7 citations · 18 across the 19 of their papers we have counts for
7 papers · 1 filter
Symbolic Regression via Latent Iterative Refinement
Xieting Chu, Sriram Vishwanath, Vijay Ganesh
Symbolic regression (SR) seeks closed-form mathematical expressions that fit observed data. Neural SR methods amortize the search by training an encoder to map observations directl…
MathConstraint: Automated Generation of Verified Combinatorial Reasoning Instances for LLMs
Viresh Pati, Zhengyu Li, Piyush Jha +3
We introduce MathConstraint, a hard, adaptive benchmark for evaluating the combinatorial reasoning capabilities of LLMs. We combine constraint satisfaction problems with rigorous s…
LLMStinger: Jailbreaking LLMs using RL fine-tuned LLMs
Piyush Jha, Arnav Arora, Vijay Ganesh
We introduce LLMStinger, a novel approach that leverages Large Language Models (LLMs) to automatically generate adversarial suffixes for jailbreak attacks. Unlike traditional metho…
CGDTest: A Constrained Gradient Descent Algorithm for Testing Neural Networks
Vineel Nagisetty, Laura Graves, Guanting Pan +2
In this paper, we propose a new Deep Neural Network (DNN) testing algorithm called the Constrained Gradient Descent (CGD) method, and an implementation we call CGDTest aimed at exp…
Learning Modulo Theories
Matt Fredrikson, Kaiji Lu, Saranya Vijayakumar +3
Recent techniques that integrate \emph{solver layers} into Deep Neural Networks (DNNs) have shown promise in bridging a long-standing gap between inductive learning and symbolic re…
A Solver + Gradient Descent Training Algorithm for Deep Neural Networks
Dhananjay Ashok, Vineel Nagisetty, Christopher Srinivasa +1
We present a novel hybrid algorithm for training Deep Neural Networks that combines the state-of-the-art Gradient Descent (GD) method with a Mixed Integer Linear Programming (MILP)…
xAI-GAN: Enhancing Generative Adversarial Networks via Explainable AI Systems
Vineel Nagisetty, Laura Graves, Joseph Scott +1
Generative Adversarial Networks (GANs) are a revolutionary class of Deep Neural Networks (DNNs) that have been successfully used to generate realistic images, music, text, and othe…