no code implementations • 29 Apr 2024 • Nathan Huetsch, Javier Mariño Villadamigo, Alexander Shmakov, Sascha Diefenbacher, Vinicius Mikuni, Theo Heimel, Michael Fenton, Kevin Greif, Benjamin Nachman, Daniel Whiteson, Anja Butter, Tilman Plehn
Recent innovations from machine learning allow for data unfolding, without binning and including correlations across many dimensions.
no code implementations • 22 Apr 2024 • Alexander Shmakov, Kevin Greif, Michael James Fenton, Aishik Ghosh, Pierre Baldi, Daniel Whiteson
The measurements performed by particle physics experiments must account for the imperfect response of the detectors used to observe the interactions.
no code implementations • 17 Apr 2024 • Soumyendu Sarkar, Vineet Gundecha, Sahand Ghorbanpour, Alexander Shmakov, Ashwin Ramesh Babu, Avisek Naug, Alexandre Pichard, Mathieu Cocho
Our results show that the transformer model of moderate depth with gated residual connections around the multi-head attention, multi-layer perceptron, and the transformer block (STrXL) proposed in this paper is optimal and boosts energy efficiency by an average of 22. 1% for these complex spread waves over the existing spring damper (SD) controller.
no code implementations • 5 Oct 2023 • Alexander Shmakov, Avisek Naug, Vineet Gundecha, Sahand Ghorbanpour, Ricardo Luna Gutierrez, Ashwin Ramesh Babu, Antonio Guillen, Soumyendu Sarkar
In this paper, we combine recent developments in Deep Kernel Learning (DKL) and attention-based Transformer models to improve the modeling powers of GP surrogates with meta-learning.
no code implementations • 5 Sep 2023 • Michael James Fenton, Alexander Shmakov, Hideki Okawa, Yuji Li, Ko-Yang Hsiao, Shih-Chieh Hsu, Daniel Whiteson, Pierre Baldi
We explore the performance of the extended capability of SPA-NET in the context of semi-leptonic decays of top quark pairs as well as top quark pairs produced in association with a Higgs boson.
no code implementations • 10 Mar 2023 • Alexander Shmakov, Alejandro Yankelevich, Jianming Bian, Pierre Baldi
TransformerCVN classifies events with 90\% accuracy and improves the reconstruction of individual particles by 6\% over baseline methods which lack the integrated architecture of TransformerCVN.
no code implementations • 13 Sep 2022 • Soumyendu Sarkar, Vineet Gundecha, Sahand Ghorbanpour, Alexander Shmakov, Ashwin Ramesh Babu, Alexandre Pichard, Mathieu Cocho
Recent Wave Energy Converters (WEC) are equipped with multiple legs and generators to maximize energy generation.
Multi-agent Reinforcement Learning reinforcement-learning +1
no code implementations • 6 Jun 2022 • Alexander Shmakov, Mohammadamin Tavakoli, Pierre Baldi, Christopher M. Karwin, Alex Broughton, Simona Murgia
A significant point-like component from the small scale (or discrete) structure in the H2 interstellar gas might be present in the Fermi-LAT data, but modeling this emission relies on observations of rare gas tracers only available in limited regions of the sky.
no code implementations • 2 Jan 2022 • Mohammadamin Tavakoli, Alexander Shmakov, Francesco Ceccarelli, Pierre Baldi
To achieve such skills, it is important to develop good representations of chemical reactions, or good deep learning architectures that can learn such representations automatically from the data.
1 code implementation • 7 Jun 2021 • Alexander Shmakov, Michael James Fenton, Ta-Wei Ho, Shih-Chieh Hsu, Daniel Whiteson, Pierre Baldi
The creation of unstable heavy particles at the Large Hadron Collider is the most direct way to address some of the deepest open questions in physics.
no code implementations • 8 Feb 2021 • Forest Agostinelli, Alexander Shmakov, Stephen Mcaleer, Roy Fox, Pierre Baldi
We use Q* search to solve the Rubik's cube when formulated with a large action space that includes 1872 meta-actions and find that this 157-fold increase in the size of the action space incurs less than a 4-fold increase in computation time and less than a 3-fold increase in number of nodes generated when performing Q* search.
1 code implementation • 19 Oct 2020 • Michael James Fenton, Alexander Shmakov, Ta-Wei Ho, Shih-Chieh Hsu, Daniel Whiteson, Pierre Baldi
Top quarks, produced in large numbers at the Large Hadron Collider, have a complex detector signature and require special reconstruction techniques.
no code implementations • 10 Dec 2019 • Alexander Shmakov, John Lanier, Stephen Mcaleer, Rohan Achar, Cristina Lopes, Pierre Baldi
Much of recent success in multiagent reinforcement learning has been in two-player zero-sum games.
Multiagent Systems
no code implementations • ICLR 2019 • Stephen McAleer, Forest Agostinelli, Alexander Shmakov, Pierre Baldi
Autodidactic Iteration is able to learn how to solve the Rubik’s Cube and the 15-puzzle without relying on human data.
9 code implementations • 18 May 2018 • Stephen McAleer, Forest Agostinelli, Alexander Shmakov, Pierre Baldi
A generally intelligent agent must be able to teach itself how to solve problems in complex domains with minimal human supervision.