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Found 9 papers, 6 papers with code
Language models scale reliably with over-training and on downstream tasks
Second, we relate the perplexity of a language model to its downstream task performance by proposing a power law.
DataComp: In search of the next generation of multimodal datasets
Multimodal datasets are a critical component in recent breakthroughs such as Stable Diffusion and GPT-4, yet their design does not receive the same research attention as model architectures or training algorithms.
Multimodal C4: An Open, Billion-scale Corpus of Images Interleaved with Text
We release Multimodal C4, an augmentation of the popular text-only C4 corpus with images interleaved.
Patching open-vocabulary models by interpolating weights
We study model patching, where the goal is to improve accuracy on specific tasks without degrading accuracy on tasks where performance is already adequate.
Structure from Action: Learning Interactions for Articulated Object 3D Structure Discovery
We introduce Structure from Action (SfA), a framework to discover 3D part geometry and joint parameters of unseen articulated objects via a sequence of inferred interactions.
Continuous Scene Representations for Embodied AI
Our method captures feature relationships between objects, composes them into a graph structure on-the-fly, and situates an embodied agent within the representation.
CoWs on Pasture: Baselines and Benchmarks for Language-Driven Zero-Shot Object Navigation
To better evaluate L-ZSON, we introduce the Pasture benchmark, which considers finding uncommon objects, objects described by spatial and appearance attributes, and hidden objects described relative to visible objects.
Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time
The conventional recipe for maximizing model accuracy is to (1) train multiple models with various hyperparameters and (2) pick the individual model which performs best on a held-out validation set, discarding the remainder.
Ranked #1 on
Image Classification
on ImageNet V2
(using extra training data)
Act the Part: Learning Interaction Strategies for Articulated Object Part Discovery
People often use physical intuition when manipulating articulated objects, irrespective of object semantics.
