no code implementations • 6 Feb 2024 • Yash Shukla, Tanushree Burman, Abhishek Kulkarni, Robert Wright, Alvaro Velasquez, Jivko Sinapov
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.
no code implementations • 6 Jan 2024 • Justus Renkhoff, Ke Feng, Marc Meier-Doernberg, Alvaro Velasquez, Houbing Herbert Song
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.
no code implementations • 15 Dec 2023 • Milad Kazemi, Mateo Perez, Fabio Somenzi, Sadegh Soudjani, Ashutosh Trivedi, Alvaro Velasquez
We present a modular approach to \emph{reinforcement learning} (RL) in environments consisting of simpler components evolving in parallel.
no code implementations • 29 Oct 2023 • Suraj Singireddy, Andre Beckus, George Atia, Sumit Jha, Alvaro Velasquez
Reinforcement learning (RL) is a powerful tool for finding optimal policies in sequential decision processes.
no code implementations • 14 Oct 2023 • Yash Shukla, Wenchang Gao, Vasanth Sarathy, Alvaro Velasquez, Robert Wright, Jivko Sinapov
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.
no code implementations • 11 Oct 2023 • Jingxuan Zhu, Alec Koppel, Alvaro Velasquez, Ji Liu
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.
no code implementations • 28 Sep 2023 • Sumit Kumar Jha, Susmit Jha, Patrick Lincoln, Nathaniel D. Bastian, Alvaro Velasquez, Rickard Ewetz, Sandeep Neema
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.
no code implementations • 27 Sep 2023 • Sumit Kumar Jha, Susmit Jha, Rickard Ewetz, Alvaro Velasquez
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.
no code implementations • 8 Sep 2023 • Abhinav Rajvanshi, Karan Sikka, Xiao Lin, Bhoram Lee, Han-Pang Chiu, Alvaro Velasquez
We present SayNav, a new approach that leverages human knowledge from Large Language Models (LLMs) for efficient generalization to complex navigation tasks in unknown large-scale environments.
no code implementations • 25 Jul 2023 • Alexander Grushin, Walt Woods, Alvaro Velasquez, Simon Khan
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.
no code implementations • 17 May 2023 • Yue Wang, Alvaro Velasquez, George Atia, Ashley Prater-Bennette, Shaofeng Zou
Robust Markov decision processes (MDPs) address the challenge of model uncertainty by optimizing the worst-case performance over an uncertainty set of MDPs.
1 code implementation • 11 Apr 2023 • Yash Shukla, Abhishek Kulkarni, Robert Wright, Alvaro Velasquez, Jivko Sinapov
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).
no code implementations • 1 Apr 2023 • Jingxuan Zhu, Alvaro Velasquez, Ji Liu
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.
no code implementations • 9 Mar 2023 • Wenkai Tan, Justus Renkhoff, Alvaro Velasquez, Ziyu Wang, Lusi Li, Jian Wang, Shuteng Niu, Fan Yang, Yongxin Liu, Houbing Song
Our work could provide a useful tool to defend against certain adversarial attacks on deep neural networks.
1 code implementation • 8 Mar 2023 • Justus Renkhoff, Wenkai Tan, Alvaro Velasquez, illiam Yichen Wang, Yongxin Liu, Jian Wang, Shuteng Niu, Lejla Begic Fazlic, Guido Dartmann, Houbing Song
Finally, we demonstrate that the layers $Block4\_conv1$ and $Block5\_cov1$ of the VGG-16 model are more susceptible to adversarial attacks.
no code implementations • 7 Mar 2023 • Lijing Zhu, Qizhen Lan, Alvaro Velasquez, Houbing Song, Acharya Kamal, Qing Tian, Shuteng Niu
Our method effectively captures the sentiment representation of HOIs by integrating both spatial and semantic knowledge.
Computational Efficiency Human-Object Interaction Detection +1
no code implementations • 10 Jan 2023 • Ismail Alkhouri, Sumit Jha, Andre Beckus, George Atia, Alvaro Velasquez, Rickard Ewetz, Arvind Ramanathan, Susmit Jha
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.
no code implementations • 2 Jan 2023 • Yue Wang, Alvaro Velasquez, George Atia, Ashley Prater-Bennette, Shaofeng Zou
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.
no code implementations • 26 Mar 2022 • Jingxuan Zhu, Yixuan Lin, Alvaro Velasquez, Ji Liu
This paper considers a resilient high-dimensional constrained consensus problem and studies a resilient distributed algorithm for complete graphs.
no code implementations • 15 Mar 2022 • Ismail R. Alkhouri, George K. Atia, Alvaro Velasquez
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.
no code implementations • 9 Sep 2021 • Sumit Kumar Jha, Arvind Ramanathan, Rickard Ewetz, Alvaro Velasquez, Susmit Jha
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.
1 code implementation • 1 Sep 2021 • Edward Verenich, Tobias Martin, Alvaro Velasquez, Nazar Khan, Faraz Hussain
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.
1 code implementation • 5 Aug 2021 • Ismail R. Alkhouri, Alvaro Velasquez, George K. Atia
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.
no code implementations • 9 Jul 2021 • Taylor Dohmen, Noah Topper, George Atia, Andre Beckus, Ashutosh Trivedi, Alvaro Velasquez
The success of reinforcement learning in typical settings is predicated on Markovian assumptions on the reward signal by which an agent learns optimal policies.
no code implementations • 5 Jun 2021 • Alvaro Velasquez, Ismail Alkhouri, Andre Beckus, Ashutosh Trivedi, George Atia
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.
no code implementations • 1 Jan 2021 • Sumit Kumar Jha, Susmit Jha, Rickard Ewetz, Alvaro Velasquez
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.
no code implementations • 3 Dec 2020 • George K. Atia, Andre Beckus, Ismail Alkhouri, Alvaro Velasquez
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.
no code implementations • 11 Nov 2020 • Wesley H. Brigner, Naimul Hassan, Xuan Hu, Christopher H. Bennett, Felipe Garcia-Sanchez, Can Cui, Alvaro Velasquez, Matthew J. Marinella, Jean Anne C. Incorvia, Joseph S. Friedman
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.
no code implementations • 17 Sep 2020 • Sumit Kumar Jha, Susmit Jha, Rickard Ewetz, Sunny Raj, Alvaro Velasquez, Laura L. Pullum, Ananthram Swami
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.
no code implementations • 6 Aug 2020 • Edward Verenich, Alvaro Velasquez, Nazar Khan, Faraz Hussain
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.
no code implementations • 24 Mar 2020 • Alvaro Velasquez, Christopher H. Bennett, Naimul Hassan, Wesley H. Brigner, Otitoaleke G. Akinola, Jean Anne C. Incorvia, Matthew J. Marinella, Joseph S. Friedman
We propose a hardware learning rule for unsupervised clustering within a novel spintronic computing architecture.
no code implementations • 4 Mar 2020 • Christopher H. Bennett, T. Patrick Xiao, Can Cui, Naimul Hassan, Otitoaleke G. Akinola, Jean Anne C. Incorvia, Alvaro Velasquez, Joseph S. Friedman, Matthew J. Marinella
Machine learning implements backpropagation via abundant training samples.
no code implementations • 29 Feb 2020 • Edward Verenich, Alvaro Velasquez, M. G. Sarwar Murshed, Faraz Hussain
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.
1 code implementation • 29 Feb 2020 • Rashik Shadman, M. G. Sarwar Murshed, Edward Verenich, Alvaro Velasquez, Faraz Hussain
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.