Machine Learning Research

TinyML shows promise for bringing deep learning to applications where electrical power is scarce, processing in the cloud is impractical, and/or data privacy is paramount.

Researchers struggle to build models that can generate a three-dimensional scene from a text prompt largely because they lack sufficient paired text-3D training examples. A new approach works without any 3D data whatsoever.

Asked to produce “a landscape by Thomas Kinkade,” a text-to-image generator fine-tuned on the pastoral painter’s work can mimic his style in seconds, often for pennies. A new technique aims to make it harder for algorithms to mimic an artist’s style.

If you want a model to answer questions correctly, then enriching the input with reference text retrieved from the web is a reliable way to increase the accuracy of its output. But the web isn’t necessarily the best source of reference text.

We know that image generators create wonderful original works, but do they sometimes replicate their training data? Recent work found that replication does occur.

When you’re looking for answers from a large language model, some prompts are better than others. So how can you come up with the best one? A new model automates the process.

How can you tell when you’re reading machine-generated text? Three recent papers proposed solutions: Watermarking, classification, and a statistical method.

Text from current language models can be useful as a rough draft, but that leaves the polishing to human writers. A language model learned how to generate and respond to editorial directions.

Both transformers and reinforcement learning models are notoriously data-hungry. They may be less so when they work together. Vincent Micheli and colleagues at the University of Geneva trained a transformer-based system to simulate Atari games using a small amount of gameplay.

Roboticists often train their machines in simulation, where the controller model can learn from millions of hours of experience. A new method trained robots in the real world in 20 minutes.

Recent multimodal models process both text and images as sequences of tokens, but they learn to represent these distinct data types using separate loss functions. Recent work unifies the loss function as well.

Get ready for the next wave of language-model mania. OpenAI introduced the latest in its GPT series of large language models to widespread excitement. The company showed statistics and examples designed to demonstrate...

On the heels of systems that generate video directly from text, new work uses text to adjust the imagery in existing videos. Researchers unveiled Gen-1...

Principal component analysis is a key machine learning technique for reducing the number of dimensions in a dataset, but new research shows that its output can be inconsistent and unreliable.

Autonomous robots trained to navigate in a simulation often struggle in the real world. New work helps bridge the gap in a counterintuitive way.