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Found 9 papers, 0 papers with code
MM1: Methods, Analysis & Insights from Multimodal LLM Pre-training
Through careful and comprehensive ablations of the image encoder, the vision language connector, and various pre-training data choices, we identified several crucial design lessons.
Ranked #18 on
Visual Question Answering
on MM-Vet
Mobile V-MoEs: Scaling Down Vision Transformers via Sparse Mixture-of-Experts
We empirically show that our sparse Mobile Vision MoEs (V-MoEs) can achieve a better trade-off between performance and efficiency than the corresponding dense ViTs.
STAIR: Learning Sparse Text and Image Representation in Grounded Tokens
We extend the CLIP model and build a sparse text and image representation (STAIR), where the image and text are mapped to a sparse token space.
Masked Autoencoding Does Not Help Natural Language Supervision at Scale
Self supervision and natural language supervision have emerged as two exciting ways to train general purpose image encoders which excel at a variety of downstream tasks.
Unknowable Manipulators: Social Network Curator Algorithms
For a social networking service to acquire and retain users, it must find ways to keep them engaged.
Blitzkriging: Kronecker-structured Stochastic Gaussian Processes
We present Blitzkriging, a new approach to fast inference for Gaussian processes, applicable to regression, optimisation and classification.
Sampling for Inference in Probabilistic Models with Fast Bayesian Quadrature
We propose a novel sampling framework for inference in probabilistic models: an active learning approach that converges more quickly (in wall-clock time) than Markov chain Monte Carlo (MCMC) benchmarks.
Variational Inference for Gaussian Process Modulated Poisson Processes
We present the first fully variational Bayesian inference scheme for continuous Gaussian-process-modulated Poisson processes.
Efficient Bayesian Nonparametric Modelling of Structured Point Processes
This paper presents a Bayesian generative model for dependent Cox point processes, alongside an efficient inference scheme which scales as if the point processes were modelled independently.
