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Found 16 papers, 5 papers with code
Predictive Analytics of Varieties of Potatoes
We explore the application of machine learning algorithms to predict the suitability of Russet potato clones for advancement in breeding trials.
A Robust, Efficient Predictive Safety Filter
In this paper, we propose a novel predictive safety filter that is robust to bounded perturbations and is implemented in an even-triggered fashion to reduce online computation.
Semi-Supervised Learning of Dynamical Systems with Neural Ordinary Differential Equations: A Teacher-Student Model Approach
Modeling dynamical systems is crucial for a wide range of tasks, but it remains challenging due to complex nonlinear dynamics, limited observations, or lack of prior knowledge.
Extreme Risk Mitigation in Reinforcement Learning using Extreme Value Theory
Our work proposes to enhance the resilience of RL agents when faced with very rare and risky events by focusing on refining the predictions of the extreme values predicted by the state-action value function distribution.
There is more to graphs than meets the eye: Learning universal features with self-supervision
We hypothesize that leveraging multiple graphs of the same type/class can improve the quality of learnt representations in the model by extracting features that are universal to the class of graphs.
Perspectives on AI Architectures and Co-design for Earth System Predictability
This paper discusses the topic of the `AI Architectures and Co-design' session and associated outcomes.
Deep Reinforcement Learning for Cyber System Defense under Dynamic Adversarial Uncertainties
Development of autonomous cyber system defense strategies and action recommendations in the real-world is challenging, and includes characterizing system state uncertainties and attack-defense dynamics.
Extending Conformal Prediction to Hidden Markov Models with Exact Validity via de Finetti's Theorem for Markov Chains
Conformal prediction is a widely used method to quantify the uncertainty of a classifier under the assumption of exchangeability (e. g., IID data).
Differentiable Predictive Control with Safety Guarantees: A Control Barrier Function Approach
We develop a novel form of differentiable predictive control (DPC) with safety and robustness guarantees based on control barrier functions.
Challenges and Opportunities in Deep Reinforcement Learning with Graph Neural Networks: A Comprehensive review of Algorithms and Applications
Deep reinforcement learning (DRL) has empowered a variety of artificial intelligence fields, including pattern recognition, robotics, recommendation-systems, and gaming.
GraMeR: Graph Meta Reinforcement Learning for Multi-Objective Influence Maximization
However, there are serious limitations in current approaches such as: (1) IM formulations only consider influence via spread and ignore self activation; (2) scalability to large graphs; (3) generalizability across graph families; (4) low computational efficiency with a large running time to identify seed sets for every test network.
Neural Lyapunov Differentiable Predictive Control
We present a learning-based predictive control methodology using the differentiable programming framework with probabilistic Lyapunov-based stability guarantees.
Learning Stochastic Parametric Differentiable Predictive Control Policies
The problem of synthesizing stochastic explicit model predictive control policies is known to be quickly intractable even for systems of modest complexity when using classical control-theoretic methods.
On the Stochastic Stability of Deep Markov Models
Deep Markov models (DMM) are generative models that are scalable and expressive generalization of Markov models for representation, learning, and inference problems.
V2W-BERT: A Framework for Effective Hierarchical Multiclass Classification of Software Vulnerabilities
Weaknesses in computer systems such as faults, bugs and errors in the architecture, design or implementation of software provide vulnerabilities that can be exploited by attackers to compromise the security of a system.
Ranked #1 on
General Classification
on CVE to CWE mapping
Feature Clustering for Accelerating Parallel Coordinate Descent
We give a unified convergence analysis for the family of block-greedy algorithms.
