55 citations · 125 across the 20 of their papers we have counts for
22 papers
A Personalised Learning Tool for Physics Undergraduate Students Built On a Large Language Model for Symbolic Regression
Yufan Zhu, Zi-Yu Khoo, Jonathan Sze Choong Low +1
Interleaved practice enhances the memory and problem-solving ability of students in undergraduate courses. We introduce a personalized learning tool built on a Large Language Model…
Expected Shapley-Like Scores of Boolean Functions: Complexity and Applications to Probabilistic Databases
Pratik Karmakar, Mikaël Monet, Pierre Senellart +1
Shapley values, originating in game theory and increasingly prominent in explainable AI, have been proposed to assess the contribution of facts in query answering over databases, a…
Celestial Machine Learning: Discovering the Planarity, Heliocentricity, and Orbital Equation of Mars with AI Feynman
Zi-Yu Khoo, Gokul Rajiv, Abel Yang +2
Can a machine or algorithm discover or learn the elliptical orbit of Mars from astronomical sightings alone? Johannes Kepler required two paradigm shifts to discover his First Law…
A Comparative Evaluation of Additive Separability Tests for Physics-Informed Machine Learning
Zi-Yu Khoo, Jonathan Sze Choong Low, Stéphane Bressan
Many functions characterising physical systems are additively separable. This is the case, for instance, of mechanical Hamiltonian functions in physics, population growth equations…
Celestial Machine Learning: From Data to Mars and Beyond with AI Feynman
Zi-Yu Khoo, Abel Yang, Jonathan Sze Choong Low +1
Can a machine or algorithm discover or learn Kepler's first law from astronomical sightings alone? We emulate Johannes Kepler's discovery of the equation of the orbit of Mars with…
What's Next? Predicting Hamiltonian Dynamics from Discrete Observations of a Vector Field
Zi-Yu Khoo, Delong Zhang, Stéphane Bressan
We present several methods for predicting the dynamics of Hamiltonian systems from discrete observations of their vector field. Each method is either informed or uninformed of the…
Separable Hamiltonian Neural Networks
Zi-Yu Khoo, Dawen Wu, Jonathan Sze Choong Low +1
Hamiltonian neural networks (HNNs) are state-of-the-art models that regress the vector field of a dynamical system under the learning bias of Hamilton's equations. A recent observa…
Extending JumpProcess.jl for fast point process simulation with time-varying intensities
Guilherme Augusto Zagatti, Samuel A. Isaacson, Christopher Rackauckas +3
Point processes model the occurrence of a countable number of random points over some support. They can model diverse phenomena, such as chemical reactions, stock market transactio…
Confidential Truth Finding with Multi-Party Computation (Extended Version)
Angelo Saadeh, Pierre Senellart, Stéphane Bressan
Federated knowledge discovery and data mining are challenged to assess the trustworthiness of data originating from autonomous sources while protecting confidentiality and privacy.…
Supervised Training of Neural-Network Quantum States for the Next Nearest Neighbor Ising model
Zheyu Wu, Remmy Zen, Heitor P. Casagrande +2
Different neural network architectures can be unsupervisedly or supervisedly trained to represent quantum states. We explore and compare different strategies for the supervised tra…
Syntax-informed Question Answering with Heterogeneous Graph Transformer
Fangyi Zhu, Lok You Tan, See-Kiong Ng +1
Large neural language models are steadily contributing state-of-the-art performance to question answering and other natural language and information processing tasks. These models…
COOL, a Context Outlooker, and its Application to Question Answering and other Natural Language Processing Tasks
Fangyi Zhu, See-Kiong Ng, Stéphane Bressan
Vision outlooker improves the performance of vision transformers, which implements a self-attention mechanism by adding an outlook attention, a form of local attention. In natural…
-net Induced Lazy Witness Complexes on Graphs
Naheed Anjum Arafat, Debabrota Basu, Stéphane Bressan
Computation of persistent homology of simplicial representations such as the Rips and the Cěch complexes do not efficiently scale to large point clouds. It is, therefore, meaningfu…
Differential Privacy at Risk: Bridging Randomness and Privacy Budget
Ashish Dandekar, Debabrota Basu, Stephane Bressan
The calibration of noise for a privacy-preserving mechanism depends on the sensitivity of the query and the prescribed privacy level. A data steward must make the non-trivial choic…
Construction and Random Generation of Hypergraphs with Prescribed Degree and Dimension Sequences
Naheed Anjum Arafat, Debabrota Basu, Laurent Decreusefond +1
We propose algorithms for construction and random generation of hypergraphs without loops and with prescribed degree and dimension sequences. The objective is to provide a starting…
Finding Quantum Critical Points with Neural-Network Quantum States
Remmy Zen, Long My, Ryan Tan +5
Finding the precise location of quantum critical points is of particular importance to characterise quantum many-body systems at zero temperature. However, quantum many-body system…
Transfer learning for scalability of neural-network quantum states
Remmy Zen, Long My, Ryan Tan +5
Neural-network quantum states have shown great potential for the study of many-body quantum systems. In statistical machine learning, transfer learning designates protocols reusing…
Topological Data Analysis with -net Induced Lazy Witness Complex
Naheed Anjum Arafat, Debabrota Basu, Stéphane Bressan
Topological data analysis computes and analyses topological features of the point clouds by constructing and studying a simplicial representation of the underlying topological stru…
An Efficient and Truthful Pricing Mechanism for Team Formation in Crowdsourcing Markets
Qing Liu, Tie Luo, Ruiming Tang +1
In a crowdsourcing market, a requester is looking to form a team of workers to perform a complex task that requires a variety of skills. Candidate workers advertise their certified…
BelMan: Bayesian Bandits on the Belief--Reward Manifold
Debabrota Basu, Pierre Senellart, Stéphane Bressan
We propose a generic, Bayesian, information geometric approach to the exploration--exploitation trade-off in multi-armed bandit problems. Our approach, BelMan, uniformly supports p…
Computing the Homology of Hypergraphs
Shiquan Ren, Chengyuan Wu, Stephane Bressan +1
Hypergraph is a topological model for networks. In order to study the topology of hypergraphs, the homology of the associated simplicial complexes and the embedded homology have be…
The Embedded Homology of Hypergraphs and Applications
Stephane Bressan, Jingyan Li, Shiquan Ren +1
Hypergraphs are mathematical models for many problems in data sciences. In recent decades, the topological properties of hypergraphs have been studied and various kinds of (co)homo…