808 citations · 2.3k across the 26 of their papers we have counts for
29 papers
LatentCRF: Continuous CRF for Efficient Latent Diffusion
Kanchana Ranasinghe, Sadeep Jayasumana, Andreas Veit +5
Latent Diffusion Models (LDMs) produce high-quality, photo-realistic images, however, the latency incurred by multiple costly inference iterations can restrict their applicability.…
Efficient Document Ranking with Learnable Late Interactions
Ziwei Ji, Himanshu Jain, Andreas Veit +6
Cross-Encoder (CE) and Dual-Encoder (DE) models are two fundamental approaches for query-document relevance in information retrieval. To predict relevance, CE models use joint quer…
Rethinking FID: Towards a Better Evaluation Metric for Image Generation
Sadeep Jayasumana, Srikumar Ramalingam, Andreas Veit +3
As with many machine learning problems, the progress of image generation methods hinges on good evaluation metrics. One of the most popular is the Frechet Inception Distance (FID).…
MarkovGen: Structured Prediction for Efficient Text-to-Image Generation
Sadeep Jayasumana, Daniel Glasner, Srikumar Ramalingam +3
Modern text-to-image generation models produce high-quality images that are both photorealistic and faithful to the text prompts. However, this quality comes at significant computa…
Large Language Models with Controllable Working Memory
Daliang Li, Ankit Singh Rawat, Manzil Zaheer +5
Large language models (LLMs) have led to a series of breakthroughs in natural language processing (NLP), owing to their excellent understanding and generation abilities. Remarkably…
When does mixup promote local linearity in learned representations?
Arslan Chaudhry, Aditya Krishna Menon, Andreas Veit +3
Mixup is a regularization technique that artificially produces new samples using convex combinations of original training points. This simple technique has shown strong empirical p…