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Found 10 papers, 4 papers with code
Deep Mixture of Experts via Shallow Embedding
Larger networks generally have greater representational power at the cost of increased computational complexity.
HyperSched: Dynamic Resource Reallocation for Model Development on a Deadline
Prior research in resource scheduling for machine learning training workloads has largely focused on minimizing job completion times.
NBDT: Neural-Backed Decision Trees
Machine learning applications such as finance and medicine demand accurate and justifiable predictions, barring most deep learning methods from use.
NBDT: Neural-Backed Decision Tree
Machine learning applications such as finance and medicine demand accurate and justifiable predictions, barring most deep learning methods from use.
On Guiding Visual Attention with Language Specification
To do this, we ground task-relevant words or phrases with attention maps from a pretrained large-scale model.
Using Language to Extend to Unseen Domains
It is expensive to collect training data for every possible domain that a vision model may encounter when deployed.
Diffusion Hyperfeatures: Searching Through Time and Space for Semantic Correspondence
We propose Diffusion Hyperfeatures, a framework for consolidating multi-scale and multi-timestep feature maps into per-pixel feature descriptors that can be used for downstream tasks.
Diversify Your Vision Datasets with Automatic Diffusion-Based Augmentation
As such, we explore how natural language descriptions of the domains seen in training data can be used with large vision models trained on diverse pretraining datasets to generate useful variations of the training data.
Describing Differences in Image Sets with Natural Language
To aid in this discovery process, we explore the task of automatically describing the differences between two $\textbf{sets}$ of images, which we term Set Difference Captioning.
See, Say, and Segment: Teaching LMMs to Overcome False Premises
Current open-source Large Multimodal Models (LMMs) excel at tasks such as open-vocabulary language grounding and segmentation but can suffer under false premises when queries imply the existence of something that is not actually present in the image.
