carcinoma
Our system automatically detects the tumor region removing artifacts and scores based on Allred standard.
Multi-Stain Multi-Level Convolutional Network for Multi-Tissue Breast Cancer Image Segmentation
To extrapolate stain and scanner invariance, our model was evaluated on 23000 patches which were for a completely new stain (Hematoxylin and Eosin) from a completely new scanner (Motic) from a different lab.
Towards a Game-theoretic Understanding of Explanation-based Membership Inference Attacks
By means of a comprehensive set of simulations of the proposed game model, we assess different factors that can impact the capability of an adversary to launch MIA in such repeated interaction settings.
Automaton Distillation: Neuro-Symbolic Transfer Learning for Deep Reinforcement Learning
Reinforcement learning (RL) is a powerful tool for finding optimal policies in sequential decision processes.
Neuro Symbolic Reasoning for Planning: Counterexample Guided Inductive Synthesis using Large Language Models and Satisfiability Solving
We posit that we can use the satisfiability modulo theory (SMT) solvers as deductive reasoning engines to analyze the generated solutions from the LLMs, produce counterexamples when the solutions are incorrect, and provide that feedback to the LLMs exploiting the dialog capability of instruct-trained LLMs.
Neural Stochastic Differential Equations for Robust and Explainable Analysis of Electromagnetic Unintended Radiated Emissions
We provide an empirical demonstration of the fragility of ResNet-like models to Gaussian noise perturbations, where the model performance deteriorates sharply and its F1-score drops to near insignificance at 0. 008 with a Gaussian noise of only 0. 5 standard deviation.
A Game-theoretic Understanding of Repeated Explanations in ML Models
This paper formally models the strategic repeated interactions between a system, comprising of a machine learning (ML) model and associated explanation method, and an end-user who is seeking a prediction/label and its explanation for a query/input, by means of game theory.
Protein Folding Neural Networks Are Not Robust
We define the robustness measure for the predicted structure of a protein sequence to be the inverse of the root-mean-square distance (RMSD) in the predicted structure and the structure of its adversarially perturbed sequence.
CrossedWires: A Dataset of Syntactically Equivalent but Semantically Disparate Deep Learning Models
Currently, our ability to build standardized deep learning models is limited by the availability of a suite of neural network and corresponding training hyperparameter benchmarks that expose differences between existing deep learning frameworks.
Robust Ensembles of Neural Networks using Itô Processes
We exploit this connection and the theory of stochastic dynamical systems to construct a novel ensemble of Itô processes as a new deep learning representation that is more robust than classical residual networks.
An Extension of Fano's Inequality for Characterizing Model Susceptibility to Membership Inference Attacks
We present a new extension of Fano's inequality and employ it to theoretically establish that the probability of success for a membership inference attack on a deep neural network can be bounded using the mutual information between its inputs and its activations.
Quantifying Membership Inference Vulnerability via Generalization Gap and Other Model Metrics
We demonstrate how a target model's generalization gap leads directly to an effective deterministic black box membership inference attack (MIA).
Attribution-driven Causal Analysis for Detection of Adversarial Examples
We study the robustness of machine learning models on benign and adversarial inputs in this neighborhood.
