Results 21 to 30 of about 24,500,069 (341)
Selective Amnesia: A Continual Learning Approach to Forgetting in Deep Generative Models [PDF]
Neural Information Processing Systems, 2023 The recent proliferation of large-scale text-to-image models has led to growing concerns that such models may be misused to generate harmful, misleading, and inappropriate content.
Alvin Heng, Harold Soh
semanticscholar +1 more source
Skilful precipitation nowcasting using deep generative models of radar [PDF]
Nature, 2021 Precipitation nowcasting, the high-resolution forecasting of precipitation up to two hours ahead, supports the real-world socioeconomic needs of many sectors reliant on weather-dependent decision-making1,2. State-of-the-art operational nowcasting methods
Suman V. Ravuri +19 more
semanticscholar +1 more source
Design Guidelines for Prompt Engineering Text-to-Image Generative Models [PDF]
International Conference on Human Factors in Computing Systems, 2021 Text-to-image generative models are a new and powerful way to generate visual artwork. However, the open-ended nature of text as interaction is double-edged; while users can input anything and have access to an infinite range of generations, they also ...
Vivian Liu, Lydia B. Chilton
semanticscholar +1 more source
H2O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models [PDF]
Neural Information Processing Systems, 2023 Large Language Models (LLMs), despite their recent impressive accomplishments, are notably cost-prohibitive to deploy, particularly for applications involving long-content generation, such as dialogue systems and story writing.
Zhenyu (Allen) Zhang +11 more
semanticscholar +1 more source
Generative Multimodal Models are In-Context Learners [PDF]
Computer Vision and Pattern Recognition, 2023 The human ability to easily solve multimodal tasks in context (i.e., with only a few demonstrations or simple instructions), is what current multimodal systems have largely struggled to imitate.
Quan Sun +10 more
semanticscholar +1 more source
LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion [PDF]
Annual Meeting of the Association for Computational Linguistics, 2023 We present LLM-Blender, an ensembling framework designed to attain consistently superior performance by leveraging the diverse strengths of multiple open-source large language models (LLMs). Our framework consists of two modules: PairRanker and GenFuser,
Dongfu Jiang, Xiang Ren, Bill Yuchen Lin
semanticscholar +1 more source
Generative Models of Brain Dynamics
Frontiers in Artificial Intelligence, 2022 This review article gives a high-level overview of the approaches across different scales of organization and levels of abstraction. The studies covered in this paper include fundamental models in computational neuroscience, nonlinear dynamics, data ...
Mahta Ramezanian-Panahi +12 more
doaj +1 more source
High-throughput Generative Inference of Large Language Models with a Single GPU [PDF]
International Conference on Machine Learning, 2023 The high computational and memory requirements of large language model (LLM) inference make it feasible only with multiple high-end accelerators. Motivated by the emerging demand for latency-insensitive tasks with batched processing, this paper initiates
Ying Sheng +13 more
semanticscholar +1 more source
Continual-Learning-of-Generative-Models-with-Limited-Data
, 2023 This repository contains the implementation of the paper ``Continual Learning of Generative Models with Limited Data: From Wasserstein-1 Barycenter to Adaptive Coalescence.''
Mehmet Dedeoglu (14644163)
core +1 more source
Disease variant prediction with deep generative models of evolutionary data
Nature, 2021 Quantifying the pathogenicity of protein variants in human disease-related genes would have a marked effect on clinical decisions, yet the overwhelming majority (over 98%) of these variants still have unknown consequences1–3.
J. Frazer +7 more
semanticscholar +1 more source