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Found 16 papers, 6 papers with code
General surgery vision transformer: A video pre-trained foundation model for general surgery
The absence of openly accessible data and specialized foundation models is a major barrier for computational research in surgery.
Addressing cognitive bias in medical language models
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.
General-purpose foundation models for increased autonomy in robot-assisted surgery
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
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
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
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
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
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
Grand efforts in neuroscience are working toward mapping the connectomes of many new species, including the near completion of the Drosophila melanogaster.
Learning to learn online with neuromodulated synaptic plasticity in spiking neural networks
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.
Stable Lifelong Learning: Spiking neurons as a solution to instability in plastic neural networks
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
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
Can reproduction alone in the context of survival produce intelligence in our machines?
SpikePropamine: Differentiable Plasticity in Spiking Neural Networks
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
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
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.
