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Found 15 papers, 5 papers with code
Retrospective for the Dynamic Sensorium Competition for predicting large-scale mouse primary visual cortex activity from videos
To address this gap, we established the Sensorium 2023 Benchmark Competition with dynamic input, featuring a new large-scale dataset from the primary visual cortex of ten mice.
Tx-LLM: A Large Language Model for Therapeutics
Developing therapeutics is a lengthy and expensive process that requires the satisfaction of many different criteria, and AI models capable of expediting the process would be invaluable.
Semantic Loss Functions for Neuro-Symbolic Structured Prediction
We discuss the semantic loss, which injects knowledge about such structure, defined symbolically, into training by minimizing the network's violation of such dependencies, steering the network towards predicting distributions satisfying the underlying structure.
Advancing Multimodal Medical Capabilities of Gemini
Building upon Gemini's multimodal models, we develop several models within the new Med-Gemini family that inherit core capabilities of Gemini and are optimized for medical use via fine-tuning with 2D and 3D radiology, histopathology, ophthalmology, dermatology and genomic data.
Security and Privacy Product Inclusion
In this paper, we explore the challenges of ensuring security and privacy for users from diverse demographic backgrounds.
Speeding up and reducing memory usage for scientific machine learning via mixed precision
Scientific machine learning (SciML) has emerged as a versatile approach to address complex computational science and engineering problems.
The Dynamic Sensorium competition for predicting large-scale mouse visual cortex activity from videos
We hope this competition will continue to strengthen the accompanying Sensorium benchmarks collection as a standard tool to measure progress in large-scale neural system identification models of the entire mouse visual hierarchy and beyond.
Multi-Rate VAE: Train Once, Get the Full Rate-Distortion Curve
Variational autoencoders (VAEs) are powerful tools for learning latent representations of data used in a wide range of applications.
Neuro-Symbolic Entropy Regularization
We propose a loss, neuro-symbolic entropy regularization, that encourages the model to confidently predict a valid object.
Probabilistic Sufficient Explanations
Understanding the behavior of learned classifiers is an important task, and various black-box explanations, logical reasoning approaches, and model-specific methods have been proposed.
Leveraging Unlabeled Data for Entity-Relation Extraction through Probabilistic Constraint Satisfaction
We study the problem of entity-relation extraction in the presence of symbolic domain knowledge.
Generalization in data-driven models of primary visual cortex
With this new readout we train our network on neural responses from mouse primary visual cortex (V1) and obtain a gain in performance of 7% compared to the previous state-of-the-art network.
Stochastic Optimization of Sorting Networks via Continuous Relaxations
Sorting input objects is an important step in many machine learning pipelines.
Large-Scale Deep Learning on the YFCC100M Dataset
We train our three-layer deep neural network on the Yahoo!
Joint Analysis of Time-Evolving Binary Matrices and Associated Documents
An objective of such analysis is to infer structure and inter-relationships underlying the matrices, here defined by latent features associated with each axis of the matrix.
