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Found 32 papers, 6 papers with code
On the Prospects of Incorporating Large Language Models (LLMs) in Automated Planning and Scheduling (APS)
Automated Planning and Scheduling is among the growing areas in Artificial Intelligence (AI) where mention of LLMs has gained popularity.
AI Hilbert: A New Paradigm for Scientific Discovery by Unifying Data and Background Knowledge
The discovery of scientific formulae that parsimoniously explain natural phenomena and align with existing background theory is a key goal in science.
Value-based Fast and Slow AI Nudging
Values that are relevant to a specific decision scenario are used to decide when and how to use each of these nudging modalities.
Understanding the Capabilities of Large Language Models for Automated Planning
Firstly, we want to understand the extent to which LLMs can be used for plan generation.
Fast and Slow Planning
The concept of Artificial Intelligence has gained a lot of attention over the last decade.
Plansformer: Generating Symbolic Plans using Transformers
Large Language Models (LLMs) have been the subject of active research, significantly advancing the field of Natural Language Processing (NLP).
Bayesian Experimental Design for Symbolic Discovery
This study concerns the formulation and application of Bayesian optimal experimental design to symbolic discovery, which is the inference from observational data of predictive models taking general functional forms.
Topological data analysis on noisy quantum computers
In this study, we present NISQ-TDA, a fully implemented end-to-end quantum machine learning algorithm needing only a short circuit-depth, that is applicable to high-dimensional classical data, and with provable asymptotic speedup for certain classes of problems.
Distributed Adversarial Training to Robustify Deep Neural Networks at Scale
Spurred by that, we propose distributed adversarial training (DAT), a large-batch adversarial training framework implemented over multiple machines.
PCENet: High Dimensional Surrogate Modeling for Learning Uncertainty
In this paper, we present a novel surrogate model for representation learning and uncertainty quantification, which aims to deal with data of moderate to high dimensions.
Combining Fast and Slow Thinking for Human-like and Efficient Navigation in Constrained Environments
Current AI systems lack several important human capabilities, such as adaptability, generalizability, self-control, consistency, common sense, and causal reasoning.
Thinking Fast and Slow in AI: the Role of Metacognition
AI systems have seen dramatic advancement in recent years, bringing many applications that pervade our everyday life.
AI Descartes: Combining Data and Theory for Derivable Scientific Discovery
We develop a method to enable principled derivations of models of natural phenomena from axiomatic knowledge and experimental data by combining logical reasoning with symbolic regression.
Quantum Topological Data Analysis with Linear Depth and Exponential Speedup
In this paper, we completely overhaul the QTDA algorithm to achieve an improved exponential speedup and depth complexity of $O(n\log(1/(\delta\epsilon)))$.
E-PDDL: A Standardized Way of Defining Epistemic Planning Problems
Epistemic Planning (EP) refers to an automated planning setting where the agent reasons in the space of knowledge states and tries to find a plan to reach a desirable state from the current state.
Denoising quantum states with Quantum Autoencoders -- Theory and Applications
We implement a Quantum Autoencoder (QAE) as a quantum circuit capable of correcting Greenberger-Horne-Zeilinger (GHZ) states subject to various noisy quantum channels : the bit-flip channel and the more general quantum depolarizing channel.
Overcoming Catastrophic Forgetting via Direction-Constrained Optimization
We study the principal directions of the trajectory of the optimizer after convergence and show that traveling along a few top principal directions can quickly bring the parameters outside the cone but this is not the case for the remaining directions.
Quantum-Inspired Algorithms from Randomized Numerical Linear Algebra
We create classical (non-quantum) dynamic data structures supporting queries for recommender systems and least-squares regression that are comparable to their quantum analogues.
Projection techniques to update the truncated SVD of evolving matrices
This paper considers the problem of updating the rank-k truncated Singular Value Decomposition (SVD) of matrices subject to the addition of new rows and/or columns over time.
Thinking Fast and Slow in AI
This paper proposes a research direction to advance AI which draws inspiration from cognitive theories of human decision making.
Dynamic graph and polynomial chaos based models for contact tracing data analysis and optimal testing prescription
Thus, estimation of state uncertainty is paramount for both eminent risk assessment, as well as for closing the tracing-testing loop by optimal testing prescription.
Symbolic Regression using Mixed-Integer Nonlinear Optimization
The Symbolic Regression (SR) problem, where the goal is to find a regression function that does not have a pre-specified form but is any function that can be composed of a list of operators, is a hard problem in machine learning, both theoretically and computationally.
Dynamic Graph Convolutional Networks Using the Tensor M-Product
In recent years, a variety of graph neural networks (GNNs) have been successfully applied for representation learning and prediction on such graphs.
Recurrent Neural Networks in the Eye of Differential Equations
To understand the fundamental trade-offs between training stability, temporal dynamics and architectural complexity of recurrent neural networks~(RNNs), we directly analyze RNN architectures using numerical methods of ordinary differential equations~(ODEs).
Stable Tensor Neural Networks for Rapid Deep Learning
To exemplify the elegant, matrix-mimetic algebraic structure of our $t$-NNs, we expand on recent work (Haber and Ruthotto, 2017) which interprets deep neural networks as discretizations of non-linear differential equations and introduces stable neural networks which promote superior generalization.
Unification of Recurrent Neural Network Architectures and Quantum Inspired Stable Design
We show that most of the existing proposals of RNN architectures belong to different orders of $n$-$t$-ORNNs.
Should You Derive, Or Let the Data Drive? An Optimization Framework for Hybrid First-Principles Data-Driven Modeling
This work takes a different perspective and targets the construction of a correction model operator with implicit attributes.
Globally Optimal Symbolic Regression
In this study we introduce a new technique for symbolic regression that guarantees global optimality.
Breaking the 49-Qubit Barrier in the Simulation of Quantum Circuits
With the current rate of progress in quantum computing technologies, 50-qubit systems will soon become a reality.
Quantum Physics
Image classification using local tensor singular value decompositions
From linear classifiers to neural networks, image classification has been a widely explored topic in mathematics, and many algorithms have proven to be effective classifiers.
Experimental Design for Non-Parametric Correction of Misspecified Dynamical Models
We consider a class of misspecified dynamical models where the governing term is only approximately known.
Accelerating Hessian-free optimization for deep neural networks by implicit preconditioning and sampling
This study aims at speeding up Hessian-free training, both by means of decreasing the amount of data used for training, as well as through reduction of the number of Krylov subspace solver iterations used for implicit estimation of the Hessian.
