no code implementations • 26 Sep 2024 • Alexander Grushin, Walt Woods, Alvaro Velasquez, Simon Khan

Therefore, we seek to define a criticality framework with both a quantifiable ground truth and a clear significance to users.

no code implementations • 19 Sep 2024 • Hairi, Minghong Fang, Zifan Zhang, Alvaro Velasquez, Jia Liu

We investigate the multi-agent policy evaluation problem in the presence of Byzantine agents, particularly in the setting of heterogeneous local rewards.

no code implementations • 4 Sep 2024 • Joshua Pickard, Marc Andrew Choi, Natalie Oliven, Cooper Stansbury, Jillian Cwycyshyn, Nicholas Galioto, Alex Gorodetsky, Alvaro Velasquez, Indika Rajapakse

We present a prototype for a Bioinformatics Retrieval Augmentation Data (BRAD) digital assistant.

no code implementations • 6 Aug 2024 • Lekai Chen, Ashutosh Trivedi, Alvaro Velasquez

The emergence of intelligence in large language models (LLMs) has inspired investigations into their integration into automata learning.

no code implementations • 17 Jul 2024 • Kamal Acharya, Alvaro Velasquez, Yongxin Liu, Dahai Liu, Liang Sun, Houbing Song

We then propose a novel Neural Network (NN) accelerated Genetic Algorithm(GA) for evacuation planning.

no code implementations • 12 Jul 2024 • Kamal Acharya, Alvaro Velasquez, Houbing Herbert Song

This survey paper delves into the emerging and critical area of symbolic knowledge distillation in Large Language Models (LLMs).

no code implementations • 3 Jul 2024 • Walt Woods, Alexander Grushin, Simon Khan, Alvaro Velasquez

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.

no code implementations • 27 Jun 2024 • Ismail Alkhouri, Cedric Le Denmat, Yingjie Li, Cunxi Yu, Jia Liu, Rongrong Wang, Alvaro Velasquez

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.

no code implementations • 20 Jun 2024 • Noah Topper, Alvaro Velasquez, George Atia

Inverse reinforcement learning (IRL) is the problem of inferring a reward function from expert behavior.

no code implementations • 13 Jun 2024 • Prathyush Poduval, Zhuowen Zou, Alvaro Velasquez, Mohsen Imani

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.

no code implementations • 12 Mar 2024 • Hanning Chen, Wenjun Huang, Yang Ni, Sanggeon Yun, Yezi Liu, Fei Wen, Alvaro Velasquez, Hugo Latapie, Mohsen Imani

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.

1 code implementation • 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.

1 code implementation • 8 Sep 2023 • Abhinav Rajvanshi, Karan Sikka, Xiao Lin, Bhoram Lee, Han-Pang Chiu, Alvaro Velasquez

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.

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.

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.

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.

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