1 code implementation • 16 Aug 2017 • Alex Fukunaga, Adi Botea, Yuu Jinnai, Akihiro Kishimoto
A* is a best-first search algorithm for finding optimal-cost paths in graphs.
no code implementations • NeurIPS 2015 • Akihiro Kishimoto, Radu Marinescu, Adi Botea
The paper presents and evaluates the power of parallel search for exact MAP inference in graphical models.
no code implementations • 2 Feb 2019 • Adi Botea, Christian Muise, Shubham Agarwal, Oznur Alkan, Ondrej Bajgar, Elizabeth Daly, Akihiro Kishimoto, Luis Lastras, Radu Marinescu, Josef Ondrej, Pablo Pedemonte, Miroslav Vodolan
Dialogue systems have many applications such as customer support or question answering.
no code implementations • NeurIPS 2019 • Akihiro Kishimoto, Beat Buesser, Bei Chen, Adi Botea
Search techniques, such as Monte Carlo Tree Search (MCTS) and Proof-Number Search (PNS), are effective in playing and solving games.
no code implementations • 2 Jul 2020 • Tamami Nakano, Atsuya Sakata, Akihiro Kishimoto
Highlight detection in sports videos has a broad viewership and huge commercial potential.
no code implementations • 2 Jul 2021 • Paulito P. Palmes, Akihiro Kishimoto, Radu Marinescu, Parikshit Ram, Elizabeth Daly
The pipeline optimization problem in machine learning requires simultaneous optimization of pipeline structures and parameter adaptation of their elements.
no code implementations • ICML Workshop AutoML 2021 • Akihiro Kishimoto, Djallel Bouneffouf, Radu Marinescu, Parikshit Ram, Ambrish Rawat, Martin Wistuba, Paulito Pedregosa Palmes, Adi Botea
Optimizing a machine learning (ML) pipeline has been an important topic of AI and ML.
no code implementations • LNLS (ACL) 2022 • Ryokan Ri, Yufang Hou, Radu Marinescu, Akihiro Kishimoto
When mapping a natural language instruction to a sequence of actions, it is often useful toidentify sub-tasks in the instruction.
no code implementations • 28 Sep 2023 • Akihiro Kishimoto, Hiroshi Kajino, Masataka Hirose, Junta Fuchiwaki, Indra Priyadarsini, Lisa Hamada, Hajime Shinohara, Daiju Nakano, Seiji Takeda
Property prediction plays an important role in material discovery.
1 code implementation • 15 May 2023 • Achille Fokoue, Ibrahim Abdelaziz, Maxwell Crouse, Shajith Ikbal, Akihiro Kishimoto, Guilherme Lima, Ndivhuwo Makondo, Radu Marinescu
NIAGRA addresses this problem by using 1) improved Graph Neural Networks for learning name-invariant formula representations that is tailored for their unique characteristics and 2) an efficient ensemble approach for automated theorem proving.
1 code implementation • 20 Oct 2023 • Eduardo Soares, Akihiro Kishimoto, Emilio Vital Brazil, Seiji Takeda, Hiroshi Kajino, Renato Cerqueira
Pre-trained Language Models have emerged as promising tools for predicting molecular properties, yet their development is in its early stages, necessitating further research to enhance their efficacy and address challenges such as generalization and sample efficiency.
1 code implementation • 8 Jul 2022 • Matteo Manica, Jannis Born, Joris Cadow, Dimitrios Christofidellis, Ashish Dave, Dean Clarke, Yves Gaetan Nana Teukam, Giorgio Giannone, Samuel C. Hoffman, Matthew Buchan, Vijil Chenthamarakshan, Timothy Donovan, Hsiang Han Hsu, Federico Zipoli, Oliver Schilter, Akihiro Kishimoto, Lisa Hamada, Inkit Padhi, Karl Wehden, Lauren McHugh, Alexy Khrabrov, Payel Das, Seiji Takeda, John R. Smith
With the growing availability of data within various scientific domains, generative models hold enormous potential to accelerate scientific discovery.