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pro vyhledávání: '"Komanduri, Aneesh"'
Diffusion probabilistic models (DPMs) have become the state-of-the-art in high-quality image generation. However, DPMs have an arbitrary noisy latent space with no interpretable or controllable semantics. Although there has been significant research
Externí odkaz:
http://arxiv.org/abs/2404.17735
Publikováno v:
Transactions on Machine Learning Research, 2024
Deep generative models have shown tremendous capability in data density estimation and data generation from finite samples. While these models have shown impressive performance by learning correlations among features in the data, some fundamental sho
Externí odkaz:
http://arxiv.org/abs/2310.11011
Learning disentangled causal representations is a challenging problem that has gained significant attention recently due to its implications for extracting meaningful information for downstream tasks. In this work, we define a new notion of causal di
Externí odkaz:
http://arxiv.org/abs/2306.01213
Neighborhood Random Walk Graph Sampling for Regularized Bayesian Graph Convolutional Neural Networks
Autor:
Komanduri, Aneesh, Zhan, Justin
In the modern age of social media and networks, graph representations of real-world phenomena have become an incredibly useful source to mine insights. Often, we are interested in understanding how entities in a graph are interconnected. The Graph Ne
Externí odkaz:
http://arxiv.org/abs/2112.07743
Question Answering (QA) is a task in natural language processing that has seen considerable growth after the advent of transformers. There has been a surge in QA datasets that have been proposed to challenge natural language processing models to impr
Externí odkaz:
http://arxiv.org/abs/2110.03142