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Found 18 papers, 6 papers with code
Vision-language models for decoding provider attention during neonatal resuscitation
Neonatal resuscitations demand an exceptional level of attentiveness from providers, who must process multiple streams of information simultaneously.
The neuroconnectionist research programme
Artificial Neural Networks (ANNs) inspired by biology are beginning to be widely used to model behavioral and neural data, an approach we call neuroconnectionism.
MoVi: A large multi-purpose human motion and video dataset
Large high-quality datasets of human body shape and kinematics lay the foundation for modelling and simulation approaches in computer vision, computer graphics, and biomechanics.
A critical reappraisal of predicting suicidal ideation using fMRI
For many psychiatric disorders, neuroimaging offers a potential for revolutionizing diagnosis, and potentially treatment, by providing access to preverbal mental processes.
A Philosophical Understanding of Representation for Neuroscience
The reasons to call some neural activity a representation and the assumptions that come with this term are not generally made clear from its common uses in neuroscience.
Neuromatch Academy: Teaching Computational Neuroscience with global accessibility
Neuromatch Academy designed and ran a fully online 3-week Computational Neuroscience summer school for 1757 students with 191 teaching assistants working in virtual inverted (or flipped) classrooms and on small group projects.
MoVi: A Large Multipurpose Motion and Video Dataset
In five capture rounds, the same actors and movements were recorded using different hardware systems, including an optical motion capture system, video cameras, and inertial measurement units (IMU).
End-to-end Training of CNN-CRF via Differentiable Dual-Decomposition
These algorithms can efficiently and exactly solve sub-problems and directly optimize a convex upper bound of the real problem, providing optimality certificates on the way.
Claim Extraction in Biomedical Publications using Deep Discourse Model and Transfer Learning
Claims are a fundamental unit of scientific discourse.
Rarely-switching linear bandits: optimization of causal effects for the real world
Using this idea, and the theory of linear contextual bandits, we present a conservative policy updating procedure which updates a deterministic policy only when justified.
Accelerating Dynamic Programs via Nested Benders Decomposition with Application to Multi-Person Pose Estimation
We present a novel approach to solve dynamic programs (DP), which are frequent in computer vision, on tree-structured graphs with exponential node state space.
Measuring and regularizing networks in function space
To optimize a neural network one often thinks of optimizing its parameters, but it is ultimately a matter of optimizing the function that maps inputs to outputs.
Improving generalization by regularizing in $L^2$ function space
The resulting learning rule, which we call Hilbert-constrained gradient descent (HCGD), is thus closely related to the natural gradient but regularizes a different and more calculable metric over the space of functions.
Exploiting skeletal structure in computer vision annotation with Benders decomposition
Many annotation problems in computer vision can be phrased as integer linear programs (ILPs).
Towards an integration of deep learning and neuroscience
We hypothesize that (1) the brain optimizes cost functions, (2) these cost functions are diverse and differ across brain locations and over development, and (3) optimization operates within a pre-structured architecture matched to the computational problems posed by behavior.
Neurons and Cognition
Science Concierge: A fast content-based recommendation system for scientific publications
Here, we develop an algorithm, and an accompanying Python library, that implements a recommendation system based on the content of articles.
Convex Relaxation Regression: Black-Box Optimization of Smooth Functions by Learning Their Convex Envelopes
Our approach enables the use of convex optimization tools to solve a class of non-convex optimization problems.
Automatic discovery of cell types and microcircuitry from neural connectomics
Neural connectomics has begun producing massive amounts of data, necessitating new analysis methods to discover the biological and computational structure.
