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Found 8 papers, 2 papers with code
Improving fine-grained understanding in image-text pre-training
We introduce SPARse Fine-grained Contrastive Alignment (SPARC), a simple method for pretraining more fine-grained multimodal representations from image-text pairs.
Autoregressive Diffusion Models
We introduce Autoregressive Diffusion Models (ARDMs), a model class encompassing and generalizing order-agnostic autoregressive models (Uria et al., 2014) and absorbing discrete diffusion (Austin et al., 2021), which we show are special cases of ARDMs under mild assumptions.
Ranked #8 on
Image Generation
on CIFAR-10
(bits/dimension metric)
VUT: Versatile UI Transformer for Multimodal Multi-Task User Interface Modeling
Our model consists of a multimodal Transformer encoder that jointly encodes UI images and structures, and performs UI object detection when the UI structures are absent in the input.
The Benchmark Lottery
The world of empirical machine learning (ML) strongly relies on benchmarks in order to determine the relative effectiveness of different algorithms and methods.
A Spectral Energy Distance for Parallel Speech Synthesis
Speech synthesis is an important practical generative modeling problem that has seen great progress over the last few years, with likelihood-based autoregressive neural models now outperforming traditional concatenative systems.
IDF++: Analyzing and Improving Integer Discrete Flows for Lossless Compression
Furthermore, we zoom in on the effect of gradient bias due to the straight-through estimator in integer discrete flows, and demonstrate that its influence is highly dependent on architecture choices and less prominent than previously thought.
On the relationship between Normalising Flows and Variational- and Denoising Autoencoders
Normalising Flows (NFs) are a class of likelihood-based generative models that have recently gained popularity.
BriarPatches: Pixel-Space Interventions for Inducing Demographic Parity
We introduce the BriarPatch, a pixel-space intervention that obscures sensitive attributes from representations encoded in pre-trained classifiers.
