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Found 9 papers, 2 papers with code
SEEDS: Emulation of Weather Forecast Ensembles with Diffusion Models
The dominant approach to representing uncertainty in weather forecasting is to generate an ensemble of forecasts.
Deep Learning Models for Predicting Wildfires from Historical Remote-Sensing Data
Identifying regions that have high likelihood for wildfires is a key component of land and forestry management and disaster preparedness.
Zero-Shot Heterogeneous Transfer Learning from Recommender Systems to Cold-Start Search Retrieval
In light of these problems, we observed that most online content platforms have both a search and a recommender system that, while having heterogeneous input spaces, can be connected through their common output item space and a shared semantic representation.
Neural Collaborative Filtering vs. Matrix Factorization Revisited
This approach is often referred to as neural collaborative filtering (NCF).
Ranked #6 on
Link Prediction
on Yelp
Superbloom: Bloom filter meets Transformer
We extend the idea of word pieces in natural language models to machine learning tasks on opaque ids.
Scaling Up Collaborative Filtering Data Sets through Randomized Fractal Expansions
Recommender system research suffers from a disconnect between the size of academic data sets and the scale of industrial production systems.
Scalable Realistic Recommendation Datasets through Fractal Expansions
A larger version features 655 billion ratings, 7 million items and 17 million users.
Efficient Training on Very Large Corpora via Gramian Estimation
We study the problem of learning similarity functions over very large corpora using neural network embedding models.
Machine Learning in High Energy Physics Community White Paper
In this document we discuss promising future research and development areas for machine learning in particle physics.
BIG-bench Machine Learning
Vocal Bursts Intensity Prediction
