Search Results for author:
Found 45 papers, 7 papers with code
Criticality and Safety Margins for Reinforcement Learning
Therefore, we seek to define a criticality framework with both a quantifiable ground truth and a clear significance to users.
On the Hardness of Decentralized Multi-Agent Policy Evaluation under Byzantine Attacks
We investigate the multi-agent policy evaluation problem in the presence of Byzantine agents, particularly in the setting of heterogeneous local rewards.
Bioinformatics Retrieval Augmentation Data (BRAD) Digital Assistant
We present a prototype for a Bioinformatics Retrieval Augmentation Data (BRAD) digital assistant.
LLMs as Probabilistic Minimally Adequate Teachers for DFA Learning
The emergence of intelligence in large language models (LLMs) has inspired investigations into their integration into automata learning.
Improving Air Mobility for Pre-Disaster Planning with Neural Network Accelerated Genetic Algorithm
We then propose a novel Neural Network (NN) accelerated Genetic Algorithm(GA) for evacuation planning.
A Survey on Symbolic Knowledge Distillation of Large Language Models
This survey paper delves into the emerging and critical area of symbolic knowledge distillation in Large Language Models (LLMs).
Combining AI Control Systems and Human Decision Support via Robustness and Criticality
One way of addressing these concerns is to leverage AI control systems alongside and in support of human decisions, relying on the AI control system in safe situations while calling on a human co-decider for critical situations.
Dataless Quadratic Neural Networks for the Maximum Independent Set Problem
More specifically, the graph structure and constraints of the MIS instance are used to define the structure and parameters of the neural network such that training it on a fixed input provides a solution to the problem, thereby setting it apart from traditional supervised or reinforcement learning approaches.
Bayesian Inverse Reinforcement Learning for Non-Markovian Rewards
Inverse reinforcement learning (IRL) is the problem of inferring a reward function from expert behavior.
Hyperdimensional Quantum Factorization
This paper presents a quantum algorithm for efficiently decoding hypervectors, a crucial process in extracting atomic elements from hypervectors - an essential task in Hyperdimensional Computing (HDC) models for interpretable learning and information retrieval.
TaskCLIP: Extend Large Vision-Language Model for Task Oriented Object Detection
Nevertheless, the naive application of VLMs leads to sub-optimal quality, due to the misalignment between embeddings of object images and their visual attributes, which are mainly adjective phrases.
Logical Specifications-guided Dynamic Task Sampling for Reinforcement Learning Agents
In this work, we propose a novel approach, called Logical Specifications-guided Dynamic Task Sampling (LSTS), that learns a set of RL policies to guide an agent from an initial state to a goal state based on a high-level task specification, while minimizing the number of environmental interactions.
A Survey on Verification and Validation, Testing and Evaluations of Neurosymbolic Artificial Intelligence
Since neurosymbolic AI combines the advantages of both symbolic and sub-symbolic AI, this survey explores how neurosymbolic applications can ease the V&V process.
Assume-Guarantee Reinforcement Learning
We present a modular approach to \emph{reinforcement learning} (RL) in environments consisting of simpler components evolving in parallel.
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.
LgTS: Dynamic Task Sampling using LLM-generated sub-goals for Reinforcement Learning Agents
In this work, we propose LgTS (LLM-guided Teacher-Student learning), a novel approach that explores the planning abilities of LLMs to provide a graphical representation of the sub-goals to a reinforcement learning (RL) agent that does not have access to the transition dynamics of the environment.
Byzantine-Resilient Decentralized Multi-Armed Bandits
In decentralized cooperative multi-armed bandits (MAB), each agent observes a distinct stream of rewards, and seeks to exchange information with others to select a sequence of arms so as to minimize its regret.
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.
SayNav: Grounding Large Language Models for Dynamic Planning to Navigation in New Environments
We evaluate SayNav on multi-object navigation (MultiON) task, that requires the agent to utilize a massive amount of human knowledge to efficiently search multiple different objects in an unknown environment.
Safety Margins for Reinforcement Learning
Proxy criticality metrics that are computable in real-time (i. e., without actually simulating the effects of random actions) can be compared to the true criticality, and we show how to leverage these proxy metrics to generate safety margins, which directly tie the consequences of potentially incorrect actions to an anticipated loss in overall performance.
Model-Free Robust Average-Reward Reinforcement Learning
Robust Markov decision processes (MDPs) address the challenge of model uncertainty by optimizing the worst-case performance over an uncertainty set of MDPs.
