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Found 11 papers, 3 papers with code
Federated Learning Over Images: Vertical Decompositions and Pre-Trained Backbones Are Difficult to Beat
We carefully evaluate a number of algorithms for learning in a federated environment, and test their utility for a variety of image classification tasks.
Auto-Differentiation of Relational Computations for Very Large Scale Machine Learning
The relational data model was designed to facilitate large-scale data management and analytics.
Coarse-Tuning Models of Code with Reinforcement Learning Feedback
Our experiments show that RLCF raises the odds that an LLM-generated program compiles, is executable, and produces the right output on tests, often allowing LLMs to match the performance of 2x-8x larger LLMs.
LOFT: Finding Lottery Tickets through Filter-wise Training
\textsc{LoFT} is a model-parallel pretraining algorithm that partitions convolutional layers by filters to train them independently in a distributed setting, resulting in reduced memory and communication costs during pretraining.
Efficient and Light-Weight Federated Learning via Asynchronous Distributed Dropout
Asynchronous learning protocols have regained attention lately, especially in the Federated Learning (FL) setup, where slower clients can severely impede the learning process.
Neural Program Generation Modulo Static Analysis
State-of-the-art neural models of source code tend to be evaluated on the generation of individual expressions and lines of code, and commonly fail on long-horizon tasks such as the generation of entire method bodies.
Few-shot Image Classification: Just Use a Library of Pre-trained Feature Extractors and a Simple Classifier
Recent papers have suggested that transfer learning can outperform sophisticated meta-learning methods for few-shot image classification.
Tensor Relational Algebra for Machine Learning System Design
This implementation abstraction provides little built-in support for ML systems to scale past a single machine, or for handling large models with matrices or tensors that do not easily fit into the RAM of an ASIC.
Meta-Meta Classification for One-Shot Learning
We present a new approach, called meta-meta classification, to learning in small-data settings.
Declarative Recursive Computation on an RDBMS, or, Why You Should Use a Database For Distributed Machine Learning
A number of popular systems, most notably Google's TensorFlow, have been implemented from the ground up to support machine learning tasks.
Neural Sketch Learning for Conditional Program Generation
We study the problem of generating source code in a strongly typed, Java-like programming language, given a label (for example a set of API calls or types) carrying a small amount of information about the code that is desired.
