1 code implementation • 9 Mar 2024 • Samuel Schmidgall, Ji Woong Kim, Jeffrey Jopling, Axel Krieger
The absence of openly accessible data and specialized foundation models is a major barrier for computational research in surgery.
1 code implementation • 12 Feb 2024 • Samuel Schmidgall, Carl Harris, Ime Essien, Daniel Olshvang, Tawsifur Rahman, Ji Woong Kim, Rojin Ziaei, Jason Eshraghian, Peter Abadir, Rama Chellappa
There is increasing interest in the application large language models (LLMs) to the medical field, in part because of their impressive performance on medical exam questions.
no code implementations • 1 Jan 2024 • Samuel Schmidgall, Ji Woong Kim, Alan Kuntz, Ahmed Ezzat Ghazi, Axel Krieger
The dominant paradigm for end-to-end robot learning focuses on optimizing task-specific objectives that solve a single robotic problem such as picking up an object or reaching a target position.
1 code implementation • 7 Oct 2023 • Samuel Schmidgall, Axel Krieger, Jason Eshraghian
Recent advances in robot-assisted surgery have resulted in progressively more precise, efficient, and minimally invasive procedures, sparking a new era of robotic surgical intervention.
no code implementations • 17 Sep 2023 • Rojin Ziaei, Samuel Schmidgall
Large language models (LLMs) are becoming increasingly relevant as a potential tool for healthcare, aiding communication between clinicians, researchers, and patients.
no code implementations • 2 Jun 2023 • Samuel Schmidgall, Joe Hays
Legged robots operating in real-world environments must possess the ability to rapidly adapt to unexpected conditions, such as changing terrains and varying payloads.
no code implementations • 18 May 2023 • Samuel Schmidgall, Jascha Achterberg, Thomas Miconi, Louis Kirsch, Rojin Ziaei, S. Pardis Hajiseyedrazi, Jason Eshraghian
Artificial neural networks (ANNs) have emerged as an essential tool in machine learning, achieving remarkable success across diverse domains, including image and speech generation, game playing, and robotics.
1 code implementation • 10 Apr 2023 • Jason Yik, Korneel Van den Berghe, Douwe den Blanken, Younes Bouhadjar, Maxime Fabre, Paul Hueber, Denis Kleyko, Noah Pacik-Nelson, Pao-Sheng Vincent Sun, Guangzhi Tang, Shenqi Wang, Biyan Zhou, Soikat Hasan Ahmed, George Vathakkattil Joseph, Benedetto Leto, Aurora Micheli, Anurag Kumar Mishra, Gregor Lenz, Tao Sun, Zergham Ahmed, Mahmoud Akl, Brian Anderson, Andreas G. Andreou, Chiara Bartolozzi, Arindam Basu, Petrut Bogdan, Sander Bohte, Sonia Buckley, Gert Cauwenberghs, Elisabetta Chicca, Federico Corradi, Guido de Croon, Andreea Danielescu, Anurag Daram, Mike Davies, Yigit Demirag, Jason Eshraghian, Tobias Fischer, Jeremy Forest, Vittorio Fra, Steve Furber, P. Michael Furlong, William Gilpin, Aditya Gilra, Hector A. Gonzalez, Giacomo Indiveri, Siddharth Joshi, Vedant Karia, Lyes Khacef, James C. Knight, Laura Kriener, Rajkumar Kubendran, Dhireesha Kudithipudi, Yao-Hong Liu, Shih-Chii Liu, Haoyuan Ma, Rajit Manohar, Josep Maria Margarit-Taulé, Christian Mayr, Konstantinos Michmizos, Dylan Muir, Emre Neftci, Thomas Nowotny, Fabrizio Ottati, Ayca Ozcelikkale, Priyadarshini Panda, Jongkil Park, Melika Payvand, Christian Pehle, Mihai A. Petrovici, Alessandro Pierro, Christoph Posch, Alpha Renner, Yulia Sandamirskaya, Clemens JS Schaefer, André van Schaik, Johannes Schemmel, Samuel Schmidgall, Catherine Schuman, Jae-sun Seo, Sadique Sheik, Sumit Bam Shrestha, Manolis Sifalakis, Amos Sironi, Matthew Stewart, Kenneth Stewart, Terrence C. Stewart, Philipp Stratmann, Jonathan Timcheck, Nergis Tömen, Gianvito Urgese, Marian Verhelst, Craig M. Vineyard, Bernhard Vogginger, Amirreza Yousefzadeh, Fatima Tuz Zohora, Charlotte Frenkel, Vijay Janapa Reddi
The NeuroBench framework introduces a common set of tools and systematic methodology for inclusive benchmark measurement, delivering an objective reference framework for quantifying neuromorphic approaches in both hardware-independent (algorithm track) and hardware-dependent (system track) settings.
no code implementations • 28 Sep 2022 • Samuel Schmidgall, Catherine Schuman, Maryam Parsa
Grand efforts in neuroscience are working toward mapping the connectomes of many new species, including the near completion of the Drosophila melanogaster.
no code implementations • 25 Jun 2022 • Samuel Schmidgall, Joe Hays
We propose that in order to harness our understanding of neuroscience toward machine learning, we must first have powerful tools for training brain-like models of learning.
no code implementations • 7 Nov 2021 • Samuel Schmidgall, Joe Hays
A recent resurgence of interest has developed in utilizing Artificial Neural Networks (ANNs) together with synaptic plasticity for intra-lifetime learning.
no code implementations • 27 Sep 2021 • Samuel Schmidgall
In this work, a neural network is trained to replicate the code that trains it using only its own output as input.
1 code implementation • 16 Sep 2021 • Samuel Schmidgall, Joseph Hays
Can reproduction alone in the context of survival produce intelligence in our machines?
no code implementations • 4 Jun 2021 • Samuel Schmidgall, Julia Ashkanazy, Wallace Lawson, Joe Hays
The adaptive changes in synaptic efficacy that occur between spiking neurons have been demonstrated to play a critical role in learning for biological neural networks.
1 code implementation • 29 Mar 2021 • Samuel Schmidgall
The search for neural architecture is producing many of the most exciting results in artificial intelligence.
no code implementations • 22 May 2020 • Samuel Schmidgall
The adaptive learning capabilities seen in biological neural networks are largely a product of the self-modifying behavior emerging from online plastic changes in synaptic connectivity.