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Found 9 papers, 2 papers with code
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.
Integrated Decision Gradients: Compute Your Attributions Where the Model Makes Its Decision
Attribution algorithms are frequently employed to explain the decisions of neural network models.
On the Robustness of AlphaFold: A COVID-19 Case Study
To measure the robustness of the predicted structures, we utilize (i) the root-mean-square deviation (RMSD) and (ii) the Global Distance Test (GDT) similarity measure between the predicted structure of the original sequence and the structure of its adversarially perturbed version.
Classifying the Ideological Orientation of User-Submitted Texts in Social Media
With the long-term goal of understanding how language is used and evolves within online communities, this work explores the application of natural language processing techniques to classify text articles according to their ideological orientation (i. e., conservative or liberal).
Ranked #1 on
Classification
on Reddit Ideology Database
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.
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.
Representable Matrices: Enabling High Accuracy Analog Computation for Inference of DNNs using Memristors
In this paper, a technique that aims to produce the correct output for every input vector is proposed, which involves specifying the memristor conductance values and a scaling factor realized by the peripheral circuitry.
