Search Results for author: Samuel Schmidgall

Found 17 papers, 6 papers with code

AgentClinic: a multimodal agent benchmark to evaluate AI in simulated clinical environments

no code implementations13 May 2024 Samuel Schmidgall, Rojin Ziaei, Carl Harris, Eduardo Reis, Jeffrey Jopling, Michael Moor

Here, we present AgentClinic: a multimodal benchmark to evaluate LLMs in their ability to operate as agents in simulated clinical environments.

Decision Making Question Answering

General surgery vision transformer: A video pre-trained foundation model for general surgery

1 code implementation9 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.

Video Prediction

Addressing cognitive bias in medical language models

1 code implementation12 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.

Decision Making

General-purpose foundation models for increased autonomy in robot-assisted surgery

no code implementations1 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.

Surgical Gym: A high-performance GPU-based platform for reinforcement learning with surgical robots

1 code implementation7 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.


Language models are susceptible to incorrect patient self-diagnosis in medical applications

no code implementations17 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.


Synaptic motor adaptation: A three-factor learning rule for adaptive robotic control in spiking neural networks

no code implementations2 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.

Brain-inspired learning in artificial neural networks: a review

no code implementations18 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.

NeuroBench: A Framework for Benchmarking Neuromorphic Computing Algorithms and Systems

1 code implementation10 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.


Biological connectomes as a representation for the architecture of artificial neural networks

no code implementations28 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.

Inductive Bias

Learning to learn online with neuromodulated synaptic plasticity in spiking neural networks

no code implementations25 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.

BIG-bench Machine Learning

Stable Lifelong Learning: Spiking neurons as a solution to instability in plastic neural networks

no code implementations7 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.

Self-Replicating Neural Programs

no code implementations27 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.

Evolutionary Self-Replication as a Mechanism for Producing Artificial Intelligence

1 code implementation16 Sep 2021 Samuel Schmidgall, Joseph Hays

Can reproduction alone in the context of survival produce intelligence in our machines?

Atari Games

SpikePropamine: Differentiable Plasticity in Spiking Neural Networks

no code implementations4 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.

Self-Constructing Neural Networks Through Random Mutation

1 code implementation29 Mar 2021 Samuel Schmidgall

The search for neural architecture is producing many of the most exciting results in artificial intelligence.

Adaptive Reinforcement Learning through Evolving Self-Modifying Neural Networks

no code implementations22 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.

Meta-Learning reinforcement-learning +1

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