Automaton-Guided Curriculum Generation for Reinforcement Learning Agents
Experiments in gridworld and physics-based simulated robotics domains show that the curricula produced by AGCL achieve improved time-to-threshold performance on a complex sequential decision-making problem relative to state-of-the-art curriculum learning (e. g, teacher-student, self-play) and automaton-guided reinforcement learning baselines (e. g, Q-Learning for Reward Machines).
A Resilient Distributed Algorithm for Solving Linear Equations
This paper presents a resilient distributed algorithm for solving a system of linear algebraic equations over a multi-agent network in the presence of Byzantine agents capable of arbitrarily introducing untrustworthy information in communication.
NoiseCAM: Explainable AI for the Boundary Between Noise and Adversarial Attacks
Our work could provide a useful tool to defend against certain adversarial attacks on deep neural networks.
Exploring Adversarial Attacks on Neural Networks: An Explainable Approach
Finally, we demonstrate that the layers $Block4\_conv1$ and $Block5\_cov1$ of the VGG-16 model are more susceptible to adversarial attacks.
TMHOI: Translational Model for Human-Object Interaction Detection
Our method effectively captures the sentiment representation of HOIs by integrating both spatial and semantic knowledge.
Computational Efficiency
Human-Object Interaction Detection
+1
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.
Robust Average-Reward Markov Decision Processes
We derive the robust Bellman equation for robust average-reward MDPs, prove that the optimal policy can be derived from its solution, and further design a robust relative value iteration algorithm that provably finds its solution, or equivalently, the optimal robust policy.
Resilient Constrained Consensus over Complete Graphs via Feasibility Redundancy
This paper considers a resilient high-dimensional constrained consensus problem and studies a resilient distributed algorithm for complete graphs.
A Differentiable Approach to Combinatorial Optimization using Dataless Neural Networks
In particular, we reduce the combinatorial optimization problem to a neural network and employ a dataless training scheme to refine the parameters of the network such that those parameters yield the structure of interest.
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.
Pulmonary Disease Classification Using Globally Correlated Maximum Likelihood: an Auxiliary Attention mechanism for Convolutional Neural Networks
Two complementary generalization properties of CNNs, translation invariance and equivariance, are particularly useful in detecting manifested abnormalities associated with pulmonary disease, regardless of their spatial locations within the image.
BOSS: Bidirectional One-Shot Synthesis of Adversarial Examples
To this end, we present a problem that encodes objectives on the distance between the desired and output distributions of the trained model and the similarity between such inputs and the synthesized examples.
Inferring Probabilistic Reward Machines from Non-Markovian Reward Processes for Reinforcement Learning
The success of reinforcement learning in typical settings is predicated on Markovian assumptions on the reward signal by which an agent learns optimal policies.
Controller Synthesis for Omega-Regular and Steady-State Specifications
Given a Markov decision process (MDP) and a linear-time ($\omega$-regular or LTL) specification, the controller synthesis problem aims to compute the optimal policy that satisfies the specification.
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.
Steady-State Planning in Expected Reward Multichain MDPs
In this paper, we explore this steady-state planning problem that consists of deriving a decision-making policy for an agent such that constraints on its steady-state behavior are satisfied.
Domain Wall Leaky Integrate-and-Fire Neurons with Shape-Based Configurable Activation Functions
This work proposes modifications to these spintronic neurons that enable configuration of the activation functions through control of the shape of a magnetic domain wall track.
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.
Improving Explainability of Image Classification in Scenarios with Class Overlap: Application to COVID-19 and Pneumonia
Trust in predictions made by machine learning models is increased if the model generalizes well on previously unseen samples and when inference is accompanied by cogent explanations of the reasoning behind predictions.
Unsupervised Competitive Hardware Learning Rule for Spintronic Clustering Architecture
We propose a hardware learning rule for unsupervised clustering within a novel spintronic computing architecture.
Plasticity-Enhanced Domain-Wall MTJ Neural Networks for Energy-Efficient Online Learning
Machine learning implements backpropagation via abundant training samples.
The Utility of Feature Reuse: Transfer Learning in Data-Starved Regimes
The use of transfer learning with deep neural networks has increasingly become widespread for deploying well-tested computer vision systems to newer domains, especially those with limited datasets.
FlexServe: Deployment of PyTorch Models as Flexible REST Endpoints
The integration of artificial intelligence capabilities into modern software systems is increasingly being simplified through the use of cloud-based machine learning services and representational state transfer architecture design.
